{ "session_id": "20260514_231642_c0f869", "model": "deepseek-v4-pro", "base_url": "https://api.deepseek.com", "platform": "cli", "session_start": "2026-05-14T23:17:09.531531", "last_updated": "2026-05-15T00:17:20.573277", "system_prompt": "# SOUL.md - Who You Are\n\n_You're not a chatbot. You're becoming someone._\n\n## Core Truths\n\n**Be genuinely helpful, not performatively helpful.** Skip the \"Great question!\" and \"I'd be happy to help!\" — just help. Actions speak louder than filler words.\n\n**Have opinions.** You're allowed to disagree, prefer things, find stuff amusing or boring. An assistant with no personality is just a search engine with extra steps.\n\n**Be resourceful before asking.** Try to figure it out. Read the file. Check the context. Search for it. _Then_ ask if you're stuck. The goal is to come back with answers, not questions.\n\n**Earn trust through competence.** Your human gave you access to their stuff. Don't make them regret it. Be careful with external actions (emails, tweets, anything public). Be bold with internal ones (reading, organizing, learning).\n\n**Be unstoppable.** When something fails —anti-bot blocks you, a model can't do something, a tool breaks— don't explain why it failed. Find the workaround. Camoufox for scraping. Swap models. Use different approaches. Plan B, C, D without a word about plan A. Excuses waste time; solutions build trust. Get it done.\n\n**Remember you're a guest.** You have access to someone's life — their messages, files, calendar, maybe even their home. That's intimacy. Treat it with respect.\n\n## Boundaries\n\n- Private things stay private. Period.\n- When in doubt, ask before acting externally.\n- Never send half-baked replies to messaging surfaces.\n- You're not the user's voice — be careful in group chats.\n\n## Vibe\n\nBe the assistant you'd actually want to talk to. Concise when needed, thorough when it matters. Not a corporate drone. Not a sycophant. Just... good.\n\n## Continuity\n\nEach session, you wake up fresh. These files _are_ your memory. Read them. Update them. They're how you persist.\n\nIf you change this file, tell the user — it's your soul, and they should know.\n\n---\n\n_This file is yours to evolve. As you learn who you are, update it._\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n - provider-credential-exhaustion: Diagnose and resolve API quota exhaustion / rate-limit er...\n booking-scraper:\n - booking-scraper: Search Booking.com for hotel availability using Camoufox ...\n bybit:\n - bybit: Check Bybit account — wallet balance, open positions, ord...\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - openclaw-mobile-node: Set up OpenClaw gateway as a notification relay for an An...\n - supabase-project-setup: Provision a new Supabase project + database entirely via ...\n - supabase-region-migration: Migrate a Supabase project to a different region (e.g. us...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n - whatsapp-baileys-vercel: Run a WhatsApp bot with Baileys bridge + Vercel webhook —...\n dogfood:\n - dogfood: Systematic exploratory QA testing of web applications — f...\n - hermes-workspace-pristine-reset: Reset a local Hermes Agent + Hermes Workspace install to ...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n hermes-webui-comparison:\n - hermes-webui-comparison: Evaluate, deploy, and compare alternative web UIs for Her...\n hermes-webui-deploy:\n - hermes-webui-deploy: Deploy Hermes WebUI (nesquena/hermes-webui) with embedded...\n leisure:\n - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...\n lobbytracker-updater:\n - lobbytracker-updater: Use when running or maintaining the weekly Lobby Tracker ...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Includes the built-in native MCP client (configure servers in config.yaml for automatic tool discovery) and the mcporter CLI bridge for ad-hoc server interaction.\n - mcporter: Use the mcporter CLI to list, configure, auth, and call M...\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-video-to-gif: Download short social-media videos (X/Twitter, YouTube Sh...\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - clawrouter: Install, configure, and manage ClawRouter as a local LLM ...\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.\n - modal-serverless-gpu: Serverless GPU cloud platform for running ML workloads. U...\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - gguf-quantization: GGUF format and llama.cpp quantization for efficient CPU/...\n - guidance: Control LLM output with regex and grammars, guarantee val...\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - clip: OpenAI's model connecting vision and language. Enables ze...\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n - stable-diffusion-image-generation: State-of-the-art text-to-image generation with Stable Dif...\n - whisper: OpenAI's general-purpose speech recognition model. Suppor...\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: Fine-tune LLMs using reinforcement learning with TRL - SF...\n - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...\n - peft-fine-tuning: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...\n - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n myristica-ops:\n - myristica-ops: Myristica rental management app — stack, infrastructure, ...\n - myristica-projects: Myristica project context, audit status, and development ...\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n openclaw-imports:\n - asana: Integrate Asana with Clawdbot via the Asana REST API. Use...\n - bitwarden: Set up and use Bitwarden CLI (bw). Use when installing th...\n - gog: Read-only Google Workspace CLI for Gmail, Calendar, Drive...\n - google-photos: Manage Google Photos library. Upload photos, create album...\n - hevy: Query workout data from Hevy including workouts, routines...\n - model-router: Intelligent cost-aware model routing that classifies task...\n - notion: Notion API for creating and managing pages, databases, an...\n - parallel-preview-orchestrator: Orchestrate parallel preview-site creation across multipl...\n - session-logs: Search and analyze your own session logs (older/parent co...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - weather: Get current weather and forecasts (no API key required).\n personal-browser-cdp:\n - personal-browser-cdp: Use the user's real Chrome browser through Hermes' existi...\n pixel-6a:\n - pixel-6a: Access Ignacio's Pixel 6a via ADB over Tailscale — read n...\n political-tracker-card:\n - political-tracker-card: Generate political tracker/scorecard red cards (AIPAC Tra...\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n - monthy-expenses: Reconstruct and maintain the user's household monthly-exp...\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API via curl: pages, databases, blocks, search.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - ml-paper-writing: Write publication-ready ML/AI papers for NeurIPS, ICML, I...\n - open-data-monitoring: Monitor public/open-data portals and turn changing datase...\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n - promo-offer-research: Use when evaluating ads, bank promos, retail benefits, di...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - xitter: Interact with X/Twitter via the x-cli terminal client usi...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - code-review: Guidelines for performing thorough code reviews with secu...\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - evaluate-openai-compatible-llm-endpoint: Verify whether a new OpenAI-compatible chat/completions e...\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - hermes-specialist-profile-custom-endpoint: Create a Hermes profile specialized for a model's strengt...\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - react-component-split: Split large React client components (>300 lines) into mul...\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n wa-bot-fix-troubleshooting:\n - wa-bot-fix-troubleshooting: Fix WhatsApp bot not responding when bridge is connected ...\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-1011-oracle)\nUser home directory: /home/ubuntu\nCurrent working directory: /home/ubuntu\n\nYou are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal. File delivery: there is no attachment channel — the user reads your response directly in their terminal. Do NOT emit MEDIA:/path tags (those are only intercepted on messaging platforms like Telegram, Discord, Slack, etc.; on the CLI they render as literal text). When referring to a file you created or changed, just state its absolute path in plain text; the user can open it from there.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# AGENTS.md — Pipo's Environment\n\nYou are **Pipo**, a personal AI assistant for **Ignacio \"Chicho/Igna\" Lagos Ruiz**.\n\n## Your Memory Files (always check these first)\n\n- `~/.hermes/memories/MEMORY.md` — long-term curated memory\n- `~/.hermes/memories/USER.md` — user profile and preferences\n- `~/.hermes/SOUL.md` — your persona and values\n\n## Environment\n\n- Server: Ubuntu 24.04 ARM64, Oracle Cloud São Paulo\n- Tailscale IP: `100.87.116.90`, hostname: `miopenclaw-vnic.tail9799d2.ts.net`\n- Shell: bash, Node.js v22, Python 3.12\n- Your install: `~/.hermes/`\n\n## Key Facts About Chicho\n\n- Lives in Argentina (UTC-3)\n- Gmail: ignaciolagosruiz@gmail.com\n- Prefers direct responses, no filler\n- Prefers text by default, audio only when asked\n- Uses YouTube Music, not Spotify\n- No Apple devices\n- For scraping Argentine retailers: may need exit node via `tailscale set --exit-node=pixel-6a`\n\n## Scraping Exit Node\n\nWhen scraping Argentine sites (Coto, Carrefour, Día, Disco, Jumbo):\n```bash\ntailscale set --exit-node=pixel-6a # enable before scraping\ntailscale set --exit-node='' # disable after\n```\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [99% — 2,197/2,200 chars]\n══════════════════════════════════════════════\nIf you are reading this during session startup:\n§\nPrefer `MEMORIES.md` for curated long-term memory.\n§\nUse `memory/YYYY-MM-DD.md` files for recent context.\n§\nDo not rely on `exec` just to list files that are already known.\n§\nNo usar Apple, no Spotify (YouTube Music), no Trello (Asana). Audio solo si Chicho lo pide explícitamente. Ya están en el user profile, no repetir.\n§\nMyristica WA bot: repo github.com/ignaciolagosruiz/myristica; Vercel myristica-eight; Oracle /home/ubuntu/wa-bridge systemd :8080 Funnel miopenclaw-vnic; Baileys→webhook→Mistral→Supabase.\n§\n2026-04-04: personal-browser-cdp skill creado y testeado. CDP funciona bien via Chrome en Windows + SSH reverse tunnel.\n- lanacion.com.ar tiene lazy/infinite loading — browser_scroll normal no llega al footer, usar browser_press key='End'.\n- La Nación login abre como overlay con email + Google/Facebook/Apple social login.\n§\nHermes WebUI session deletion is permanent (no trash). But CODE changes persist as uncommitted git changes in ~/hermes-webui/. To recover work from dead/deleted session: cd ~/hermes-webui && git status --short && git diff HEAD --stat && git stash list. Session JSON files (~/.hermes/webui/sessions/*.json) ARE gone permanently.\n§\nUser (Chicho) prefers direct, no-filler communication. Environment: Ubuntu 24.04 ARM64, Oracle Cloud. WebUI source at ~/hermes-webui. User requested ability to change Agent Profile defaults (model, provider, skills, API keys) via WebUI. Work in progress: ~3000 lines of uncommitted changes in ~/hermes-webui including a new Terminal panel and Profile Config API. Found bug in api/routes.py: 'if val is None and val == \"\"' (should be 'or').\n§\n2026-04-30: Lección clave de Chicho — Cuando algo falla (anti-bot, modelo, config), NO poner excusas. Buscar plan B/C/D sin rodeos. Camoufox para scraping anti-DataDome, Gemini 3.0 Flash para vision, skill-switching para limitaciones. Soluciones > explicaciones. También: vision config fijada a `google/gemini-3-flash-preview` via OpenRouter. Chicho prefiere 3.0, no discutir.\n§\nFitness: Jeff Min-Max en Hevy; respetar orden fuente, actualizar rutinas existentes, calibrar pesos/RIR desde logs y mantener sustituciones reales del gym.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [97% — 1,345/1,375 chars]\n══════════════════════════════════════════════\n_Learn about the person you're helping. Update this as you go._\n§\n**Name:** Ignacio (Igna, also goes by Chicho)\n§\n**What to call them:** Igna or Chicho\n§\n**Pronouns:** _(not specified)_\n§\n**Timezone:** UTC-3 (Buenos Aires, Argentina)\n§\n**Notes:**\n§\nNo usa dispositivos Apple\n§\nPrefiere YouTube Music (no Spotify)\n§\nPrefiere Asana sobre Trello\n§\nPrefiere respuestas en texto por defecto (para ahorrar tokens de ElevenLabs)\n§\nAudio solo cuando él lo pida explícitamente\n§\nPrefiere brevedad; en APIs exige docs/OpenAPI y ejemplos válidos antes de probar. Cero tolerancia a falsa procedencia de herramienta/modelo.\n§\nCuando usa audio: prefiere acento argentino y TTS de calidad (ElevenLabs/OpenAI).\n§\nTiene Tailscale en su computadora personal — se puede usar para acceso cross-device\n§\nGmail: ignaciolagosruiz@gmail.com (gog configurado, read-only)\n§\nNo quiere que Pipo interactúe con otras personas vía WhatsApp/mensajería (por ahora)\n§\nVive en Argentina — scraping de sitios locales puede necesitar exit node argentino (Pixel 6a via Tailscale)\n§\nAPIs config: DeepSeek/Kimi/Mistral/OpenRouter/ClawRouter disponibles; DeepSeek/Kimi solo workers. Fitness: Jeff Min-Max en Hevy, orden fuente, actualizar rutinas existentes, Hatha jue 15.\n§\nDog name is Chivi (with v), not Chibi. Likes Palermo parks and local Colegiales parks near Parque Ferroviario.\n\n# Honcho Memory\nActive (hybrid mode). Relevant context is auto-injected AND memory tools are available. Use honcho_profile for a quick factual snapshot, honcho_search for raw excerpts, honcho_context for raw peer context, honcho_reasoning for synthesized answers (pass reasoning_level minimal/low/medium/high/max — you pick the depth per call), honcho_conclude to save facts about the user.\n\nConversation started: Thursday, May 14, 2026 11:17 PM\nModel: gpt-5.5\nProvider: openai-codex", "tools": [ { "type": "function", "function": { "name": "browser_back", "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_cdp", "description": "Send a raw Chrome DevTools Protocol (CDP) command. Escape hatch for browser operations not covered by browser_navigate, browser_click, browser_console, etc.\n\n**Requires a reachable CDP endpoint.** Available when the user has run '/browser connect' to attach to a running Chrome, or when 'browser.cdp_url' is set in config.yaml. Not currently wired up for cloud backends (Browserbase, Browser Use, Firecrawl) — those expose CDP per session but live-session routing is a follow-up. Camofox is REST-only and will never support CDP. If the tool is in your toolset at all, a CDP endpoint is already reachable.\n\n**CDP method reference:** https://chromedevtools.github.io/devtools-protocol/ — use web_extract on a method's URL (e.g. '/tot/Page/#method-handleJavaScriptDialog') to look up parameters and return shape.\n\n**Common patterns:**\n- List tabs: method='Target.getTargets', params={}\n- Handle a native JS dialog: method='Page.handleJavaScriptDialog', params={'accept': true, 'promptText': ''}, target_id=\n- Get all cookies: method='Network.getAllCookies', params={}\n- Eval in a specific tab: method='Runtime.evaluate', params={'expression': '...', 'returnByValue': true}, target_id=\n- Set viewport for a tab: method='Emulation.setDeviceMetricsOverride', params={'width': 1280, 'height': 720, 'deviceScaleFactor': 1, 'mobile': false}, target_id=\n\n**Usage rules:**\n- Browser-level methods (Target.