Navigate back to the previous page in browser history. Requires browser_navigate to be called first.

Send a raw Chrome DevTools Protocol (CDP) command. Escape hatch for browser operations not covered by browser_navigate, browser_click, browser_console, etc.

**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.

**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.

**Common patterns:**
- List tabs: method='Target.getTargets', params={}
- Handle a native JS dialog: method='Page.handleJavaScriptDialog', params={'accept': true, 'promptText': ''}, target_id=<tabId>
- Get all cookies: method='Network.getAllCookies', params={}
- Eval in a specific tab: method='Runtime.evaluate', params={'expression': '...', 'returnByValue': true}, target_id=<tabId>
- Set viewport for a tab: method='Emulation.setDeviceMetricsOverride', params={'width': 1280, 'height': 720, 'deviceScaleFactor': 1, 'mobile': false}, target_id=<tabId>

**Usage rules:**
- Browser-level methods (Target.*, Browser.*, Storage.*): omit target_id and frame_id.
- Page-level methods (Page.*, Runtime.*, DOM.*, Emulation.*, Network.* scoped to a tab): pass target_id from Target.getTargets.
- **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.
- 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.

CDP method name, e.g. 'Target.getTargets', 'Runtime.evaluate', 'Page.handleJavaScriptDialog'.

Method-specific parameters as a JSON object. Omit or pass {} for methods that take no parameters.

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.

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 in seconds (default 30, max 300).

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.

The element reference from the snapshot (e.g., '@e5', '@e12')

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.

If true, clear the message buffers after reading

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'

Respond to a native JavaScript dialog (alert / confirm / prompt / beforeunload) that is currently blocking the page.

**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'``.

**Prompt dialogs:** pass ``prompt_text`` to supply the response string. Ignored for alert/confirm/beforeunload.

**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.

**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).

'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.

Response string for a ``prompt()`` dialog. Ignored for other dialog types. Defaults to empty string.

Specific dialog to respond to, from ``browser_snapshot.pending_dialogs[].id``. Required only when multiple dialogs are queued.

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.

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.

The URL to navigate to (e.g., 'https://example.com')

Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.

Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')

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.

Direction to scroll

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.

If true, returns complete page content. If false (default), returns compact view with interactive elements only.

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.

The element reference from the snapshot (e.g., '@e3')

The text to type into the field

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:<screenshot_path> in your response. Requires browser_navigate to be called first.

What you want to know about the page visually. Be specific about what you're looking for.

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.

clarify

Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:

1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.
2. **Open-ended** — omit choices entirely. The user types a free-form response.

Use this tool when:
- The task is ambiguous and you need the user to choose an approach
- You want post-task feedback ('How did that work out?')
- You want to offer to save a skill or update memory
- A decision has meaningful trade-offs the user should weigh in on

Do 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.

The question to present to the user.

Choices:
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.

cronjob

Manage scheduled cron jobs with a single compressed tool.

Use action='create' to schedule a new job from a prompt or one or more skills.
Use action='list' to inspect jobs.
Use action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.

To 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.

Jobs run in a fresh session with no current-chat context, so prompts must be self-contained.
If skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.
On update, passing skills=[] clears attached skills.

NOTE: The agent's final response is auto-delivered to the target. Put the primary
user-facing content in the final response. Cron jobs run autonomously with no user
present — they cannot ask questions or request clarification.

Important safety rule: cron-run sessions should not recursively schedule more cron jobs.

One of: create, list, update, pause, resume, remove, run

Required for update/pause/resume/remove/run

For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills.

For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp

Optional human-friendly name

Optional repeat count. Omit for defaults (once for one-shot, forever for recurring).

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.

Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills.

Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.

Provider name (e.g. 'openrouter', 'anthropic', or 'custom:<name>' for a provider defined in custom_providers config — always include the ':<name>' suffix, never pass the bare 'custom'). Omit to use and pin the current provider.

Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')

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.

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.

REQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored).

DELIVERY 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.

WHEN 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.

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.

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.

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.

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.

TWO MODES (one of 'goal' or 'tasks' is required):
1. Single task: provide 'goal' (+ optional context, toolsets)
2. 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.

WHEN TO USE delegate_task:
- Reasoning-heavy subtasks (debugging, code review, research synthesis)
- Tasks that would flood your context with intermediate data
- Parallel independent workstreams (research A and B simultaneously)

WHEN NOT TO USE (use these instead):
- Mechanical multi-step work with no reasoning needed -> use execute_code
- Single tool call -> just call the tool directly
- Tasks needing user interaction -> subagents cannot use clarify
- 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.

IMPORTANT:
- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.
- 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.
- 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.
- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.
- 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.
- Each subagent gets its own terminal session (separate working directory and state).
- Results are always returned as an array, one entry per task.

What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history.

Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs.

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.

Task goal

Task-specific context

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.

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.

Per-task ACP args override. Leave empty unless acp_command is set.

Per-task role override. See top-level 'role' for semantics.

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 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.

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).

Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided.

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).

Use 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.

Available via `from hermes_tools import ...`:

  web_search(query: str, limit: int = 5) -> dict
    Returns {"data": {"web": [{"url", "title", "description"}, ...]}}
  web_extract(urls: list[str]) -> dict
    Returns {"results": [{"url", "title", "content", "error"}, ...]} where content is markdown
  read_file(path: str, offset: int = 1, limit: int = 500) -> dict
    Lines are 1-indexed. Returns {"content": "...", "total_lines": N}
  write_file(path: str, content: str) -> dict
    Always overwrites the entire file.
  search_files(pattern: str, target="content", path=".", file_glob=None, limit=50) -> dict
    target: "content" (search inside files) or "files" (find files by name). Returns {"matches": [...]}
  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict
    Replaces old_string with new_string in the file.
  terminal(command: str, timeout=None, workdir=None) -> dict
    Foreground only (no background/pty). Returns {"output": "...", "exit_code": N}

Limits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).

Scripts 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().

Print your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.

Also available (no import needed — built into hermes_tools):
  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars
  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands
  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures

Python code to execute. Import tools with `from hermes_tools import web_search, terminal, ...` and print your final result to stdout.

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.

The text prompt describing the desired image. Be detailed and descriptive.

The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.

Retrieves and queries up-to-date documentation and code examples from Context7 for any programming library or framework.

You 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.

Do not call this tool more than 3 times per question.

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'.

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.

Resolves a package/product name to a Context7-compatible library ID and returns matching libraries.

You 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.

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

Selection Process:
1. Analyze the query to understand what library/package the user is looking for
2. Return the most relevant match based on:
- Name similarity to the query (exact matches prioritized)
- Description relevance to the query's intent
- Documentation coverage (prioritize libraries with higher Code Snippet counts)
- Source reputation (consider libraries with High or Medium reputation more authoritative)
- Benchmark Score: Quality indicator (100 is the highest score)

Response Format:
- Return the selected library ID in a clearly marked section
- Provide a brief explanation for why this library was chosen