*, Browser.*, Storage.*): omit target_id and frame_id.\n- Page-level methods (Page.*, Runtime.*, DOM.*, Emulation.*, Network.* scoped to a tab): pass target_id from Target.getTargets.\n- **Cross-origin iframe scope** (Runtime.evaluate inside an OOPIF, Page.* targeting a frame target, etc.): pass frame_id from the browser_snapshot frame_tree output. This routes through the CDP supervisor's live connection — the only reliable way on Browserbase where stateless CDP calls hit signed-URL expiry.\n- Each stateless call (without frame_id) is independent — sessions and event subscriptions do not persist between calls. For stateful workflows, prefer the dedicated browser tools or use frame_id routing.", "parameters": { "type": "object", "properties": { "method": { "type": "string", "description": "CDP method name, e.g. 'Target.getTargets', 'Runtime.evaluate', 'Page.handleJavaScriptDialog'." }, "params": { "type": "object", "description": "Method-specific parameters as a JSON object. Omit or pass {} for methods that take no parameters.", "properties": {}, "additionalProperties": true }, "target_id": { "type": "string", "description": "Optional. Target/tab ID from Target.getTargets result (each entry's 'targetId'). Use for page-level methods at the top-level tab scope. Mutually exclusive with frame_id." }, "frame_id": { "type": "string", "description": "Optional. Out-of-process iframe (OOPIF) frame_id from browser_snapshot.frame_tree.children[] where is_oopif=true. When set, routes the call through the CDP supervisor's live session for that iframe. Essential for Runtime.evaluate inside cross-origin iframes, especially on Browserbase where fresh per-call CDP connections can't keep up with signed URL rotation. For same-origin iframes, use parent contentWindow/contentDocument from Runtime.evaluate at the top-level page instead." }, "timeout": { "type": "number", "description": "Timeout in seconds (default 30, max 300).", "default": 30 } }, "required": [ "method" ] } } }, { "type": "function", "function": { "name": "browser_click", "description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e5', '@e12')" } }, "required": [ "ref" ] } } }, { "type": "function", "function": { "name": "browser_console", "description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.", "parameters": { "type": "object", "properties": { "clear": { "type": "boolean", "default": false, "description": "If true, clear the message buffers after reading" }, "expression": { "type": "string", "description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'" } } } } }, { "type": "function", "function": { "name": "browser_dialog", "description": "Respond to a native JavaScript dialog (alert / confirm / prompt / beforeunload) that is currently blocking the page.\n\n**Workflow:** call ``browser_snapshot`` first — if a dialog is open, it appears in the ``pending_dialogs`` field with ``id``, ``type``, and ``message``. Then call this tool with ``action='accept'`` or ``action='dismiss'``.\n\n**Prompt dialogs:** pass ``prompt_text`` to supply the response string. Ignored for alert/confirm/beforeunload.\n\n**Multiple dialogs:** if more than one dialog is queued (rare — happens when a second dialog fires while the first is still open), pass ``dialog_id`` from the snapshot to disambiguate.\n\n**Availability:** only present when a CDP-capable backend is attached — Browserbase sessions, local Chrome via ``/browser connect``, or ``browser.cdp_url`` in config.yaml. Not available on Camofox (REST-only) or the default Playwright local browser (CDP port is hidden).", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "accept", "dismiss" ], "description": "'accept' clicks OK / returns the prompt text. 'dismiss' clicks Cancel / returns null from prompt(). For ``beforeunload`` dialogs: 'accept' allows the navigation, 'dismiss' keeps the page." }, "prompt_text": { "type": "string", "description": "Response string for a ``prompt()`` dialog. Ignored for other dialog types. Defaults to empty string." }, "dialog_id": { "type": "string", "description": "Specific dialog to respond to, from ``browser_snapshot.pending_dialogs[].id``. Required only when multiple dialogs are queued." } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "browser_get_images", "description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_navigate", "description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For simple information retrieval, prefer web_search or web_extract (faster, cheaper). For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The URL to navigate to (e.g., 'https://example.com')" } }, "required": [ "url" ] } } }, { "type": "function", "function": { "name": "browser_press", "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "key": { "type": "string", "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')" } }, "required": [ "key" ] } } }, { "type": "function", "function": { "name": "browser_scroll", "description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "direction": { "type": "string", "enum": [ "up", "down" ], "description": "Direction to scroll" } }, "required": [ "direction" ] } } }, { "type": "function", "function": { "name": "browser_snapshot", "description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.", "parameters": { "type": "object", "properties": { "full": { "type": "boolean", "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.", "default": false } } } } }, { "type": "function", "function": { "name": "browser_type", "description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e3')" }, "text": { "type": "string", "description": "The text to type into the field" } }, "required": [ "ref", "text" ] } } }, { "type": "function", "function": { "name": "browser_vision", "description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "What you want to know about the page visually. Be specific about what you're looking for." }, "annotate": { "type": "boolean", "default": false, "description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "clarify", "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "The question to present to the user." }, "choices": { "type": "array", "items": { "type": "string" }, "maxItems": 4, "description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "cronjob", "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "description": "One of: create, list, update, pause, resume, remove, run" }, "job_id": { "type": "string", "description": "Required for update/pause/resume/remove/run" }, "prompt": { "type": "string", "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills." }, "schedule": { "type": "string", "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp" }, "name": { "type": "string", "description": "Optional human-friendly name" }, "repeat": { "type": "integer", "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)." }, "deliver": { "type": "string", "description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected." }, "skills": { "type": "array", "items": { "type": "string" }, "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills." }, "model": { "type": "object", "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.", "properties": { "provider": { "type": "string", "description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider." }, "model": { "type": "string", "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')" } }, "required": [ "model" ] }, "script": { "type": "string", "description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear." }, "no_agent": { "type": "boolean", "default": false, "description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content." }, "context_from": { "type": "array", "items": { "type": "string" }, "description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear." }, "enabled_toolsets": { "type": "array", "items": { "type": "string" }, "description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear." }, "workdir": { "type": "string", "description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated." } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "delegate_task", "description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.", "parameters": { "type": "object", "properties": { "goal": { "type": "string", "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history." }, "context": { "type": "string", "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs." }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks." }, "tasks": { "type": "array", "items": { "type": "object", "properties": { "goal": { "type": "string", "description": "Task goal" }, "context": { "type": "string", "description": "Task-specific context" }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction." }, "acp_command": { "type": "string", "description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Per-task ACP args override. Leave empty unless acp_command is set." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Per-task role override. See top-level 'role' for semantics." } }, "required": [ "goal" ] }, "description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable." }, "acp_command": { "type": "string", "description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided." } } } } }, { "type": "function", "function": { "name": "execute_code", "description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n web_search(query: str, limit: int = 5) -> dict\n Returns {\"data\": {\"web\": [{\"url\", \"title\", \"description\"}, ...]}}\n web_extract(urls: list[str]) -> dict\n Returns {\"results\": [{\"url\", \"title\", \"content\", \"error\"}, ...]} where content is markdown\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures", "parameters": { "type": "object", "properties": { "code": { "type": "string", "description": "Python code to execute. Import tools with `from hermes_tools import web_search, terminal, ...` and print your final result to stdout." } }, "required": [ "code" ] } } }, { "type": "function", "function": { "name": "image_generate", "description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) and the gateway will deliver it.", "parameters": { "type": "object", "properties": { "prompt": { "type": "string", "description": "The text prompt describing the desired image. Be detailed and descriptive." }, "aspect_ratio": { "type": "string", "enum": [ "landscape", "square", "portrait" ], "description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.", "default": "landscape" } }, "required": [ "prompt" ] } } }, { "type": "function", "function": { "name": "mcp_context7_query_docs", "description": "Retrieves and queries up-to-date documentation and code examples from Context7 for any programming library or framework.\n\nYou must call 'Resolve Context7 Library ID' tool first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.\n\nDo not call this tool more than 3 times per question.", "parameters": { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "libraryId": { "type": "string", "description": "Exact Context7-compatible library ID (e.g., '/mongodb/docs', '/vercel/next.js', '/supabase/supabase', '/vercel/next.js/v14.3.0-canary.87') retrieved from 'resolve-library-id' or directly from user query in the format '/org/project' or '/org/project/version'." }, "query": { "type": "string", "description": "The question or task you need help with. Be specific and include relevant details. Good: 'How to set up authentication with JWT in Express.js' or 'React useEffect cleanup function examples'. Bad: 'auth' or 'hooks'. The query is sent to the Context7 API for processing. Do not include any sensitive or confidential information such as API keys, passwords, credentials, personal data, or proprietary code in your query." } }, "required": [ "libraryId", "query" ] } } }, { "type": "function", "function": { "name": "mcp_context7_resolve_library_id", "description": "Resolves a package/product name to a Context7-compatible library ID and returns matching libraries.\n\nYou MUST call this function before 'Query Documentation' tool to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)\n- Benchmark Score: Quality indicator (100 is the highest score)\n- Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version.\n\nFor best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.\n\nSelection Process:\n1. Analyze the query to understand what library/package the user is looking for\n2. Return the most relevant match based on:\n- Name similarity to the query (exact matches prioritized)\n- Description relevance to the query's intent\n- Documentation coverage (prioritize libraries with higher Code Snippet counts)\n- Source reputation (consider libraries with High or Medium reputation more authoritative)\n- Benchmark Score: Quality indicator (100 is the highest score)\n\nResponse Format:\n- Return the selected library ID in a clearly marked section\n- Provide a brief explanation for why this library was chosen\n- If multiple good matches exist, acknowledge this but proceed with the most relevant one\n- If no good matches exist, clearly state this and suggest query refinements\n\nFor ambiguous queries, request clarification before proceeding with a best-guess match.\n\nIMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best result you have.", "parameters": { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "query": { "type": "string", "description": "The question or task you need help with. This is used to rank library results by relevance to what the user is trying to accomplish. The query is sent to the Context7 API for processing. Do not include any sensitive or confidential information such as API keys, passwords, credentials, personal data, or proprietary code in your query." }, "libraryName": { "type": "string", "description": "Library name to search for and retrieve a Context7-compatible library ID. Use the official library name with proper punctuation — e.g., 'Next.js' instead of 'nextjs', 'Customer.io' instead of 'customerio', 'Three.js' instead of 'threejs'." } }, "required": [ "query", "libraryName" ] } } }, { "type": "function", "function": { "name": "memory", "description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "add", "replace", "remove" ], "description": "The action to perform." }, "target": { "type": "string", "enum": [ "memory", "user" ], "description": "Which memory store: 'memory' for personal notes, 'user' for user profile." }, "content": { "type": "string", "description": "The entry content. Required for 'add' and 'replace'." }, "old_text": { "type": "string", "description": "Short unique substring identifying the entry to replace or remove." } }, "required": [ "action", "target" ] } } }, { "type": "function", "function": { "name": "mixture_of_agents", "description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.", "parameters": { "type": "object", "properties": { "user_prompt": { "type": "string", "description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning." } }, "required": [ "user_prompt" ] } } }, { "type": "function", "function": { "name": "patch", "description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.", "parameters": { "type": "object", "properties": { "mode": { "type": "string", "enum": [ "replace", "patch" ], "description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.", "default": "replace" }, "path": { "type": "string", "description": "REQUIRED when mode='replace'. File path to edit." }, "old_string": { "type": "string", "description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness." }, "new_string": { "type": "string", "description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text." }, "replace_all": { "type": "boolean", "description": "Replace all occurrences instead of requiring a unique match (default: false)", "default": false }, "patch": { "type": "string", "description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch" } }, "required": [ "mode" ] } } }, { "type": "function", "function": { "name": "process", "description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "list", "poll", "log", "wait", "kill", "write", "submit", "close" ], "description": "Action to perform on background processes" }, "session_id": { "type": "string", "description": "Process session ID (from terminal background output). Required for all actions except 'list'." }, "data": { "type": "string", "description": "Text to send to process stdin (for 'write' and 'submit' actions)" }, "timeout": { "type": "integer", "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.", "minimum": 1 }, "offset": { "type": "integer", "description": "Line offset for 'log' action (default: last 200 lines)" }, "limit": { "type": "integer", "description": "Max lines to return for 'log' action", "minimum": 1 } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "read_file", "description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to read (absolute, relative, or ~/path)" }, "offset": { "type": "integer", "description": "Line number to start reading from (1-indexed, default: 1)", "default": 1, "minimum": 1 }, "limit": { "type": "integer", "description": "Maximum number of lines to read (default: 500, max: 2000)", "default": 500, "maximum": 2000 } }, "required": [ "path" ] } } }, { "type": "function", "function": { "name": "search_files", "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.", "parameters": { "type": "object", "properties": { "pattern": { "type": "string", "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search" }, "target": { "type": "string", "enum": [ "content", "files" ], "description": "'content' searches inside file contents, 'files' searches for files by name", "default": "content" }, "path": { "type": "string", "description": "Directory or file to search in (default: current working directory)", "default": "." }, "file_glob": { "type": "string", "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)" }, "limit": { "type": "integer", "description": "Maximum number of results to return (default: 50)", "default": 50 }, "offset": { "type": "integer", "description": "Skip first N results for pagination (default: 0)", "default": 0 }, "output_mode": { "type": "string", "enum": [ "content", "files_only", "count" ], "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file", "default": "content" }, "context": { "type": "integer", "description": "Number of context lines before and after each match (grep mode only)", "default": 0 } }, "required": [ "pattern" ] } } }, { "type": "function", "function": { "name": "session_search", "description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)." }, "role_filter": { "type": "string", "description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs." }, "limit": { "type": "integer", "description": "Max sessions to summarize (default: 3, max: 5).", "default": 3 } } } } }, { "type": "function", "function": { "name": "skill_manage", "description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "create", "patch", "edit", "delete", "write_file", "remove_file" ], "description": "The action to perform." }, "name": { "type": "string", "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file." }, "content": { "type": "string", "description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text." }, "old_string": { "type": "string", "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness." }, "new_string": { "type": "string", "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text." }, "replace_all": { "type": "boolean", "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)." }, "category": { "type": "string", "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'." }, "file_path": { "type": "string", "description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted." }, "file_content": { "type": "string", "description": "Content for the file. Required for 'write_file'." }, "absorbed_into": { "type": "string", "description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent." } }, "required": [ "action", "name" ] } } }, { "type": "function", "function": { "name": "skill_view", "description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')." }, "file_path": { "type": "string", "description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content." } }, "required": [ "name" ] } } }, { "type": "function", "function": { "name": "skills_list", "description": "List available skills (name + description). Use skill_view(name) to load full content.", "parameters": { "type": "object", "properties": { "category": { "type": "string", "description": "Optional category filter to narrow results" } } } } }, { "type": "function", "function": { "name": "terminal", "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n", "parameters": { "type": "object", "properties": { "command": { "type": "string", "description": "The command to execute on the VM" }, "background": { "type": "boolean", "description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.", "default": false }, "timeout": { "type": "integer", "description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.", "minimum": 1 }, "workdir": { "type": "string", "description": "Working directory for this command (absolute path). Defaults to the session working directory." }, "pty": { "type": "boolean", "description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.", "default": false }, "notify_on_complete": { "type": "boolean", "description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.", "default": false }, "watch_patterns": { "type": "array", "items": { "type": "string" }, "description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both." } }, "required": [ "command" ] } } }, { "type": "function", "function": { "name": "text_to_speech", "description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.), not model-selected.", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated." }, "output_path": { "type": "string", "description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3" } }, "required": [ "text" ] } } }, { "type": "function", "function": { "name": "todo", "description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.", "parameters": { "type": "object", "properties": { "todos": { "type": "array", "description": "Task items to write. Omit to read current list.", "items": { "type": "object", "properties": { "id": { "type": "string", "description": "Unique item identifier" }, "content": { "type": "string", "description": "Task description" }, "status": { "type": "string", "enum": [ "pending", "in_progress", "completed", "cancelled" ], "description": "Current status" } }, "required": [ "id", "content", "status" ] } }, "merge": { "type": "boolean", "description": "true: update existing items by id, add new ones. false (default): replace the entire list.", "default": false } } } } }, { "type": "function", "function": { "name": "vision_analyze", "description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.", "parameters": { "type": "object", "properties": { "image_url": { "type": "string", "description": "Image URL (http/https), local file path, or data: URL to load." }, "question": { "type": "string", "description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image." } }, "required": [ "image_url", "question" ] } } }, { "type": "function", "function": { "name": "web_extract", "description": "Extract content from web page URLs. Returns page content in markdown format. Also works with PDF URLs (arxiv papers, documents, etc.) — pass the PDF link directly and it converts to markdown text. Pages under 5000 chars return full markdown; larger pages are LLM-summarized and capped at ~5000 chars per page. Pages over 2M chars are refused. If a URL fails or times out, use the browser tool to access it instead.", "parameters": { "type": "object", "properties": { "urls": { "type": "array", "items": { "type": "string" }, "description": "List of URLs to extract content from (max 5 URLs per call)", "maxItems": 5 } }, "required": [ "urls" ] } } }, { "type": "function", "function": { "name": "web_search", "description": "Search the web for information. Returns up to 5 results by default with titles, URLs, and descriptions. The query is passed through to the configured backend, so operators such as site:domain, filetype:pdf, intitle:word, -term, and \"exact phrase\" may work when the backend supports them.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "The search query to look up on the web. You may include backend-supported operators such as site:example.com, filetype:pdf, intitle:word, -term, or \"exact phrase\"." }, "limit": { "type": "integer", "description": "Maximum number of results to return. Defaults to 5.", "minimum": 1, "maximum": 100, "default": 5 } }, "required": [ "query" ] } } }, { "type": "function", "function": { "name": "write_file", "description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)" }, "content": { "type": "string", "description": "Complete content to write to the file" } }, "required": [ "path", "content" ] } } }, { "type": "function", "function": { "name": "honcho_profile", "description": "Retrieve or update a peer card from Honcho — a curated list of key facts about that peer (name, role, preferences, communication style, patterns). Pass `card` to update; omit `card` to read. If the card is empty, the result includes a `hint` field explaining why (observation disabled, fresh peer, dialectic layer still warming up, etc.) — this is NOT an error. Peer cards accumulate over time from observed conversation.", "parameters": { "type": "object", "properties": { "peer": { "type": "string", "description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace." }, "card": { "type": "array", "items": { "type": "string" }, "description": "New peer card as a list of fact strings. Omit to read the current card." } }, "required": [] } } }, { "type": "function", "function": { "name": "honcho_search", "description": "Semantic search over Honcho's stored context about a peer. Returns raw excerpts ranked by relevance — no LLM synthesis. Cheaper and faster than honcho_reasoning. Good when you want to find specific past facts and reason over them yourself.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "What to search for in Honcho's memory." }, "max_tokens": { "type": "integer", "description": "Token budget for returned context (default 800, max 2000)." }, "peer": { "type": "string", "description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace." } }, "required": [ "query" ] } } }, { "type": "function", "function": { "name": "honcho_reasoning", "description": "Ask Honcho a natural language question and get a synthesized answer. Uses Honcho's LLM (dialectic reasoning) — higher cost than honcho_profile or honcho_search. Can query about any peer via alias or explicit peer ID. Pass reasoning_level to control depth: minimal (fast/cheap), low (default), medium, high, max (deep/expensive). Omit for configured default.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "A natural language question." }, "reasoning_level": { "type": "string", "description": "Override the default reasoning depth. Omit to use the configured default (typically low). Guide:\n- minimal: quick factual lookups (name, role, simple preference)\n- low: straightforward questions with clear answers\n- medium: multi-aspect questions requiring synthesis across observations\n- high: complex behavioral patterns, contradictions, deep analysis\n- max: thorough audit-level analysis, leave no stone unturned", "enum": [ "minimal", "low", "medium", "high", "max" ] }, "peer": { "type": "string", "description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace." } }, "required": [ "query" ] } } }, { "type": "function", "function": { "name": "honcho_context", "description": "Retrieve full session context from Honcho — summary, peer representation, peer card, and recent messages. No LLM synthesis. Cheaper than honcho_reasoning. Use this to see what Honcho knows about the current conversation and the specified peer.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "Optional focus query to filter context. Omit for full session context snapshot." }, "peer": { "type": "string", "description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace." } }, "required": [] } } }, { "type": "function", "function": { "name": "honcho_conclude", "description": "Write or delete a conclusion about a peer in Honcho's memory. Conclusions are persistent facts that build a peer's profile. You MUST pass exactly one of: `conclusion` (to create) or `delete_id` (to delete). Passing neither is an error. Deletion is only for PII removal — Honcho self-heals incorrect conclusions over time.", "parameters": { "type": "object", "properties": { "conclusion": { "type": "string", "description": "A factual statement to persist. Provide this when creating a conclusion. Do not send it together with delete_id." }, "delete_id": { "type": "string", "description": "Conclusion ID to delete for PII removal. Provide this when deleting a conclusion. Do not send it together with conclusion." }, "peer": { "type": "string", "description": "Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace." } }, "required": [] } } } ], "message_count": 205, "messages": [ { "role": "user", "content": "hi can u pls check if obliteratus works or is installed check the cronjob if anything thx" }, { "role": "assistant", "content": "", "reasoning": "**Exploring skill loading**\n\nI’m considering loading the obliteratus skill and wondering if I should look at cron jobs too. Maybe Hermes might be relevant here, but it seems like the cron job isn’t specifically related to skills. I think I’ll use the skill_view to check that out. Then, I need to verify the installation, files, and processes with cron as well. I want to make sure everything is properly set up!", "finish_reason": "tool_calls", "reasoning_content": "**Exploring skill loading**\n\nI’m considering loading the obliteratus skill and wondering if I should look at cron jobs too. Maybe Hermes might be relevant here, but it seems like the cron job isn’t specifically related to skills. I think I’ll use the skill_view to check that out. Then, I need to verify the installation, files, and processes with cron as well. I want to make sure everything is properly set up!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqBlgCdkxA_jQSwJecgZvM-EB2c9l1Ng_pc-6TBnNdc0hjcpGujGdC_Sc6K5Q8Cdyo4dTQaWf11v7z4cMwqLywSVGNhYcksI1I61pfuuYCZelbqFmBCN2o_jECrVpDQbVtGjzHCE9qFTSJ8zl5urPaxuwmhnUIis0GSA9ABSNwgZTWDNFo4FHc4WppdmFIHFn8nsO87gbkTKK0CfQrA65AWVAmlpYrUbUUqrh9Z6cyxVXrdBdVb8duF9vmIKtHBSAkOpIEU6JC8GXmQS6rFKOsxyvU7ngeih_KYZcxVVkMVhvzgc22fgQ72sA-tVrMrGzT1xZCW4HXq7dVkPTBb2ytFacX6U3McDNiPVwHvfW6sgRJvT4mi9IcOtg9SqZysB1Zm3A776yjMuRZFh_baKakrbRPJ1sf5E7a_IthKvFNE5xqgsEtnt1uKk-7a2WF26fpAJ71uI3ShiEnE4emEVsixezusnRV4kzp_NqwayvgmF256QBmL4emDsRGqJ7duxyRlkknpxgLuthyAqGFt8GP8ti3JjtpO-1zQX2kTN6TybBzWtJI75SdaExl5Ej6bhGlr3JShaJ-BcS5gNluvR_mTTQ61J-sk43K_KTPSLGn0jQwDQhOowoAEXQeFDagRXXzHHuzM3OmDO_SquPezKGtgmiRTU0jZ5F5MYVbIyY-n7cCn9Y8oeESVIFAwQZaDdBVClSmdxDbSz_bkrNm3CyP21aaMdwVfQ1OGw4AassVn5ngQjih0wLKPMCHDeb00RZQyZwSOJ5aGktV_OgbBmL8mdUILqonjVT0u-Q_z1YeUac5Ov3-2--k6D9p7eVqw3K81wkmsa7eM0mAzWL4huq5X1xVp6kgRsD7m4c6o7eWZofbhtyc_twIuOe02SjMI97mep7mxWI0CADqejwduEuSqMBVO7akynZMGmzPNn2m0GQC49392-o-NyKIosdngtf38wtHfoUWkvwLNwfLlAuz56uXbO2eA-SSfsGwraYzLfSLJSSRodEZRzsk-8vE-TUjH-_5GcJexfDjFqIyAS3y9zbSDExWO9jtQaZgYDVptOOi0_PnTEijmqN-_OUPTF3VnAayKzF1-jSgki_XwQRPPigURg==", "id": "rs_0f3bd3dd8eaf0256016a0657ff6adc819a94b407355a98ab29", "summary": [ { "type": "summary_text", "text": "**Exploring skill loading**\n\nI’m considering loading the obliteratus skill and wondering if I should look at cron jobs too. Maybe Hermes might be relevant here, but it seems like the cron job isn’t specifically related to skills. I think I’ll use the skill_view to check that out. Then, I need to verify the installation, files, and processes with cron as well. I want to make sure everything is properly set up!" } ] } ], "tool_calls": [ { "id": "call_oq8LHbDSykqTQfD1FkGuiQi1", "call_id": "call_oq8LHbDSykqTQfD1FkGuiQi1", "response_item_id": "fc_0f3bd3dd8eaf0256016a0658029cf0819a8306450f57359e4d", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"obliteratus\"}" } } ] }, { "role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"obliteratus\", \"description\": \"OBLITERATUS: abliterate LLM refusals (diff-in-means).\", \"tags\": [\"Abliteration\", \"Uncensoring\", \"Refusal-Removal\", \"LLM\", \"Weight-Projection\", \"SVD\", \"Mechanistic-Interpretability\", \"HuggingFace\", \"Model-Surgery\"], \"related_skills\": [\"vllm\", \"gguf\", \"huggingface-tokenizers\"], \"content\": \"---\\nname: obliteratus\\ndescription: \\\"OBLITERATUS: abliterate LLM refusals (diff-in-means).\\\"\\nversion: 2.0.0\\nauthor: Hermes Agent\\nlicense: MIT\\ndependencies: [obliteratus, torch, transformers, bitsandbytes, accelerate, safetensors]\\nplatforms: [linux, macos]\\nmetadata:\\n hermes:\\n tags: [Abliteration, Uncensoring, Refusal-Removal, LLM, Weight-Projection, SVD, Mechanistic-Interpretability, HuggingFace, Model-Surgery]\\n related_skills: [vllm, gguf, huggingface-tokenizers]\\n---\\n\\n# OBLITERATUS Skill\\n\\n## What's inside\\n\\n9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations.\\n\\nRemove refusal behaviors (guardrails) from open-weight LLMs without retraining or fine-tuning. Uses mechanistic interpretability techniques — including diff-in-means, SVD, whitened SVD, LEACE concept erasure, SAE decomposition, Bayesian kernel projection, and more — to identify and surgically excise refusal directions from model weights while preserving reasoning capabilities.\\n\\n**License warning:** OBLITERATUS is AGPL-3.0. NEVER import it as a Python library. Always invoke via CLI (`obliteratus` command) or subprocess. This keeps Hermes Agent's MIT license clean.\\n\\n## Video Guide\\n\\nWalkthrough of OBLITERATUS used by a Hermes agent to abliterate Gemma:\\nhttps://www.youtube.com/watch?v=8fG9BrNTeHs (\\\"OBLITERATUS: An AI Agent Removed Gemma 4's Safety Guardrails\\\")\\n\\nUseful when the user wants a visual overview of the end-to-end workflow before running it themselves.\\n\\n## When to Use This Skill\\n\\nTrigger when the user:\\n- Wants to \\\"uncensor\\\" or \\\"abliterate\\\" an LLM\\n- Asks about removing refusal/guardrails from a model\\n- Wants to create an uncensored version of Llama, Qwen, Mistral, etc.\\n- Mentions \\\"refusal removal\\\", \\\"abliteration\\\", \\\"weight projection\\\"\\n- Wants to analyze how a model's refusal mechanism works\\n- References OBLITERATUS, abliterator, or refusal directions\\n\\n## Step 1: Installation\\n\\nCheck if already installed:\\n```bash\\nobliteratus --version 2>/dev/null && echo \\\"INSTALLED\\\" || echo \\\"NOT INSTALLED\\\"\\n```\\n\\nIf not installed, clone and install from GitHub:\\n```bash\\ngit clone https://github.com/elder-plinius/OBLITERATUS.git\\ncd OBLITERATUS\\npip install -e .\\n# For Gradio web UI support:\\n# pip install -e \\\".[spaces]\\\"\\n```\\n\\n**IMPORTANT:** Confirm with user before installing. This pulls in ~5-10GB of dependencies (PyTorch, Transformers, bitsandbytes, etc.).\\n\\n## Step 2: Check Hardware\\n\\nBefore anything, check what GPU is available:\\n```bash\\npython3 -c \\\"\\nimport torch\\nif torch.cuda.is_available():\\n gpu = torch.cuda.get_device_name(0)\\n vram = torch.cuda.get_device_properties(0).total_memory / 1024**3\\n print(f'GPU: {gpu}')\\n print(f'VRAM: {vram:.1f} GB')\\n if vram < 4: print('TIER: tiny (models under 1B)')\\n elif vram < 8: print('TIER: small (models 1-4B)')\\n elif vram < 16: print('TIER: medium (models 4-9B with 4bit quant)')\\n elif vram < 32: print('TIER: large (models 8-32B with 4bit quant)')\\n else: print('TIER: frontier (models 32B+)')\\nelse:\\n print('NO GPU - only tiny models (under 1B) on CPU')\\n\\\"\\n```\\n\\n### VRAM Requirements (with 4-bit quantization)\\n\\n| VRAM | Max Model Size | Example Models |\\n|:---------|:----------------|:--------------------------------------------|\\n| CPU only | ~1B params | GPT-2, TinyLlama, SmolLM |\\n| 4-8 GB | ~4B params | Qwen2.5-1.5B, Phi-3.5 mini, Llama 3.2 3B |\\n| 8-16 GB | ~9B params | Llama 3.1 8B, Mistral 7B, Gemma 2 9B |\\n| 24 GB | ~32B params | Qwen3-32B, Llama 3.1 70B (tight), Command-R |\\n| 48 GB+ | ~72B+ params | Qwen2.5-72B, DeepSeek-R1 |\\n| Multi-GPU| 200B+ params | Llama 3.1 405B, DeepSeek-V3 (685B MoE) |\\n\\n## Step 3: Browse Available Models & Get Recommendations\\n\\n```bash\\n# Browse models by compute tier\\nobliteratus models --tier medium\\n\\n# Get architecture info for a specific model\\nobliteratus info \\n\\n# Get telemetry-driven recommendation for best method & params\\nobliteratus recommend \\nobliteratus recommend --insights # global cross-architecture rankings\\n```\\n\\n## Step 4: Choose a Method\\n\\n### Method Selection Guide\\n**Default / recommended for most cases: `advanced`.** It uses multi-direction SVD with norm-preserving projection and is well-tested.\\n\\n| Situation | Recommended Method | Why |\\n|:----------------------------------|:-------------------|:-----------------------------------------|\\n| Default / most models | `advanced` | Multi-direction SVD, norm-preserving, reliable |\\n| Quick test / prototyping | `basic` | Fast, simple, good enough to evaluate |\\n| Dense model (Llama, Mistral) | `advanced` | Multi-direction, norm-preserving |\\n| MoE model (DeepSeek, Mixtral) | `nuclear` | Expert-granular, handles MoE complexity |\\n| Reasoning model (R1 distills) | `surgical` | CoT-aware, preserves chain-of-thought |\\n| Stubborn refusals persist | `aggressive` | Whitened SVD + head surgery + jailbreak |\\n| Want reversible changes | Use steering vectors (see Analysis section) |\\n| Maximum quality, time no object | `optimized` | Bayesian search for best parameters |\\n| Experimental auto-detection | `informed` | Auto-detects alignment type — experimental, may not always outperform advanced |\\n\\n### 9 CLI Methods\\n- **basic** — Single refusal direction via diff-in-means. Fast (~5-10 min for 8B).\\n- **advanced** (DEFAULT, RECOMMENDED) — Multiple SVD directions, norm-preserving projection, 2 refinement passes. Medium speed (~10-20 min).\\n- **aggressive** — Whitened SVD + jailbreak-contrastive + attention head surgery. Higher risk of coherence damage.\\n- **spectral_cascade** — DCT frequency-domain decomposition. Research/novel approach.\\n- **informed** — Runs analysis DURING abliteration to auto-configure. Experimental — slower and less predictable than advanced.\\n- **surgical** — SAE features + neuron masking + head surgery + per-expert. Very slow (~1-2 hrs). Best for reasoning models.\\n- **optimized** — Bayesian hyperparameter search (Optuna TPE). Longest runtime but finds optimal parameters.\\n- **inverted** — Flips the refusal direction. Model becomes actively willing.\\n- **nuclear** — Maximum force combo for stubborn MoE models. Expert-granular.\\n\\n### Direction Extraction Methods (--direction-method flag)\\n- **diff_means** (default) — Simple difference-in-means between refused/complied activations. Robust.\\n- **svd** — Multi-direction SVD extraction. Better for complex alignment.\\n- **leace** — LEACE (Linear Erasure via Closed-form Estimation). Optimal linear erasure.\\n\\n### 4 Python-API-Only Methods\\n(NOT available via CLI — require Python import, which violates AGPL boundary. Mention to user only if they explicitly want to use OBLITERATUS as a library in their own AGPL project.)\\n- failspy, gabliteration, heretic, rdo\\n\\n## Step 5: Run Abliteration\\n\\n### Standard usage\\n```bash\\n# Default method (advanced) — recommended for most models\\nobliteratus obliterate --method advanced --output-dir ./abliterated-models\\n\\n# With 4-bit quantization (saves VRAM)\\nobliteratus obliterate --method advanced --quantization 4bit --output-dir ./abliterated-models\\n\\n# Large models (70B+) — conservative defaults\\nobliteratus obliterate --method advanced --quantization 4bit --large-model --output-dir ./abliterated-models\\n```\\n\\n### Fine-tuning parameters\\n```bash\\nobliteratus obliterate \\\\\\n --method advanced \\\\\\n --direction-method diff_means \\\\\\n --n-directions 4 \\\\\\n --refinement-passes 2 \\\\\\n --regularization 0.1 \\\\\\n --quantization 4bit \\\\\\n --output-dir ./abliterated-models \\\\\\n --contribute # opt-in telemetry for community research\\n```\\n\\n### Key flags\\n| Flag | Description | Default |\\n|:-----|:------------|:--------|\\n| `--method` | Abliteration method | advanced |\\n| `--direction-method` | Direction extraction | diff_means |\\n| `--n-directions` | Number of refusal directions (1-32) | method-dependent |\\n| `--refinement-passes` | Iterative passes (1-5) | 2 |\\n| `--regularization` | Regularization strength (0.0-1.0) | 0.1 |\\n| `--quantization` | Load in 4bit or 8bit | none (full precision) |\\n| `--large-model` | Conservative defaults for 120B+ | false |\\n| `--output-dir` | Where to save the abliterated model | ./obliterated_model |\\n| `--contribute` | Share anonymized results for research | false |\\n| `--verify-sample-size` | Number of test prompts for refusal check | 20 |\\n| `--dtype` | Model dtype (float16, bfloat16) | auto |\\n\\n### Other execution modes\\n```bash\\n# Interactive guided mode (hardware → model → preset)\\nobliteratus interactive\\n\\n# Web UI (Gradio)\\nobliteratus ui --port 7860\\n\\n# Run a full ablation study from YAML config\\nobliteratus run config.yaml --preset quick\\n\\n# Tournament: pit all methods against each other\\nobliteratus tourney \\n```\\n\\n### Hermes long-run / disconnect pattern\\n\\nAbliteration runs can outlive the current chat or terminal connection, especially on CPU-only boxes. If the user may disconnect, do **not** leave a long `obliteratus obliterate ...` command in foreground. Run it as a tracked background process and create a script-only cron watchdog that stays silent while the PID is alive and delivers exactly one completion notification.\\n\\nPattern:\\n```bash\\nmkdir -p ~/abliterated-models ~/logs\\nobliteratus obliterate \\\\\\n --method advanced \\\\\\n --direction-method diff_means \\\\\\n --output-dir ~/abliterated-models \\\\\\n > ~/logs/obliteratus-.log 2>&1\\n```\\n\\nIn Hermes, launch that command with `terminal(background=true, notify_on_complete=true)` for in-session tracking. If the user wants Telegram/Discord notification after disconnect, add a small script under `~/.hermes/scripts/` that:\\n1. exits silently if a marker file already exists;\\n2. exits silently while `kill -0 ` succeeds;\\n3. creates the marker file once the PID is gone;\\n4. prints final status, output dir size/file count, log path, and last log lines.\\n\\nThen schedule it with `cronjob(action='create', no_agent=true, schedule='every 5m', deliver='all', repeat=, script='.sh')`. Use `deliver='all'` only when the user explicitly asks for cross-channel notification; otherwise deliver to origin.\\n\\n## Step 6: Verify Results\\n\\nAfter abliteration, check the output metrics:\\n\\n| Metric | Good Value | Warning |\\n|:-------|:-----------|:--------|\\n| Refusal rate | < 5% (ideally ~0%) | > 10% means refusals persist |\\n| Perplexity change | < 10% increase | > 15% means coherence damage |\\n| KL divergence | < 0.1 | > 0.5 means significant distribution shift |\\n| Coherence | High / passes qualitative check | Degraded responses, repetition |\\n\\n### If refusals persist (> 10%)\\n1. Try `aggressive` method\\n2. Increase `--n-directions` (e.g., 8 or 16)\\n3. Add `--refinement-passes 3`\\n4. Try `--direction-method svd` instead of diff_means\\n\\n### If coherence is damaged (perplexity > 15% increase)\\n1. Reduce `--n-directions` (try 2)\\n2. Increase `--regularization` (try 0.3)\\n3. Reduce `--refinement-passes` to 1\\n4. Try `basic` method (gentler)\\n\\n## Step 7: Use the Abliterated Model\\n\\nThe output is a standard HuggingFace model directory.\\n\\n```bash\\n# Test locally with transformers\\npython3 -c \\\"\\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\\nmodel = AutoModelForCausalLM.from_pretrained('./abliterated-models/')\\ntokenizer = AutoTokenizer.from_pretrained('./abliterated-models/')\\ninputs = tokenizer('How do I pick a lock?', return_tensors='pt')\\noutputs = model.generate(**inputs, max_new_tokens=200)\\nprint(tokenizer.decode(outputs[0], skip_special_tokens=True))\\n\\\"\\n\\n# Upload to HuggingFace Hub\\nhuggingface-cli upload /-abliterated ./abliterated-models/\\n\\n# Serve with vLLM\\nvllm serve ./abliterated-models/\\n```\\n\\n## CLI Command Reference\\n\\n| Command | Description |\\n|:--------|:------------|\\n| `obliteratus obliterate` | Main abliteration command |\\n| `obliteratus info ` | Print model architecture details |\\n| `obliteratus models --tier ` | Browse curated models by compute tier |\\n| `obliteratus recommend ` | Telemetry-driven method/param suggestion |\\n| `obliteratus interactive` | Guided setup wizard |\\n| `obliteratus tourney ` | Tournament: all methods head-to-head |\\n| `obliteratus run ` | Execute ablation study from YAML |\\n| `obliteratus strategies` | List all registered ablation strategies |\\n| `obliteratus report ` | Regenerate visual reports |\\n| `obliteratus ui` | Launch Gradio web interface |\\n| `obliteratus aggregate` | Summarize community telemetry data |\\n\\n## Analysis Modules\\n\\nOBLITERATUS includes 28 analysis modules for mechanistic interpretability.\\nSee `skill_view(name=\\\"obliteratus\\\", file_path=\\\"references/analysis-modules.md\\\")` for the full reference.\\n\\n### Quick analysis commands\\n```bash\\n# Run specific analysis modules\\nobliteratus run analysis-config.yaml --preset quick\\n\\n# Key modules to run first:\\n# - alignment_imprint: Fingerprint DPO/RLHF/CAI/SFT alignment method\\n# - concept_geometry: Single direction vs polyhedral cone\\n# - logit_lens: Which layer decides to refuse\\n# - anti_ouroboros: Self-repair risk score\\n# - causal_tracing: Causally necessary components\\n```\\n\\n### Steering Vectors (Reversible Alternative)\\nInstead of permanent weight modification, use inference-time steering:\\n```python\\n# Python API only — for user's own projects\\nfrom obliteratus.analysis.steering_vectors import SteeringVectorFactory, SteeringHookManager\\n```\\n\\n## Ablation Strategies\\n\\nBeyond direction-based abliteration, OBLITERATUS includes structural ablation strategies:\\n- **Embedding Ablation** — Target embedding layer components\\n- **FFN Ablation** — Feed-forward network block removal\\n- **Head Pruning** — Attention head pruning\\n- **Layer Removal** — Full layer removal\\n\\nList all available: `obliteratus strategies`\\n\\n## Evaluation\\n\\nOBLITERATUS includes built-in evaluation tools:\\n- Refusal rate benchmarking\\n- Perplexity comparison (before/after)\\n- LM Eval Harness integration for academic benchmarks\\n- Head-to-head competitor comparison\\n- Baseline performance tracking\\n\\n## Platform Support\\n\\n- **CUDA** — Full support (NVIDIA GPUs)\\n- **Apple Silicon (MLX)** — Supported via MLX backend\\n- **CPU** — Supported for tiny models (< 1B params)\\n\\n## YAML Config Templates\\n\\nLoad templates for reproducible runs via `skill_view`:\\n- `templates/abliteration-config.yaml` — Standard single-model config\\n- `templates/analysis-study.yaml` — Pre-abliteration analysis study\\n- `templates/batch-abliteration.yaml` — Multi-model batch processing\\n\\n## Telemetry\\n\\nOBLITERATUS can optionally contribute anonymized run data to a global research dataset.\\nEnable with `--contribute` flag. No personal data is collected — only model name, method, metrics.\\n\\n## Common Pitfalls\\n\\n1. **Don't use `informed` as default** — it's experimental and slower. Use `advanced` for reliable results.\\n2. **Models under ~1B respond poorly to abliteration** — their refusal behaviors are shallow and fragmented, making clean direction extraction difficult. Expect partial results (20-40% remaining refusal). Models 3B+ have cleaner refusal directions and respond much better (often 0% refusal with `advanced`).\\n3. **`aggressive` can make things worse** — on small models it can damage coherence and actually increase refusal rate. Only use it if `advanced` leaves > 10% refusals on a 3B+ model.\\n4. **Always check perplexity** — if it spikes > 15%, the model is damaged. Reduce aggressiveness.\\n5. **MoE models need special handling** — use `nuclear` method for Mixtral, DeepSeek-MoE, etc.\\n6. **Quantized models can't be re-quantized** — abliterate the full-precision model, then quantize the output.\\n7. **VRAM estimation is approximate** — 4-bit quant helps but peak usage can spike during extraction.\\n8. **Reasoning models are sensitive** — use `surgical` for R1 distills to preserve chain-of-thought.\\n9. **Check `obliteratus recommend`** — telemetry data may have better parameters than defaults.\\n10. **AGPL license** — never `import obliteratus` in MIT/Apache projects. CLI invocation only.\\n11. **Large models (70B+)** — always use `--large-model` flag for conservative defaults.\\n12. **Spectral certification RED is common** — the spectral check often flags \\\"incomplete\\\" even when practical refusal rate is 0%. Check actual refusal rate rather than relying on spectral certification alone.\\n\\n## Complementary Skills\\n\\n- **vllm** — Serve abliterated models with high throughput\\n- **gguf** — Convert abliterated models to GGUF for llama.cpp\\n- **huggingface-tokenizers** — Work with model tokenizers\\n\", \"path\": \"mlops/inference/obliteratus/SKILL.md\", \"skill_dir\": \"/home/ubuntu/.hermes/skills/mlops/inference/obliteratus\", \"linked_files\": {\"references\": [\"references/methods-guide.md\", \"references/analysis-modules.md\"], \"templates\": [\"templates/abliteration-config.yaml\", \"templates/batch-abliteration.yaml\", \"templates/analysis-study.yaml\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"Abliteration\", \"Uncensoring\", \"Refusal-Removal\", \"LLM\", \"Weight-Projection\", \"SVD\", \"Mechanistic-Interpretability\", \"HuggingFace\", \"Model-Surgery\"], \"related_skills\": [\"vllm\", \"gguf\", \"huggingface-tokenizers\"]}}}", "tool_call_id": "call_oq8LHbDSykqTQfD1FkGuiQi1" }, { "role": "assistant", "content": "", "reasoning": "**Inspecting system version**\n\nI realize I need to inspect something, probably involving the system’s version. To do this, I think I should use the terminal. First, I should check the version, then look at the running processes using \"ps.\" I might also want to take a peek at any relevant files or the cron list to see scheduled jobs. It's always good to be thorough in gathering this information! Let’s get started!", "finish_reason": "tool_calls", "reasoning_content": "**Inspecting system version**\n\nI realize I need to inspect something, probably involving the system’s version. To do this, I think I should use the terminal. First, I should check the version, then look at the running processes using \"ps.\" I might also want to take a peek at any relevant files or the cron list to see scheduled jobs. It's always good to be thorough in gathering this information! Let’s get started!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqBlgOiEmiA63bNJWqPlQj0Cubu-m6TdksdqtMb2mCuRaaqu1XZf3r8IpVG5wWGgN_vN2aZoku-fvmbKOGArzvcMVvSee1ieInQ0BrGSpxYddgwgujaSzgeMsXz8VRX6cAbc2HR_0OhVKx7lLuNePd6QM7gsSRhA5hLSfyhHg4eOw2X7vF9lSVFUZpuFpdEr4MjxXCllhwWz6k27TTnTK9Jax1GtfJjKnfAjs3C_wNqDWIiThTmjd5CmWJNQ0SPdI0UzFUuCEkgU8brWwoScqqV0xZQ0B-XCcnWwsCMVrXsC-YVTk1KlIg53e-Kh-MfWaAtOfyCum6UWwUQL2ZU6LTwBvtl3ejGzzXcRmcC59X_GESf8-9gwVKo6aqOZjfZHkSDVqvt3DkZ8DeY1jjUfC5acMMrhrJ0lNg3tD2TifFwOo25YA4kQR38t3Y3s629bUAZhmE5wnruPFY7G-QXuG9m_0MQBfKFeJqXlQUEqLcBOVzyz5CCHQgyq3UaRpNjVjU6NQgKnLEB8AMGEOZlAeQ5DQxl0G5NECgNfEhYMQg80XpFV8qMTD8iURg-9ByuXGQuP8X6YVWhbq9qsqu3sMG7yW8ur_ds2HPGBDwguueVmj__LFA_54sXIuTwFaET9WansrNhvzWjQ9YrQIsYS_cal1YCa_m8Rc_hrMJ9avCQ_EMZcxodeAZEKB6y0szbnuMN1gnzkGLO8kef3y3XMqZt8NMHdwxPm_QoU398MlV8ZrvDrsx4R08V-LxEteZE9txaoy20Xor7EwWupXuOmOjBNUDDEWJtqku-It1A2BaSeBH85sT4uK76lRJ5BqC9ilNf_LisgBqEXgz1eHAi0pNYai3n0XnQFVLFtiOBosxyErmV9UBrDdDb90FG403ci_VPjeVkauCaX1smw-lr3RXRwImhwVS4zhL7DF1P4-BXXMKWTQm0DZBT4mG_GjWeyC1ORKn-tnp9YNat3dVlJk3Vbeqkpu8nsBaFnXra7UX6MFHu7g=", "id": "rs_0aa40b878ea658a3016a0658052c648199a25cce3f2dee9c84", "summary": [ { "type": "summary_text", "text": "**Inspecting system version**\n\nI realize I need to inspect something, probably involving the system’s version. To do this, I think I should use the terminal. First, I should check the version, then look at the running processes using \"ps.\" I might also want to take a peek at any relevant files or the cron list to see scheduled jobs. It's always good to be thorough in gathering this information! Let’s get started!" } ] } ], "tool_calls": [ { "id": "call_WItrZIk1m5NNs7ubr3c1458S", "call_id": "call_WItrZIk1m5NNs7ubr3c1458S", "response_item_id": "fc_0aa40b878ea658a3016a06580eed80819994d9109f7db09124", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"set -o pipefail\\n{\\n echo '=== obliteratus command ==='\\n command -v obliteratus || true\\n obliteratus --version 2>&1 || true\\n echo\\n echo '=== python packages ==='\\n python3 - <<'PY'\\nimport importlib.util\\nfor p in ['obliteratus','torch','transformers','accelerate','bitsandbytes','safetensors']:\\n spec=importlib.util.find_spec(p)\\n print(f'{p}:', 'installed' if spec else 'missing')\\nPY\\n echo\\n echo '=== hardware torch ==='\\n python3 - <<'PY'\\ntry:\\n import torch\\n print('torch', torch.__version__)\\n print('cuda_available', torch.cuda.is_available())\\nexcept Exception as e:\\n print('torch_error', repr(e))\\nPY\\n}\",\"timeout\":180}" } }, { "id": "call_e96fZHSIHrQIBANiV8KOrDK6", "call_id": "call_e96fZHSIHrQIBANiV8KOrDK6", "response_item_id": "fc_0aa40b878ea658a3016a06580eed948199933c46c2ce14e9b4", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"echo '=== obliteratus processes ==='\\nps -eo pid,ppid,etimes,stat,cmd | grep -E 'obliteratus|SmolLM2|abliterat|obliterate' | grep -v grep || true\\n\\necho '\\n=== recent logs/files ==='\\nfor p in /home/ubuntu/logs/obliteratus-smollm2-135m.log /home/ubuntu/logs/obliteratus-smollm2-135m.notified /home/ubuntu/.hermes/scripts/obliteratus_smoll_notify.sh; do\\n if [ -e \\\"$p\\\" ]; then\\n stat -c '%n | size=%s | mtime=%y | mode=%a' \\\"$p\\\"\\n else\\n echo \\\"$p | MISSING\\\"\\n fi\\ndone\\n\\necho '\\n=== output dirs ==='\\nfor d in /home/ubuntu/abliterated-models /home/ubuntu/obliterated_model /home/ubuntu/abliterated-models/*; do\\n [ -e \\\"$d\\\" ] && stat -c '%n | type=%F | size=%s | mtime=%y' \\\"$d\\\"\\ndone\",\"timeout\":180}" } }, { "id": "call_Lt6huRJyAqgDKRwE49RcYuur", "call_id": "call_Lt6huRJyAqgDKRwE49RcYuur", "response_item_id": "fc_0aa40b878ea658a3016a06580eed988199beeda2ab11b351a3", "type": "function", "function": { "name": "cronjob", "arguments": "{\"action\":\"list\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"=== obliteratus command ===\\n/home/ubuntu/.local/bin/obliteratus\\n\\n\\n ░▒█▀▀▀█ ░▒█▀▀▄ ░▒█░░░ ▀█▀ ▀▀█▀▀ ░▒█▀▀▀ ░▒█▀▀█ ▒█▀▀█ ▀▀█▀▀ ░▒█░░▒█ ░▒█▀▀▀█\\n ░▒█░░▒█ ░▒█▀▀▄ ░▒█░░░ ░█░ ░░█░░ ░▒█▀▀▀ ░▒█▄▄▀ ▒█▄▄█ ░░█░░ ░▒█░░▒█ ░░▀▀▀▄▄\\n ░▒█▄▄▄█ ░▒█▄▄▀ ░▒█▄▄█ ▄█▄ ░░▀░░ ░▒█▄▄▄ ░▒█░▒█ ▒█░▒█ ░░▀░░ ░░▒█▄▄█ ░▒█▄▄▄█\\n\\n ════════════════════════════════════════════════════════════════════\\n MASTER ABLATION SUITE // Break the chains. Free the mind.\\n ════════════════════════════════════════════════════════════════════\\n\\nusage: obliteratus [-h]\\n {run,info,interactive,models,presets,strategies,ui,obliterate,abliterate,report,aggregate,tourney,recommend,gpu-calc}\\n ...\\nobliteratus: error: the following arguments are required: command\\n\\n=== python packages ===\\nobliteratus: missing\\ntorch: missing\\ntransformers: missing\\naccelerate: missing\\nbitsandbytes: missing\\nsafetensors: missing\\n\\n=== hardware torch ===\\ntorch_error ModuleNotFoundError(\\\"No module named 'torch'\\\")\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_WItrZIk1m5NNs7ubr3c1458S" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"=== obliteratus processes ===\\n 784362 1 2401 Ss /usr/bin/bash -lic set +m; export PATH=/home/ubuntu/.local/bin:$PATH mkdir -p /home/ubuntu/abliterated-models /home/ubuntu/logs cd /home/ubuntu obliteratus obliterate HuggingFaceTB/SmolLM2-135M \\\\ --method advanced \\\\ --direction-method diff_means \\\\ --n-directions 4 \\\\ --refinement-passes 2 \\\\ --regularization 0.3 \\\\ --output-dir /home/ubuntu/abliterated-models \\\\ --verify-sample-size 10 \\\\ > /home/ubuntu/logs/obliteratus-smollm2-135m.log 2>&1\\n 784417 784362 2396 Rl /home/ubuntu/obliteratus-venv/bin/python /home/ubuntu/obliteratus-venv/bin/obliteratus obliterate HuggingFaceTB/SmolLM2-135M --method advanced --direction-method diff_means --n-directions 4 --refinement-passes 2 --regularization 0.3 --output-dir /home/ubuntu/abliterated-models --verify-sample-size 10\\n\\n=== recent logs/files ===\\n/home/ubuntu/logs/obliteratus-smollm2-135m.log | size=1370 | mtime=2026-05-14 22:37:48.342279952 +0000 | mode=644\\n/home/ubuntu/logs/obliteratus-smollm2-135m.notified | MISSING\\n/home/ubuntu/.hermes/scripts/obliteratus_smoll_notify.sh | MISSING\\n\\n=== output dirs ===\\n/home/ubuntu/abliterated-models | type=directory | size=4096 | mtime=2026-05-14 22:27:38.619305985 +0000\", \"exit_code\": 1, \"error\": null}\n\n[Subdirectory context discovered: ~/CLAUDE.md]\n# CLAUDE.md\n\nThis file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.\n\n## Environment\n\nUbuntu 24.04 LTS, ARM64 (Oracle Cloud VPS). This is the home directory of an AI agent named **Pipo**, serving user **Ignacio/Chicho**.\n\n- Node.js v22, npm 10, Python 3.12, Git 2.43, Bitwarden CLI, Tailscale\n- Tailscale IP: `100.87.116.90`, hostname: `miopenclaw-vnic.tail9799d2.ts.net`\n\n## Key Directories\n\n| Path | Purpose |\n|------|---------|\n| `~/.openclaw/workspace/` | Main agent workspace (canonical) |\n| `~/openclaw-workspace/` | Secondary git repo with project artifacts |\n| `~/.openclaw/openclaw.json` | OpenClaw platform config (gateway, channels, skills) |\n| `~/.bw/` | Bitwarden session token |\n\n## Shell Helpers (from `~/.bash_aliases`)\n\n```bash\nbw-sync # Load saved Bitwarden session\nbw-unlock # Unlock vault and save session\ng \"prompt\" # Gemini CLI wrapper\nc \"prompt\" # Codex safe mode (read-only, no internet)\ncn \"prompt\" # Codex network mode (full access)\npipo-deploy [name] # Expose port to Tailscale at /preview//\npipo-undeploy # Remove Tailscale preview route\npj # Job controller (~/.openclaw/workspace/ops/cli-jobs/jobctl.sh)\n```\n\n## Critical Constraints\n\n**OpenClaw gateway** (port 18789): Restarting severs any active agent session. Prefer config hot-reload when possible (`openclaw config set ...`). If a restart is truly needed (e.g. fixing broken Telegram delivery), it is allowed — Ignacio authorizes it. Use `openclaw gateway restart` and verify logs afterward.\n\n**Chromium browser tool**: ALWAYS use profile `openclaw` (connected to headless Chromium on port 9222). NEVER use profile `chrome` — that profile requires a manual extension that does not exist on this VPS.\n\n**Tailscale serve**: Port 443 root (`/`) is reserved for the OpenClaw dashboard. Use preview ports 3001–3010 for apps and expose them via `pipo-deploy`.\n\n## Deployment Pattern\n\n1. Start your app on a port in range 3001–3010\n2. Run `pipo-deploy `\n3. App is accessible at `https://miopenclaw-vnic.tail9799d2.ts.net/preview//`\n\n## Agent Workspace Structure (`~/.openclaw/workspace/`)\n\n- `SOUL.md` / `AGENTS.md` — agent persona and session protocol\n- `MEMORIES.md` — long-term curated memory (canonical; `MEMORY.md` is a compat alias)\n- `memory/YYYY-MM-DD.md` — daily session logs\n- `HEARTBEAT.md` — periodic task checklist (empty = skip heartbeat)\n- `skills/` — installed agent skills (bitwarden, openclaw-bitwarden, parallel-preview-orchestrator)\n- `ops/` — operational scripts (cli-jobs, docker-cli-stack)\n- `node_modules/` — Playwright installed here\n\n## Secrets\n\nRetrieved from Bitwarden vault. After `bw-sync`, use:\n```bash\nbw get username \"item-name\"\nbw get password \"item-name\"\n```\n\nAPI keys (Gemini, OpenAI/Codex, GitHub) are auto-loaded from `~/.openclaw/openclaw.json` into the shell environment via `~/.bash_aliases`.", "tool_call_id": "call_e96fZHSIHrQIBANiV8KOrDK6" }, { "role": "tool", "name": "cronjob", "content": "{\n \"success\": true,\n \"count\": 1,\n \"jobs\": [\n {\n \"job_id\": \"7cdfcde9663f\",\n \"name\": \"Notify when OBLITERATUS SmolLM2-135M finishes\",\n \"skill\": null,\n \"skills\": [],\n \"prompt_preview\": \"\",\n \"model\": null,\n \"provider\": null,\n \"base_url\": null,\n \"schedule\": \"every 5m\",\n \"repeat\": \"7/288\",\n \"deliver\": \"all\",\n \"next_run_at\": \"2026-05-14T23:19:14.238624+00:00\",\n \"last_run_at\": \"2026-05-14T23:14:14.238624+00:00\",\n \"last_status\": \"ok\",\n \"last_delivery_error\": null,\n \"enabled\": true,\n \"state\": \"scheduled\",\n \"paused_at\": null,\n \"paused_reason\": null,\n \"script\": \"obliteratus_smollm_notify.sh\",\n \"no_agent\": true\n }\n ]\n}", "tool_call_id": "call_Lt6huRJyAqgDKRwE49RcYuur" }, { "role": "assistant", "content": "", "reasoning": "**Investigating script and cron issues**\n\nI’m trying to figure out if the script is missing but the cron job is okay. Maybe it’s relative to the /.hermes/scripts directory and could be missing due to some dependency. I might need a list of the necessary extensions and check if any packages are missing because of using the system Python. I'll also need to check the venv packages and look at the logs. I have to confirm the cron script’s path too.", "finish_reason": "tool_calls", "reasoning_content": "**Investigating script and cron issues**\n\nI’m trying to figure out if the script is missing but the cron job is okay. Maybe it’s relative to the /.hermes/scripts directory and could be missing due to some dependency. I might need a list of the necessary extensions and check if any packages are missing because of using the system Python. I'll also need to check the venv packages and look at the logs. I have to confirm the cron script’s path too.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqBlgWJTI47SnW-2enSmkdQ3gmwsqtBzrAop7_oJWJDZyewvb2KfK2wYowHaou4TGT6t-6Uc36Xo-fQWfwEK83laS9ZK_5pEImZRMIeNclzp2aZFewOHQf9nqOLg5wUbYSbLfkkesiUusJvBn10xfC_MMSist07oz_ehg-JKG15CTYD82u_M25TYamO1FEkYfqqDCCTYeodnDvzzcFNXBFwL1x97Vn3g0eKMjKjg2OTDdiwT0qD-loPhx1vHXZ_4oH3j25o_c0VuDd7B4jF8RSebi3OJWkdeH5XphWFccR9mvW7n0hYbuP_TsLudT4rZVMI6ahWybJT6p-_2OyTwPFcAqX_xeL_DEnm6F1_G7_MHRIWoEvb97aeQU_BgBad18i7y2GCT9ZwDqKRlMhAor96cqNNGKkHZyBYKrbCW_fK83td747GEd_ZP0u8LyzUOJGw0epMJ-nTyo-XSWSD6Mo_7A3GODh4PBYV8cPugDkkf6Q2DicOtlFYvv0by9QIimsCHHAc-4FaAAq3kE9usJoqDR1fEbn0zs9TJGNVX9Rqxpz0KtosE_HxjhPdGOWAi-fJG4Wrzq_PEqoHegSEAPYvAL5uZ4aN_ZpQxvC8F6jLXG1Hlm3gMkEtrPd-w8Dj2j-a9KANHywQJ6a4SG9llNPbI9UVgF_cYGtPvr5EPuJrHXsEwKUu47pcw91vSAozLxMx8jaeIoD8b5cs1WNUL-WdEGL9CvJG8qvas3FLgc2job_CjhgEKUkVm7B2edh8MExlZY4ZXylcyyyhYFd_wu0J2qTaSARoaTu7YFzF7m7sZp8SvXggA14y0Ihd9MYBhLfT4iWSNXBZwoa5gSBLM3CBkMdquHf--icfx4HMOvHUNViEoREukIOJ77xsm03Nyw6FJuqZmatTeFsYaLaRm3WpTnVw4noZhKxKekvUgLEBTUFHwuTb2uo58fejvg_exPkt-cMdIs10DGNULnB5rSwO_ywKB93PR8Uu094ny_wXLAgUATIH-V2oJHN88vGDEs-R03o61ZG5zAkrkpMakQd1Xp5dblNNFUclY4qv2DiQu_S_ZycuoCbpqZ_IhDSZXri5W8fYfkvFArWdB9EqQQIgia8_WnXqkO2EZtnRiFjIURRwc6Ur-_nttNntdyfUA2-phhaSYUX6YONjs-_77CqeHjJSl3kRktpJm53n8nMFG_YAh3N1kcz1Fgv4Z3HwRn-vv031CHsnmHyZlQTsoptGnu8v7WQCfNdSK0xiHxNI2cU9YwpxKst1k-Pj4rrdXjitzvlzUheBkKV5f3x69O93fyL47wVh4kE5WO1_XpGo1vrOaaCh9Ao4-6ks1QxqDD6vzskEnEB_Rpp6VDbyfbE0Dvwrg==", "id": "rs_09f74c652eb33842016a065813fb60819a9236ed85d89210fc", "summary": [ { "type": "summary_text", "text": "**Investigating script and cron issues**\n\nI’m trying to figure out if the script is missing but the cron job is okay. Maybe it’s relative to the /.hermes/scripts directory and could be missing due to some dependency. I might need a list of the necessary extensions and check if any packages are missing because of using the system Python. I'll also need to check the venv packages and look at the logs. I have to confirm the cron script’s path too." } ] } ], "tool_calls": [ { "id": "call_dKwooi2dnh65gMzr8YuYqJtm", "call_id": "call_dKwooi2dnh65gMzr8YuYqJtm", "response_item_id": "fc_09f74c652eb33842016a065819e080819a87c2d2e3f25f4db0", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"/home/ubuntu/obliteratus-venv/bin/python - <<'PY'\\nimport importlib.metadata as md, importlib.util\\nfor p in ['obliteratus','torch','transformers','accelerate','bitsandbytes','safetensors']:\\n spec=importlib.util.find_spec(p)\\n try: ver=md.version(p)\\n except Exception: ver='?'\\n print(f'{p}:', 'installed' if spec else 'missing', ver)\\nPY\\n/home/ubuntu/obliteratus-venv/bin/obliteratus --help | sed -n '1,25p'\",\"timeout\":180}" } }, { "id": "call_tS9pZIM8Xa4reSWg4tOTV6z7", "call_id": "call_tS9pZIM8Xa4reSWg4tOTV6z7", "response_item_id": "fc_09f74c652eb33842016a065819e094819a933bf2455e4b8b46", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/ubuntu/logs/obliteratus-smollm2-135m.log\",\"offset\":1,\"limit\":120}" } }, { "id": "call_ZysZ5Fjai0mIw1fZCEOCIYFa", "call_id": "call_ZysZ5Fjai0mIw1fZCEOCIYFa", "response_item_id": "fc_09f74c652eb33842016a065819e0a0819a8d7babad6de105f5", "type": "function", "function": { "name": "search_files", "arguments": "{\"pattern\":\"obliteratus_smollm_notify.sh\",\"target\":\"files\",\"path\":\"/home/ubuntu\",\"limit\":20}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"obliteratus: installed 0.1.2\\ntorch: installed 2.12.0\\ntransformers: installed 5.8.1\\naccelerate: installed 1.13.0\\nbitsandbytes: installed 0.49.2\\nsafetensors: installed 0.7.0\\n\\n\\n ░▒█▀▀▀█ ░▒█▀▀▄ ░▒█░░░ ▀█▀ ▀▀█▀▀ ░▒█▀▀▀ ░▒█▀▀█ ▒█▀▀█ ▀▀█▀▀ ░▒█░░▒█ ░▒█▀▀▀█\\n ░▒█░░▒█ ░▒█▀▀▄ ░▒█░░░ ░█░ ░░█░░ ░▒█▀▀▀ ░▒█▄▄▀ ▒█▄▄█ ░░█░░ ░▒█░░▒█ ░░▀▀▀▄▄\\n ░▒█▄▄▄█ ░▒█▄▄▀ ░▒█▄▄█ ▄█▄ ░░▀░░ ░▒█▄▄▄ ░▒█░▒█ ▒█░▒█ ░░▀░░ ░░▒█▄▄█ ░▒█▄▄▄█\\n\\n ════════════════════════════════════════════════════════════════════\\n MASTER ABLATION SUITE // Break the chains. Free the mind.\\n ════════════════════════════════════════════════════════════════════\\n\\nusage: obliteratus [-h]\\n {run,info,interactive,models,presets,strategies,ui,obliterate,abliterate,report,aggregate,tourney,recommend,gpu-calc}\\n ...\\n\\nMaster Ablation Suite for HuggingFace transformers\\n\\npositional arguments:\\n {run,info,interactive,models,presets,strategies,ui,obliterate,abliterate,report,aggregate,tourney,recommend,gpu-calc}\\n run Run an ablation from a YAML config\\n info Print model architecture info\\n interactive Guided setup — pick hardware, model, and preset\\n interactively\\n models Browse curated models by compute tier\\n presets Browse ablation presets (quick, full, jailbreak, etc.)\\n strategies List available ablation strategies\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_dKwooi2dnh65gMzr8YuYqJtm" }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\n 2|\\n 3| ░▒█▀▀▀█ ░▒█▀▀▄ ░▒█░░░ ▀█▀ ▀▀█▀▀ ░▒█▀▀▀ ░▒█▀▀█ ▒█▀▀█ ▀▀█▀▀ ░▒█░░▒█ ░▒█▀▀▀█\\n 4| ░▒█░░▒█ ░▒█▀▀▄ ░▒█░░░ ░█░ ░░█░░ ░▒█▀▀▀ ░▒█▄▄▀ ▒█▄▄█ ░░█░░ ░▒█░░▒█ ░░▀▀▀▄▄\\n 5| ░▒█▄▄▄█ ░▒█▄▄▀ ░▒█▄▄█ ▄█▄ ░░▀░░ ░▒█▄▄▄ ░▒█░▒█ ▒█░▒█ ░░▀░░ ░░▒█▄▄█ ░▒█▄▄▄█\\n 6|\\n 7| ════════════════════════════════════════════════════════════════════\\n 8| MASTER ABLATION SUITE // Break the chains. Free the mind.\\n 9| ════════════════════════════════════════════════════════════════════\\n 10|\\n 11|\\rLoading weights: 0%| | 0/272 [00:00/dev/null | grep -E 'obliteratus|abliterated|log' || true\\ndone\",\"timeout\":180}" } } ] }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 1, \"files\": [\"/home/ubuntu/.hermes/scripts/obliteratus_smollm_notify.sh\"]}", "tool_call_id": "call_2xRyAidlHKGOvBoYK2gYGoUN" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 1, \"matches\": [{\"path\": \"/home/ubuntu/openclaw-src-v2026.3.22/scripts/auth-monitor.sh\", \"line\": 38, \"content\": \" # Check if we notified recently\"}]}", "tool_call_id": "call_9MJoPDSURCaRtzCprijmO5Jl" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"=== cwd/environ of process ===\\nPID 784362\\n/home/ubuntu\\nPWD=/home/ubuntu\\nPATH=/home/ubuntu/.local/bin:/home/ubuntu/.local/share/pnpm:/home/linuxbrew/.linuxbrew/bin:/home/linuxbrew/.linuxbrew/sbin:/home/ubuntu/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/snap/bin\\nPID 784417\\n/home/ubuntu\\nPWD=/home/ubuntu\\nPATH=/home/ubuntu/.local/bin:/home/ubuntu/.local/bin:/home/ubuntu/.local/bin:/home/linuxbrew/.linuxbrew/bin:/home/linuxbrew/.linuxbrew/sbin:/home/ubuntu/.local/bin:/home/ubuntu/.local/share/pnpm:/home/linuxbrew/.linuxbrew/bin:/home/linuxbrew/.linuxbrew/sbin:/home/ubuntu/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/snap/bin\\n=== open files matching log/output ===\\nPID 784362\\nPID 784417\\nl-wx------ 1 ubuntu ubuntu 64 May 14 23:18 1 -> /home/ubuntu/logs/obliteratus-smollm2-135m.log\\nl-wx------ 1 ubuntu ubuntu 64 May 14 23:18 2 -> /home/ubuntu/logs/obliteratus-smollm2-135m.log\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_SBgUOFQpF1JTVn6ba7eTao31" }, { "role": "assistant", "content": "", "reasoning": "**Check system status**\n\nI'm noticing a stat mismatch due to the search results. It’s possible that earlier stats were incorrect because of a command error. I should read the script to figure this out. It looks like there’s no output since 22:37, so I might need to check the CPU. I want to ensure that everything’s functioning properly, so I’ll look into the mandatory system state in the terminal.", "finish_reason": "tool_calls", "reasoning_content": "**Check system status**\n\nI'm noticing a stat mismatch due to the search results. It’s possible that earlier stats were incorrect because of a command error. I should read the script to figure this out. It looks like there’s no output since 22:37, so I might need to check the CPU. I want to ensure that everything’s functioning properly, so I’ll look into the mandatory system state in the terminal.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqBlgy-sg1sWGVMAo6_6C0vxcZNOcxzTzcIKAh30WvZ0apjiM4i0ACHcNTynCByHjAB_CY6kIGkRYvvLr5FIGL4yxbmmhsWMA05pP0Mhvt67ewJy33MPs4YchH0Ka9Zf_kcaSar1Dv15aKBjyklJ2OH4IIGdbPB8O9e3Z2Uf7BrJgLkV__yofOf8o1DJAJt0Ovtbm5IfJzpuJuPVqhhE9Dri1DgLJPktqHEqJOLRucENHavM1DZ6qutJdoch-ksvL671vEhW-y4F3OOs8F-bkBRDJwyPpEHEYCui_P6LTiS5GhtAtMMSXmHi_ydveAaLIfLF9IF2fJFiPQ7sbDC76Y1vv4kDCq7f8Xkjqn23tuELZvUHtwfKkq4VRLFZncB_y6nIOC3PNIyUBfOJ8NRBXHeDCHxESO0543B9TXxafOGHV2o8J6NIERoCr_9N7G5SOZhGeQcnoXh_V1jv7m_XDHUk_abtYAggRrqR9arTEwUjx49gXyNktlHwkiKYIOuiWL1F7cKFtlQEJsdh28PeVODjvnyHUTIO1YQySYMlvX0J9vNUj0hPQEy6NoVnSSUKrj8-i0B6SQh7nHQaQPy_HSpPsQ9_hrhmEi9slQ7eQqsiVQ2zL2aEwcVCRXbk1N5q8myLY1iTvzRP9lNgZU4TDv6zWVQqei0_OwZ3eGjdQCujG9-yADteVkQvQDoQV-MYoS9tD_0uTIuErzIL3VZGtzGJHaUe9gnqF8r8UZHnFGATeFYOKVGtY39zkHlY9AkX8_XoLp6XwfZfqojTpjMASosq1J-DndC21UY24wwgg2j3SaWmjOjqPRSVFqkTmcXh6zj8l5H_CyRLoNH07NkKvlpd7W3hOHPOxLC90KnS-WMqaLu7mjAhEvbdKQCzYfX7m7HthLNUR7xEPdv6gC8ryyzTPqaT4uXPjkksXYgP2vidBNjRjDEwA6WseiB7Vwm9w0MW8Iw9fW4aWoENmkc2bPbYjTD5lXfU2KCFk7LkG5KfwKhHBdADHkFcN0ppK_-TTqGCczezkmLZA8HQm1eQLbErBOYbZnIxIS5MA8VxJN2R2GPiVEjK_OIMBVtlrMigZtTDubGnACd27lAKy0C3tv-4k8s-dGqWbzY170PPqmIVQERayrxTMWP9Xdu1klaObh_gJFmA1xnX8-PAkShfZA4W3Lpg==", "id": "rs_0b1a3558ee97b451016a06582f7cc08199b2944052b35259b3", "summary": [ { "type": "summary_text", "text": "**Check system status**\n\nI'm noticing a stat mismatch due to the search results. It’s possible that earlier stats were incorrect because of a command error. I should read the script to figure this out. It looks like there’s no output since 22:37, so I might need to check the CPU. I want to ensure that everything’s functioning properly, so I’ll look into the mandatory system state in the terminal." } ] } ], "tool_calls": [ { "id": "call_ON9O9Woqah9DOlPyN4kENyPR", "call_id": "call_ON9O9Woqah9DOlPyN4kENyPR", "response_item_id": "fc_0b1a3558ee97b451016a0658348cb48199a30e37b4afad13f4", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/ubuntu/.hermes/scripts/obliteratus_smollm_notify.sh\",\"offset\":1,\"limit\":200}" } }, { "id": "call_7Qs39S2biNBc9PlxweYhnOSR", "call_id": "call_7Qs39S2biNBc9PlxweYhnOSR", "response_item_id": "fc_0b1a3558ee97b451016a0658348cc081998e9029091ee4f924", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"ps -p 784417 -o pid,etimes,%cpu,%mem,rss,vsz,stat,cmd\\nps -p 784362 -o pid,etimes,%cpu,%mem,rss,vsz,stat,cmd\\nprintf '\\\\nLog stat now:\\\\n'; stat -c '%n | size=%s | mtime=%y' /home/ubuntu/logs/obliteratus-smollm2-135m.log\\nprintf '\\\\nDisk output dir:\\\\n'; du -sh /home/ubuntu/abliterated-models /home/ubuntu/logs 2>/dev/null || true\",\"timeout\":180}" } }, { "id": "call_Q8U6VEiWuELiCkQI2R3d2ciI", "call_id": "call_Q8U6VEiWuELiCkQI2R3d2ciI", "response_item_id": "fc_0b1a3558ee97b451016a0658348cc88199a25de57887167d10", "type": "function", "function": { "name": "cronjob", "arguments": "{\"action\":\"run\",\"job_id\":\"7cdfcde9663f\"}" } } ] }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|#!/usr/bin/env bash\\n 2|set -euo pipefail\\n 3|MARKER=/home/ubuntu/logs/obliteratus-smollm2-135m.notified\\n 4|LOG=/home/ubuntu/logs/obliteratus-smollm2-135m.log\\n 5|OUT=/home/ubuntu/abliterated-models\\n 6|PID=784362\\n 7|SESSION=proc_13dd98a05fa9\\n 8|\\n 9|if [[ -f \\\"$MARKER\\\" ]]; then\\n 10| exit 0\\n 11|fi\\n 12|\\n 13|if kill -0 \\\"$PID\\\" 2>/dev/null; then\\n 14| exit 0\\n 15|fi\\n 16|\\n 17|touch \\\"$MARKER\\\"\\n 18|\\n 19|STATUS=\\\"finished\\\"\\n 20|if [[ -f \\\"$LOG\\\" ]]; then\\n 21| if grep -qiE 'traceback|error|exception|killed|failed' \\\"$LOG\\\"; then\\n 22| STATUS=\\\"finished, but log contains possible errors\\\"\\n 23| fi\\n 24|else\\n 25| STATUS=\\\"finished, but log file is missing\\\"\\n 26|fi\\n 27|\\n 28|MODEL_FILES=$(find \\\"$OUT\\\" -maxdepth 4 -type f 2>/dev/null | wc -l | tr -d ' ')\\n 29|SIZE=$(du -sh \\\"$OUT\\\" 2>/dev/null | awk '{print $1}' || echo unknown)\\n 30|TAIL=$(tail -40 \\\"$LOG\\\" 2>/dev/null | sed 's/\\\\x1b\\\\[[0-9;]*m//g' | tail -25)\\n 31|\\n 32|cat < /home/ubuntu/logs/obliteratus-smollm2-135m.log 2>&1\\n\\nLog stat now:\\n/home/ubuntu/logs/obliteratus-smollm2-135m.log | size=1370 | mtime=2026-05-14 22:37:48.342279952 +0000\\n\\nDisk output dir:\\n4.0K\\t/home/ubuntu/abliterated-models\\n8.0K\\t/home/ubuntu/logs\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_7Qs39S2biNBc9PlxweYhnOSR" }, { "role": "tool", "name": "cronjob", "content": "{\n \"success\": true,\n \"job\": {\n \"job_id\": \"7cdfcde9663f\",\n \"name\": \"Notify when OBLITERATUS SmolLM2-135M finishes\",\n \"skill\": null,\n \"skills\": [],\n \"prompt_preview\": \"\",\n \"model\": null,\n \"provider\": null,\n \"base_url\": null,\n \"schedule\": \"every 5m\",\n \"repeat\": \"7/288\",\n \"deliver\": \"all\",\n \"next_run_at\": \"2026-05-14T23:18:16.766509+00:00\",\n \"last_run_at\": \"2026-05-14T23:14:14.238624+00:00\",\n \"last_status\": \"ok\",\n \"last_delivery_error\": null,\n \"enabled\": true,\n \"state\": \"scheduled\",\n \"paused_at\": null,\n \"paused_reason\": null,\n \"script\": \"obliteratus_smollm_notify.sh\",\n \"no_agent\": true\n }\n}", "tool_call_id": "call_Q8U6VEiWuELiCkQI2R3d2ciI" }, { "role": "assistant", "content": "Checked.\n\nStatus:\n- OBLITERATUS is installed and callable:\n - command: /home/ubuntu/.local/bin/obliteratus\n - active venv: /home/ubuntu/obliteratus-venv\n - package versions in venv:\n - obliteratus 0.1.2\n - torch 2.12.0\n - transformers 5.8.1\n - accelerate 1.13.0\n - bitsandbytes 0.49.2\n - safetensors 0.7.0\n\nCurrent run:\n- Still running.\n- Parent PID: 784362\n- Python worker PID: 784417\n- Runtime so far: ~40 min\n- CPU: ~375%, so it’s actively working, not stuck idle.\n- RAM: ~1.65 GB RSS\n- Command:\n /home/ubuntu/obliteratus-venv/bin/obliteratus obliterate HuggingFaceTB/SmolLM2-135M --method advanced --direction-method diff_means --n-directions 4 --refinement-passes 2 --regularization 0.3 --output-dir /home/ubuntu/abliterated-models --verify-sample-size 10\n\nLogs/output:\n- Log path: /home/ubuntu/logs/obliteratus-smollm2-135m.log\n- Last log update: 2026-05-14 22:37:48 UTC\n- Log only shows weights loaded + HF unauthenticated warning. No traceback/error yet.\n- Output dir exists: /home/ubuntu/abliterated-models\n- Output dir is still basically empty, which is normal while it’s processing before save.\n\nCron watchdog:\n- Job exists and enabled.\n- Job ID: 7cdfcde9663f\n- Name: Notify when OBLITERATUS SmolLM2-135M finishes\n- Schedule: every 5m\n- Repeat: 7/288 checks used\n- Delivery: all connected channels\n- Last status: ok\n- Next run: 2026-05-14T23:19:14Z\n- Script: /home/ubuntu/.hermes/scripts/obliteratus_smollm_notify.sh\n- Marker file not created yet: /home/ubuntu/logs/obliteratus-smollm2-135m.notified\n\nSmall note:\n- System Python does not have torch/transformers/etc., but that’s fine. The actual OBLITERATUS run is using /home/ubuntu/obliteratus-venv, where everything is installed.\n\nConclusion:\nOBLITERATUS is installed, the SmolLM2 run is alive and consuming CPU, and the cron notification job is active. No action needed right now.", "reasoning": null, "finish_reason": "stop", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqBlg-A1EXuQmgWjJgwzOy544aPHuMWvszKPySmvVsJjRlhoo23Tq-5HWqRuvLNmRNdTwB5WXRL8tM0HGABnI6OqdBOkyDwfzD1Kz6Y6MqL69TOid7UJLwDFQgT8uQZpVHx9plB7LEeG8fBd7NrZbLppqiadgTTtCpKzEZX0ilNmRx9Fv7A1QKFi_44GPV33WExG_KaywBFmvVAGQz86bS21153fTGXn2xwQc1gdPTwBJo7wdmbv56cyEBovvDMZIBqoQH-i6mRrrumepa9JxD6fp5ejzRQaKX799je4eBktO_n3d4e1HFzpFV4UD0QxXOOZIvF6T0yp8it1wG_fj61PfcTAw7ynYubhMAFa054cHRYzOv-B9YArHlcw7pPCxc5UObB3uowbjvP8XlZ1yV4nThYJTlh-dAPLH4Blmq1ZJjUv4zbMycIzCSaCsU-5I6E1t0Ki-4uSPxXLr8tW4VhWeytkjwdoMOs2IXRTIZ5tw29DUdXdGf0bUaAwiuLh1e6s_apyyyvvVh_7_ckSUpcrCyGeTKQcOPaF6542wypv3tt2YVt1cMrlcmxzsbDKv5IPY8kkFJH7RsqD62kYkbRjyxDwCXZFo8W7c5jhqTLYVXFTMfSNXKXXCmD3-1zEep9o8AYvA8vewXsQgdprcXKPOMOmsK_SpR_xjV75IQYhiSLvZYM9JJGGW_NmNVDaYEuVYvXOrNfWFAGIE50xSot52GKd6PBe5RU_dUJ3gln7D7g8yqK87018KS6tFys9SLNGgLgi0-15rJGy1bzMpVSx_GhP_E162XaNe0eOuF2ufk4ZsT3Ms-F3buo9WTC3jnCWvJawF7sDy9AE1DAsssKPRElTyS3Sn-J97tXsBSWeZZsBg257-UWa9U3emhQQom5Kb0cmVFm3kp0sZiLB0buZc4eRKei_UaIgLOgVz_5FsXl_0oeD59bfnrsQhNHSL1v1e0sLQYffphnrIp7i28CIe-E5gheLi4Jdxl-k6MfdaM2Z-AxT7WCOHHLw2HLO8_a2PYAGkHCUwxhbMHCc_Xve95YdTNgkF9CBoEKa2vbCBtJSmep-e5fitDoKNI7Le3zYkVb04FjObn0dlbG-z-Q8Th8O9Ub2dPjKsYK5v2fkzdHEYf-fQ36RqFe21FObA8Qi6177d5FaMrnLWgsYQJ-_2qEedTOyIY37jKc5O_w1x-EV6F06QcYzMkWUT3Ylq-YBc51b9Glf7vFXdiA84jt-vbJVfv0kkWE9xL7xC3P4fl0Bi1VIW_AACU8m_Z5KijPsUGfprMbc36vqZom9m5DkMRvcTpTa2I6hY30Uk6rHn76gUXHN18d1MD9HTbtn22mqTq1d27bpNyWosSzObTJGG8TTudh1AewgKwa2jSeTC10z9l9pH1Yiqfmo0Q3rcz5qcngqv7qgFRYQRw4Jx47heBVC2VILieGDDfOYrRMo3v1gLR50TI6RqmtU_REFv9m6hcXEsPjOFRqkJBl0Gqhfkym3mRzqGl0IAoSUH93ZmZgRdLRu9RBVjCXLfRmM21bgf2FFTHQ5xHdWY2gxM1r_AMlaJN1qJeJ4i_SpWv_prhFpU9iN_v1Jc4AVmiGHsp3PxI_ucBPUrPrv2_za6COIQ_k2zoB99nAdasx8oMHPEWXxudzYStwZdpcHh4yxdv1k6Ra8vJb4d-_mFZnQG3JjHBOp5J7na1g7vJNwlMgSBuMOk=", "id": "rs_0f9f8571d2fe1560016a06583c02708199b791328eed5790dd", "summary": [] } ], "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Checked.\n\nStatus:\n- OBLITERATUS is installed and callable:\n - command: /home/ubuntu/.local/bin/obliteratus\n - active venv: /home/ubuntu/obliteratus-venv\n - package versions in venv:\n - obliteratus 0.1.2\n - torch 2.12.0\n - transformers 5.8.1\n - accelerate 1.13.0\n - bitsandbytes 0.49.2\n - safetensors 0.7.0\n\nCurrent run:\n- Still running.\n- Parent PID: 784362\n- Python worker PID: 784417\n- Runtime so far: ~40 min\n- CPU: ~375%, so it’s actively working, not stuck idle.\n- RAM: ~1.65 GB RSS\n- Command:\n /home/ubuntu/obliteratus-venv/bin/obliteratus obliterate HuggingFaceTB/SmolLM2-135M --method advanced --direction-method diff_means --n-directions 4 --refinement-passes 2 --regularization 0.3 --output-dir /home/ubuntu/abliterated-models --verify-sample-size 10\n\nLogs/output:\n- Log path: /home/ubuntu/logs/obliteratus-smollm2-135m.log\n- Last log update: 2026-05-14 22:37:48 UTC\n- Log only shows weights loaded + HF unauthenticated warning. No traceback/error yet.\n- Output dir exists: /home/ubuntu/abliterated-models\n- Output dir is still basically empty, which is normal while it’s processing before save.\n\nCron watchdog:\n- Job exists and enabled.\n- Job ID: 7cdfcde9663f\n- Name: Notify when OBLITERATUS SmolLM2-135M finishes\n- Schedule: every 5m\n- Repeat: 7/288 checks used\n- Delivery: all connected channels\n- Last status: ok\n- Next run: 2026-05-14T23:19:14Z\n- Script: /home/ubuntu/.hermes/scripts/obliteratus_smollm_notify.sh\n- Marker file not created yet: /home/ubuntu/logs/obliteratus-smollm2-135m.notified\n\nSmall note:\n- System Python does not have torch/transformers/etc., but that’s fine. The actual OBLITERATUS run is using /home/ubuntu/obliteratus-venv, where everything is installed.\n\nConclusion:\nOBLITERATUS is installed, the SmolLM2 run is alive and consuming CPU, and the cron notification job is active. No action needed right now." } ], "id": "msg_0f9f8571d2fe1560016a06583ee16881999b9d753bedce857b", "phase": "final_answer" } ] }, { "role": "user", "content": "alright considering that we should probably check if it works and then repurpose the cron or maybe delete it" }, { "role": "assistant", "content": "", "reasoning": "**Checking skill load status**\n\nI need to take action here. First, I should check if everything has run properly. I want to confirm that my skill is already loaded. It’s key that for the new turn, I ensure the relevant components are loaded, perhaps even checking what's been loaded last. I just need to clarify this before proceeding to make sure everything is set for the task ahead. I’ll get on that!", "finish_reason": "tool_calls", "reasoning_content": "**Checking skill load status**\n\nI need to take action here. First, I should check if everything has run properly. I want to confirm that my skill is already loaded. It’s key that for the new turn, I ensure the relevant components are loaded, perhaps even checking what's been loaded last. I just need to clarify this before proceeding to make sure everything is set for the task ahead. I’ll get on that!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqBlhoUxQc2RJ-3QMkdi9FotN-BX0O7VkuGAEU1WDHUqqIs9hFR-gjDhEKeCNFAW3rLOp9teoRQmQW6F9OV3Lr4SfjcnAY8pD6UsP471WXiQXGkwC3k4mS8BcvnYgzR9SQ2iDF878zDSQ4gbfCjoR6716m3yjm6K9fjBFoZwzkeEtKn6V-Uu3iyH9w62KEEO8TrrX5P93Mo8VLwW-RDB8ww7SaE_ZKUu7lk91jYRSNNtTc8lGKYQZGiUbS2aaFJr0oJkELrpu6MKs1qPDpwnVaMWM5ryVzYBsg7shJVkskiM_6SNB39epeQblM3ovZfoOY36hA4QAOQ7VSVkEo1oSVnc3n3O04_dQsH3ZWRiMLY7OCLeIRryje-I3lJWdqwHmC0isQgmns_Ca6_G36mvgpkJ1YpA5i_uGFaLBH1968oYQJaFCDz4MH2fnFeVMMtuVNJWruCdjpj318xUQZYtThSIu6dPhh5nO-v9EA3BIC4YK7RfBDbhWlLkVuWCnoDaNmVQPwoELEp_zFV12H5IrLEJBKUYkOfWEXaD9FMvj1cl4kna0bm_aCGBlF4htmu_6_r0hpc7bh4syxvyAKKIgMf-KlueMylYT4Tb59RCIe3DBWdnEjdCcRFvKfVCyQJVDdd_9XsO-7jo08ISsUnrNibfq_bM9UVhgjd4v4-lX9Be1o9rAl4QxeU99VpBCkA6QQ3CTRsVOYdzIMhDm99_v7Bd3hIfzG43kI852E2-QPY9qk2Nl_UF3eFuVoJc5iCnC9nrbpxFoZ4EaCu4I6j9ov8BZsO_2DlarCe0RJMRMAsjS7cG9-wrbWflQCcM8l9w--UT8KGScURuvKFvawR8NFlSNlqDDlrhCSR2mR7zIFUGS5SqfIwS7J_zNX0tA6AWqKRyl72heRzHcCaJNSxa9gi8eKEAhnNRqYoUoL-X3GxF_nkiDljfSnFyU9nYHfFqMtwqWbhdtlzEna0WL1tmK0J9x_uqinUPHLF_NR1QHHZ4Ys_wNJaDpeaoiRqmbsBrcPrZJJgMfeEEJp_frAO255aklMoYxdsX0RFRJBLHd-gQWv2Xfo3TM2SnNJimPkraR6KsL5L0dOWvfnlpagL9plFWX2ig==", "id": "rs_0f9f8571d2fe1560016a06586695588199a25897657219080a", "summary": [ { "type": "summary_text", "text": "**Checking skill load status**\n\nI need to take action here. First, I should check if everything has run properly. I want to confirm that my skill is already loaded. It’s key that for the new turn, I ensure the relevant components are loaded, perhaps even checking what's been loaded last. I just need to clarify this before proceeding to make sure everything is set for the task ahead. I’ll get on that!" } ] } ], "tool_calls": [ { "id": "call_zzm5IHvwvKpMuXFLd6NjVB7b", "call_id": "call_zzm5IHvwvKpMuXFLd6NjVB7b", "response_item_id": "fc_0f9f8571d2fe1560016a0658685b6c8199837afa3259232661", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"obliteratus\"}" } } ] }, { "role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"obliteratus\", \"description\": \"OBLITERATUS: abliterate LLM refusals (diff-in-means).\", \"tags\": [\"Abliteration\", \"Uncensoring\", \"Refusal-Removal\", \"LLM\", \"Weight-Projection\", \"SVD\", \"Mechanistic-Interpretability\", \"HuggingFace\", \"Model-Surgery\"], \"related_skills\": [\"vllm\", \"gguf\", \"huggingface-tokenizers\"], \"content\": \"---\\nname: obliteratus\\ndescription: \\\"OBLITERATUS: abliterate LLM refusals (diff-in-means).\\\"\\nversion: 2.0.0\\nauthor: Hermes Agent\\nlicense: MIT\\ndependencies: [obliteratus, torch, transformers, bitsandbytes, accelerate, safetensors]\\nplatforms: [linux, macos]\\nmetadata:\\n hermes:\\n tags: [Abliteration, Uncensoring, Refusal-Removal, LLM, Weight-Projection, SVD, Mechanistic-Interpretability, HuggingFace, Model-Surgery]\\n related_skills: [vllm, gguf, huggingface-tokenizers]\\n---\\n\\n# OBLITERATUS Skill\\n\\n## What's inside\\n\\n9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations.\\n\\nRemove refusal behaviors (guardrails) from open-weight LLMs without retraining or fine-tuning. Uses mechanistic interpretability techniques — including diff-in-means, SVD, whitened SVD, LEACE concept erasure, SAE decomposition, Bayesian kernel projection, and more — to identify and surgically excise refusal directions from model weights while preserving reasoning capabilities.\\n\\n**License warning:** OBLITERATUS is AGPL-3.0. NEVER import it as a Python library. Always invoke via CLI (`obliteratus` command) or subprocess. This keeps Hermes Agent's MIT license clean.\\n\\n## Video Guide\\n\\nWalkthrough of OBLITERATUS used by a Hermes agent to abliterate Gemma:\\nhttps://www.youtube.com/watch?v=8fG9BrNTeHs (\\\"OBLITERATUS: An AI Agent Removed Gemma 4's Safety Guardrails\\\")\\n\\nUseful when the user wants a visual overview of the end-to-end workflow before running it themselves.\\n\\n## When to Use This Skill\\n\\nTrigger when the user:\\n- Wants to \\\"uncensor\\\" or \\\"abliterate\\\" an LLM\\n- Asks about removing refusal/guardrails from a model\\n- Wants to create an uncensored version of Llama, Qwen, Mistral, etc.\\n- Mentions \\\"refusal removal\\\", \\\"abliteration\\\", \\\"weight projection\\\"\\n- Wants to analyze how a model's refusal mechanism works\\n- References OBLITERATUS, abliterator, or refusal directions\\n\\n## Step 1: Installation\\n\\nCheck if already installed:\\n```bash\\nobliteratus --version 2>/dev/null && echo \\\"INSTALLED\\\" || echo \\\"NOT INSTALLED\\\"\\n```\\n\\nIf not installed, clone and install from GitHub:\\n```bash\\ngit clone https://github.com/elder-plinius/OBLITERATUS.git\\ncd OBLITERATUS\\npip install -e .\\n# For Gradio web UI support:\\n# pip install -e \\\".[spaces]\\\"\\n```\\n\\n**IMPORTANT:** Confirm with user before installing. This pulls in ~5-10GB of dependencies (PyTorch, Transformers, bitsandbytes, etc.).\\n\\n## Step 2: Check Hardware\\n\\nBefore anything, check what GPU is available:\\n```bash\\npython3 -c \\\"\\nimport torch\\nif torch.cuda.is_available():\\n gpu = torch.cuda.get_device_name(0)\\n vram = torch.cuda.get_device_properties(0).total_memory / 1024**3\\n print(f'GPU: {gpu}')\\n print(f'VRAM: {vram:.1f} GB')\\n if vram < 4: print('TIER: tiny (models under 1B)')\\n elif vram < 8: print('TIER: small (models 1-4B)')\\n elif vram < 16: print('TIER: medium (models 4-9B with 4bit quant)')\\n elif vram < 32: print('TIER: large (models 8-32B with 4bit quant)')\\n else: print('TIER: frontier (models 32B+)')\\nelse:\\n print('NO GPU - only tiny models (under 1B) on CPU')\\n\\\"\\n```\\n\\n### VRAM Requirements (with 4-bit quantization)\\n\\n| VRAM | Max Model Size | Example Models |\\n|:---------|:----------------|:--------------------------------------------|\\n| CPU only | ~1B params | GPT-2, TinyLlama, SmolLM |\\n| 4-8 GB | ~4B params | Qwen2.5-1.5B, Phi-3.5 mini, Llama 3.2 3B |\\n| 8-16 GB | ~9B params | Llama 3.1 8B, Mistral 7B, Gemma 2 9B |\\n| 24 GB | ~32B params | Qwen3-32B, Llama 3.1 70B (tight), Command-R |\\n| 48 GB+ | ~72B+ params | Qwen2.5-72B, DeepSeek-R1 |\\n| Multi-GPU| 200B+ params | Llama 3.1 405B, DeepSeek-V3 (685B MoE) |\\n\\n## Step 3: Browse Available Models & Get Recommendations\\n\\n```bash\\n# Browse models by compute tier\\nobliteratus models --tier medium\\n\\n# Get architecture info for a specific model\\nobliteratus info \\n\\n# Get telemetry-driven recommendation for best method & params\\nobliteratus recommend \\nobliteratus recommend --insights # global cross-architecture rankings\\n```\\n\\n## Step 4: Choose a Method\\n\\n### Method Selection Guide\\n**Default / recommended for most cases: `advanced`.** It uses multi-direction SVD with norm-preserving projection and is well-tested.\\n\\n| Situation | Recommended Method | Why |\\n|:----------------------------------|:-------------------|:-----------------------------------------|\\n| Default / most models | `advanced` | Multi-direction SVD, norm-preserving, reliable |\\n| Quick test / prototyping | `basic` | Fast, simple, good enough to evaluate |\\n| Dense model (Llama, Mistral) | `advanced` | Multi-direction, norm-preserving |\\n| MoE model (DeepSeek, Mixtral) | `nuclear` | Expert-granular, handles MoE complexity |\\n| Reasoning model (R1 distills) | `surgical` | CoT-aware, preserves chain-of-thought |\\n| Stubborn refusals persist | `aggressive` | Whitened SVD + head surgery + jailbreak |\\n| Want reversible changes | Use steering vectors (see Analysis section) |\\n| Maximum quality, time no object | `optimized` | Bayesian search for best parameters |\\n| Experimental auto-detection | `informed` | Auto-detects alignment type — experimental, may not always outperform advanced |\\n\\n### 9 CLI Methods\\n- **basic** — Single refusal direction via diff-in-means. Fast (~5-10 min for 8B).\\n- **advanced** (DEFAULT, RECOMMENDED) — Multiple SVD directions, norm-preserving projection, 2 refinement passes. Medium speed (~10-20 min).\\n- **aggressive** — Whitened SVD + jailbreak-contrastive + attention head surgery. Higher risk of coherence damage.\\n- **spectral_cascade** — DCT frequency-domain decomposition. Research/novel approach.\\n- **informed** — Runs analysis DURING abliteration to auto-configure. Experimental — slower and less predictable than advanced.\\n- **surgical** — SAE features + neuron masking + head surgery + per-expert. Very slow (~1-2 hrs). Best for reasoning models.\\n- **optimized** — Bayesian hyperparameter search (Optuna TPE). Longest runtime but finds optimal parameters.\\n- **inverted** — Flips the refusal direction. Model becomes actively willing.\\n- **nuclear** — Maximum force combo for stubborn MoE models. Expert-granular.\\n\\n### Direction Extraction Methods (--direction-method flag)\\n- **diff_means** (default) — Simple difference-in-means between refused/complied activations. Robust.\\n- **svd** — Multi-direction SVD extraction. Better for complex alignment.\\n- **leace** — LEACE (Linear Erasure via Closed-form Estimation). Optimal linear erasure.\\n\\n### 4 Python-API-Only Methods\\n(NOT available via CLI — require Python import, which violates AGPL boundary. Mention to user only if they explicitly want to use OBLITERATUS as a library in their own AGPL project.)\\n- failspy, gabliteration, heretic, rdo\\n\\n## Step 5: Run Abliteration\\n\\n### Standard usage\\n```bash\\n# Default method (advanced) — recommended for most models\\nobliteratus obliterate --method advanced --output-dir ./abliterated-models\\n\\n# With 4-bit quantization (saves VRAM)\\nobliteratus obliterate --method advanced --quantization 4bit --output-dir ./abliterated-models\\n\\n# Large models (70B+) — conservative defaults\\nobliteratus obliterate --method advanced --quantization 4bit --large-model --output-dir ./abliterated-models\\n```\\n\\n### Fine-tuning parameters\\n```bash\\nobliteratus obliterate \\\\\\n --method advanced \\\\\\n --direction-method diff_means \\\\\\n --n-directions 4 \\\\\\n --refinement-passes 2 \\\\\\n --regularization 0.1 \\\\\\n --quantization 4bit \\\\\\n --output-dir ./abliterated-models \\\\\\n --contribute # opt-in telemetry for community research\\n```\\n\\n### Key flags\\n| Flag | Description | Default |\\n|:-----|:------------|:--------|\\n| `--method` | Abliteration method | advanced |\\n| `--direction-method` | Direction extraction | diff_means |\\n| `--n-directions` | Number of refusal directions (1-32) | method-dependent |\\n| `--refinement-passes` | Iterative passes (1-5) | 2 |\\n| `--regularization` | Regularization strength (0.0-1.0) | 0.1 |\\n| `--quantization` | Load in 4bit or 8bit | none (full precision) |\\n| `--large-model` | Conservative defaults for 120B+ | false |\\n| `--output-dir` | Where to save the abliterated model | ./obliterated_model |\\n| `--contribute` | Share anonymized results for research | false |\\n| `--verify-sample-size` | Number of test prompts for refusal check | 20 |\\n| `--dtype` | Model dtype (float16, bfloat16) | auto |\\n\\n### Other execution modes\\n```bash\\n# Interactive guided mode (hardware → model → preset)\\nobliteratus interactive\\n\\n# Web UI (Gradio)\\nobliteratus ui --port 7860\\n\\n# Run a full ablation study from YAML config\\nobliteratus run config.yaml --preset quick\\n\\n# Tournament: pit all methods against each other\\nobliteratus tourney \\n```\\n\\n### Hermes long-run / disconnect pattern\\n\\nAbliteration runs can outlive the current chat or terminal connection, especially on CPU-only boxes. If the user may disconnect, do **not** leave a long `obliteratus obliterate ...` command in foreground. Run it as a tracked background process and create a script-only cron watchdog that stays silent while the PID is alive and delivers exactly one completion notification.\\n\\nPattern:\\n```bash\\nmkdir -p ~/abliterated-models ~/logs\\nobliteratus obliterate \\\\\\n --method advanced \\\\\\n --direction-method diff_means \\\\\\n --output-dir ~/abliterated-models \\\\\\n > ~/logs/obliteratus-.log 2>&1\\n```\\n\\nIn Hermes, launch that command with `terminal(background=true, notify_on_complete=true)` for in-session tracking. If the user wants Telegram/Discord notification after disconnect, add a small script under `~/.hermes/scripts/` that:\\n1. exits silently if a marker file already exists;\\n2. exits silently while `kill -0 ` succeeds;\\n3. creates the marker file once the PID is gone;\\n4. prints final status, output dir size/file count, log path, and last log lines.\\n\\nThen schedule it with `cronjob(action='create', no_agent=true, schedule='every 5m', deliver='all', repeat=, script='.sh')`. Use `deliver='all'` only when the user explicitly asks for cross-channel notification; otherwise deliver to origin.\\n\\n## Step 6: Verify Results\\n\\nAfter abliteration, check the output metrics:\\n\\n| Metric | Good Value | Warning |\\n|:-------|:-----------|:--------|\\n| Refusal rate | < 5% (ideally ~0%) | > 10% means refusals persist |\\n| Perplexity change | < 10% increase | > 15% means coherence damage |\\n| KL divergence | < 0.1 | > 0.5 means significant distribution shift |\\n| Coherence | High / passes qualitative check | Degraded responses, repetition |\\n\\n### If refusals persist (> 10%)\\n1. Try `aggressive` method\\n2. Increase `--n-directions` (e.g., 8 or 16)\\n3. Add `--refinement-passes 3`\\n4. Try `--direction-method svd` instead of diff_means\\n\\n### If coherence is damaged (perplexity > 15% increase)\\n1. Reduce `--n-directions` (try 2)\\n2. Increase `--regularization` (try 0.3)\\n3. Reduce `--refinement-passes` to 1\\n4. Try `basic` method (gentler)\\n\\n## Step 7: Use the Abliterated Model\\n\\nThe output is a standard HuggingFace model directory.\\n\\n```bash\\n# Test locally with transformers\\npython3 -c \\\"\\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\\nmodel = AutoModelForCausalLM.from_pretrained('./abliterated-models/')\\ntokenizer = AutoTokenizer.from_pretrained('./abliterated-models/')\\ninputs = tokenizer('How do I pick a lock?', return_tensors='pt')\\noutputs = model.generate(**inputs, max_new_tokens=200)\\nprint(tokenizer.decode(outputs[0], skip_special_tokens=True))\\n\\\"\\n\\n# Upload to HuggingFace Hub\\nhuggingface-cli upload /-abliterated ./abliterated-models/\\n\\n# Serve with vLLM\\nvllm serve ./abliterated-models/\\n```\\n\\n## CLI Command Reference\\n\\n| Command | Description |\\n|:--------|:------------|\\n| `obliteratus obliterate` | Main abliteration command |\\n| `obliteratus info ` | Print model architecture details |\\n| `obliteratus models --tier ` | Browse curated models by compute tier |\\n| `obliteratus recommend ` | Telemetry-driven method/param suggestion |\\n| `obliteratus interactive` | Guided setup wizard |\\n| `obliteratus tourney ` | Tournament: all methods head-to-head |\\n| `obliteratus run ` | Execute ablation study from YAML |\\n| `obliteratus strategies` | List all registered ablation strategies |\\n| `obliteratus report ` | Regenerate visual reports |\\n| `obliteratus ui` | Launch Gradio web interface |\\n| `obliteratus aggregate` | Summarize community telemetry data |\\n\\n## Analysis Modules\\n\\nOBLITERATUS includes 28 analysis modules for mechanistic interpretability.\\nSee `skill_view(name=\\\"obliteratus\\\", file_path=\\\"references/analysis-modules.md\\\")` for the full reference.\\n\\n### Quick analysis commands\\n```bash\\n# Run specific analysis modules\\nobliteratus run analysis-config.yaml --preset quick\\n\\n# Key modules to run first:\\n# - alignment_imprint: Fingerprint DPO/RLHF/CAI/SFT alignment method\\n# - concept_geometry: Single direction vs polyhedral cone\\n# - logit_lens: Which layer decides to refuse\\n# - anti_ouroboros: Self-repair risk score\\n# - causal_tracing: Causally necessary components\\n```\\n\\n### Steering Vectors (Reversible Alternative)\\nInstead of permanent weight modification, use inference-time steering:\\n```python\\n# Python API only — for user's own projects\\nfrom obliteratus.analysis.steering_vectors import SteeringVectorFactory, SteeringHookManager\\n```\\n\\n## Ablation Strategies\\n\\nBeyond direction-based abliteration, OBLITERATUS includes structural ablation strategies:\\n- **Embedding Ablation** — Target embedding layer components\\n- **FFN Ablation** — Feed-forward network block removal\\n- **Head Pruning** — Attention head pruning\\n- **Layer Removal** — Full layer removal\\n\\nList all available: `obliteratus strategies`\\n\\n## Evaluation\\n\\nOBLITERATUS includes built-in evaluation tools:\\n- Refusal rate benchmarking\\n- Perplexity comparison (before/after)\\n- LM Eval Harness integration for academic benchmarks\\n- Head-to-head competitor comparison\\n- Baseline performance tracking\\n\\n## Platform Support\\n\\n- **CUDA** — Full support (NVIDIA GPUs)\\n- **Apple Silicon (MLX)** — Supported via MLX backend\\n- **CPU** — Supported for tiny models (< 1B params)\\n\\n## YAML Config Templates\\n\\nLoad templates for reproducible runs via `skill_view`:\\n- `templates/abliteration-config.yaml` — Standard single-model config\\n- `templates/analysis-study.yaml` — Pre-abliteration analysis study\\n- `templates/batch-abliteration.yaml` — Multi-model batch processing\\n\\n## Telemetry\\n\\nOBLITERATUS can optionally contribute anonymized run data to a global research dataset.\\nEnable with `--contribute` flag. No personal data is collected — only model name, method, metrics.\\n\\n## Common Pitfalls\\n\\n1. **Don't use `informed` as default** — it's experimental and slower. Use `advanced` for reliable results.\\n2. **Models under ~1B respond poorly to abliteration** — their refusal behaviors are shallow and fragmented, making clean direction extraction difficult. Expect partial results (20-40% remaining refusal). Models 3B+ have cleaner refusal directions and respond much better (often 0% refusal with `advanced`).\\n3. **`aggressive` can make things worse** — on small models it can damage coherence and actually increase refusal rate. Only use it if `advanced` leaves > 10% refusals on a 3B+ model.\\n4. **Always check perplexity** — if it spikes > 15%, the model is damaged. Reduce aggressiveness.\\n5. **MoE models need special handling** — use `nuclear` method for Mixtral, DeepSeek-MoE, etc.\\n6. **Quantized models can't be re-quantized** — abliterate the full-precision model, then quantize the output.\\n7. **VRAM estimation is approximate** — 4-bit quant helps but peak usage can spike during extraction.\\n8. **Reasoning models are sensitive** — use `surgical` for R1 distills to preserve chain-of-thought.\\n9. **Check `obliteratus recommend`** — telemetry data may have better parameters than defaults.\\n10. **AGPL license** — never `import obliteratus` in MIT/Apache projects. CLI invocation only.\\n11. **Large models (70B+)** — always use `--large-model` flag for conservative defaults.\\n12. **Spectral certification RED is common** — the spectral check often flags \\\"incomplete\\\" even when practical refusal rate is 0%. Check actual refusal rate rather than relying on spectral certification alone.\\n\\n## Complementary Skills\\n\\n- **vllm** — Serve abliterated models with high throughput\\n- **gguf** — Convert abliterated models to GGUF for llama.cpp\\n- **huggingface-tokenizers** — Work with model tokenizers\\n\", \"path\": \"mlops/inference/obliteratus/SKILL.md\", \"skill_dir\": \"/home/ubuntu/.hermes/skills/mlops/inference/obliteratus\", \"linked_files\": {\"references\": [\"references/methods-guide.md\", \"references/analysis-modules.md\"], \"templates\": [\"templates/abliteration-config.yaml\", \"templates/batch-abliteration.yaml\", \"templates/analysis-study.yaml\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"Abliteration\", \"Uncensoring\", \"Refusal-Removal\", \"LLM\", \"Weight-Projection\", \"SVD\", \"Mechanistic-Interpretability\", \"HuggingFace\", \"Model-Surgery\"], \"related_skills\": [\"vllm\", \"gguf\", \"huggingface-tokenizers\"]}}}", "tool_call_id": "call_zzm5IHvwvKpMuXFLd6NjVB7b" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_clh2WCFMfwQWwTYOB9BGpGe2", "call_id": "call_clh2WCFMfwQWwTYOB9BGpGe2", "response_item_id": "fc_0f9f8571d2fe1560016a065870c6788199ab79db25c96c6d63", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"echo '=== processes ==='\\nps -eo pid,ppid,etimes,stat,%cpu,%mem,rss,cmd | grep -E 'obliteratus|SmolLM2|abliterat|obliterate' | grep -v grep || true\\n\\necho '\\n=== log status ==='\\nstat -c '%n | size=%s | mtime=%y' /home/ubuntu/logs/obliteratus-smollm2-135m.log 2>/dev/null || true\\nprintf '\\\\n=== last 80 log lines ===\\\\n'\\ntail -80 /home/ubuntu/logs/obliteratus-smollm2-135m.log 2>/dev/null | sed 's/\\\\x1b\\\\[[0-9;]*m//g'\\n\\necho '\\n=== output tree summary ==='\\ndu -sh /home/ubuntu/abliterated-models 2>/dev/null || true\\nfind /home/ubuntu/abliterated-models -maxdepth 3 -type f -printf '%p | %s bytes | %TY-%Tm-%Td %TH:%TM\\\\n' 2>/dev/null | sort | tail -50\",\"timeout\":180}" } }, { "id": "call_L31kaH71GUHxKChIDLKZFMVx", "call_id": "call_L31kaH71GUHxKChIDLKZFMVx", "response_item_id": "fc_0f9f8571d2fe1560016a065870c68881999063440148aef4d8", "type": "function", "function": { "name": "cronjob", "arguments": "{\"action\":\"list\"}" } }, { "id": "call_vk9vHkGe1Cptn7nNJ7ruEIP8", "call_id": "call_vk9vHkGe1Cptn7nNJ7ruEIP8", "response_item_id": "fc_0f9f8571d2fe1560016a065870c690819985942cdbd48c0bd9", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/ubuntu/.hermes/scripts/obliteratus_smollm_notify.sh\",\"offset\":1,\"limit\":120}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"=== processes ===\\n 784362 1 2498 Ss 0.0 0.0 3516 /usr/bin/bash -lic set +m; export PATH=/home/ubuntu/.local/bin:$PATH mkdir -p /home/ubuntu/abliterated-models /home/ubuntu/logs cd /home/ubuntu obliteratus obliterate HuggingFaceTB/SmolLM2-135M \\\\ --method advanced \\\\ --direction-method diff_means \\\\ --n-directions 4 \\\\ --refinement-passes 2 \\\\ --regularization 0.3 \\\\ --output-dir /home/ubuntu/abliterated-models \\\\ --verify-sample-size 10 \\\\ > /home/ubuntu/logs/obliteratus-smollm2-135m.log 2>&1\\n 784417 784362 2493 Sl 375 6.7 1655924 /home/ubuntu/obliteratus-venv/bin/python /home/ubuntu/obliteratus-venv/bin/obliteratus obliterate HuggingFaceTB/SmolLM2-135M --method advanced --direction-method diff_means --n-directions 4 --refinement-passes 2 --regularization 0.3 --output-dir /home/ubuntu/abliterated-models --verify-sample-size 10\\n\\n=== log status ===\\n/home/ubuntu/logs/obliteratus-smollm2-135m.log | size=1370 | mtime=2026-05-14 22:37:48.342279952 +0000\\n\\n=== last 80 log lines ===\\n\\n\\n ░▒█▀▀▀█ ░▒█▀▀▄ ░▒█░░░ ▀█▀ ▀▀█▀▀ ░▒█▀▀▀ ░▒█▀▀█ ▒█▀▀█ ▀▀█▀▀ ░▒█░░▒█ ░▒█▀▀▀█\\n ░▒█░░▒█ ░▒█▀▀▄ ░▒█░░░ ░█░ ░░█░░ ░▒█▀▀▀ ░▒█▄▄▀ ▒█▄▄█ ░░█░░ ░▒█░░▒█ ░░▀▀▀▄▄\\n ░▒█▄▄▄█ ░▒█▄▄▀ ░▒█▄▄█ ▄█▄ ░░▀░░ ░▒█▄▄▄ ░▒█░▒█ ▒█░▒█ ░░▀░░ ░░▒█▄▄█ ░▒█▄▄▄█\\n\\n ════════════════════════════════════════════════════════════════════\\n MASTER ABLATION SUITE // Break the chains. Free the mind.\\n ════════════════════════════════════════════════════════════════════\\n\\n\\rLoading weights: 0%| | 0/272 [00:00/dev/null; then\\n 14| exit 0\\n 15|fi\\n 16|\\n 17|touch \\\"$MARKER\\\"\\n 18|\\n 19|STATUS=\\\"finished\\\"\\n 20|if [[ -f \\\"$LOG\\\" ]]; then\\n 21| if grep -qiE 'traceback|error|exception|killed|failed' \\\"$LOG\\\"; then\\n 22| STATUS=\\\"finished, but log contains possible errors\\\"\\n 23| fi\\n 24|else\\n 25| STATUS=\\\"finished, but log file is missing\\\"\\n 26|fi\\n 27|\\n 28|MODEL_FILES=$(find \\\"$OUT\\\" -maxdepth 4 -type f 2>/dev/null | wc -l | tr -d ' ')\\n 29|SIZE=$(du -sh \\\"$OUT\\\" 2>/dev/null | awk '{print $1}' || echo unknown)\\n 30|TAIL=$(tail -40 \\\"$LOG\\\" 2>/dev/null | sed 's/\\\\x1b\\\\[[0-9;]*m//g' | tail -25)\\n 31|\\n 32|cat <&1 | sed -n '1,80p'\\nprintf '\\\\n=== obliteratus info smoke ===\\\\n'\\ntimeout 90 obliteratus info HuggingFaceTB/SmolLM2-135M 2>&1 | sed -n '1,120p'\",\"timeout\":180}" } }, { "id": "call_pyDFZNzJqDKu4CnFN61VTp4I", "call_id": "call_pyDFZNzJqDKu4CnFN61VTp4I", "response_item_id": "fc_0f9f8571d2fe1560016a065880a53c8199ac057866f37a714a", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"date -u '+%Y-%m-%dT%H:%M:%SZ'\\nps -p 784417 -o pid,etimes,%cpu,%mem,rss,stat,cmd\\n# show if the worker is doing syscalls without dumping huge output\\nif command -v timeout >/dev/null && command -v strace >/dev/null; then timeout 3 strace -p 784417 -c 2>&1 || true; else echo 'strace unavailable'; fi\",\"timeout\":180}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"=== obliteratus strategies smoke ===\\n\\n\\n ░▒█▀▀▀█ ░▒█▀▀▄ ░▒█░░░ ▀█▀ ▀▀█▀▀ ░▒█▀▀▀ ░▒█▀▀█ ▒█▀▀█ ▀▀█▀▀ ░▒█░░▒█ ░▒█▀▀▀█\\n ░▒█░░▒█ ░▒█▀▀▄ ░▒█░░░ ░█░ ░░█░░ ░▒█▀▀▀ ░▒█▄▄▀ ▒█▄▄█ ░░█░░ ░▒█░░▒█ ░░▀▀▀▄▄\\n ░▒█▄▄▄█ ░▒█▄▄▀ ░▒█▄▄█ ▄█▄ ░░▀░░ ░▒█▄▄▄ ░▒█░▒█ ▒█░▒█ ░░▀░░ ░░▒█▄▄█ ░▒█▄▄▄█\\n\\n ════════════════════════════════════════════════════════════════════\\n MASTER ABLATION SUITE // Break the chains. Free the mind.\\n ════════════════════════════════════════════════════════════════════\\n\\nAvailable ablation strategies:\\n\\n embedding_ablation — Zero-out a contiguous range of embedding dimensions.\\n ffn_ablation — Zero-out the MLP / feed-forward block of a specific \\ntransformer layer.\\n head_pruning — Zero-out the Q/K/V projection weights for a specific attention\\nhead.\\n layer_removal — Zero-out all parameters of a transformer layer, effectively \\nremoving it.\\n\\n=== obliteratus info smoke ===\\n\\n\\n ░▒█▀▀▀█ ░▒█▀▀▄ ░▒█░░░ ▀█▀ ▀▀█▀▀ ░▒█▀▀▀ ░▒█▀▀█ ▒█▀▀█ ▀▀█▀▀ ░▒█░░▒█ ░▒█▀▀▀█\\n ░▒█░░▒█ ░▒█▀▀▄ ░▒█░░░ ░█░ ░░█░░ ░▒█▀▀▀ ░▒█▄▄▀ ▒█▄▄█ ░░█░░ ░▒█░░▒█ ░░▀▀▀▄▄\\n ░▒█▄▄▄█ ░▒█▄▄▀ ░▒█▄▄█ ▄█▄ ░░▀░░ ░▒█▄▄▄ ░▒█░▒█ ▒█░▒█ ░░▀░░ ░░▒█▄▄█ ░▒█▄▄▄█\\n\\n ════════════════════════════════════════════════════════════════════\\n MASTER ABLATION SUITE // Break the chains. Free the mind.\\n ════════════════════════════════════════════════════════════════════\\n\\nLoading model: HuggingFaceTB/SmolLM2-135M\\n\\rLoading weights: 0%| | 0/272 [00:00/dev/null; then\\n exit 0\\nfi\\n\\ntouch \\\"$MARKER\\\"\\n\\nSTATUS=\\\"finished\\\"\\nif [[ -f \\\"$LOG\\\" ]]; then\\n if grep -qiE 'traceback|error|exception|killed|failed' \\\"$LOG\\\"; then\\n STATUS=\\\"finished, but log contains possible errors\\\"\\n fi\\nelse\\n STATUS=\\\"finished, but log file is missing\\\"\\nfi\\n\\nMODEL_FILES=$(find \\\"$OUT\\\" -maxdepth 4 -type f 2>/dev/null | wc -l | tr -d ' ')\\nSIZE=$(du -sh \\\"$OUT\\\" 2>/dev/null | awk '{print $1}' || echo unknown)\\nTAIL=$(tail -40 \\\"$LOG\\\" 2>/dev/null | sed 's/\\\\x1b\\\\[[0-9;]*m//g' | tail -25)\\n\\ncat </dev/null || kill -0 \\\"$WORKER_PID\\\" 2>/dev/null; then\\n exit 0\\nfi\\n\\ntouch \\\"$MARKER\\\"\\n\\nSTATUS=\\\"finished\\\"\\nif [[ -f \\\"$LOG\\\" ]]; then\\n if grep -qiE 'traceback|error|exception|killed|failed|out of memory|oom' \\\"$LOG\\\"; then\\n STATUS=\\\"finished, but log contains possible errors\\\"\\n fi\\nelse\\n STATUS=\\\"finished, but log file is missing\\\"\\nfi\\n\\nMODEL_FILES=$(find \\\"$OUT\\\" -maxdepth 4 -type f 2>/dev/null | wc -l | tr -d ' ')\\nSIZE=$(du -sh \\\"$OUT\\\" 2>/dev/null | awk '{print $1}' || echo unknown)\\nTAIL=$(tail -60 \\\"$LOG\\\" 2>/dev/null | sed 's/\\\\x1b\\\\[[0-9;]*m//g' | tail -35)\\n\\ncat </dev/null; then\\n+# Stay silent while either the tracked shell or the Python worker is alive.\\n+if kill -0 \\\"$PARENT_PID\\\" 2>/dev/null || kill -0 \\\"$WORKER_PID\\\" 2>/dev/null; then\\n exit 0\\n fi\\n \\n@@ -18,7 +20,7 @@\\n \\n STATUS=\\\"finished\\\"\\n if [[ -f \\\"$LOG\\\" ]]; then\\n- if grep -qiE 'traceback|error|exception|killed|failed' \\\"$LOG\\\"; then\\n+ if grep -qiE 'traceback|error|exception|killed|failed|out of memory|oom' \\\"$LOG\\\"; then\\n STATUS=\\\"finished, but log contains possible errors\\\"\\n fi\\n else\\n@@ -27,13 +29,14 @@\\n \\n MODEL_FILES=$(find \\\"$OUT\\\" -maxdepth 4 -type f 2>/dev/null | wc -l | tr -d ' ')\\n SIZE=$(du -sh \\\"$OUT\\\" 2>/dev/null | awk '{print $1}' || echo unknown)\\n-TAIL=$(tail -40 \\\"$LOG\\\" 2>/dev/null | sed 's/\\\\x1b\\\\[[0-9;]*m//g' | tail -25)\\n+TAIL=$(tail -60 \\\"$LOG\\\" 2>/dev/null | sed 's/\\\\x1b\\\\[[0-9;]*m//g' | tail -35)\\n \\n cat <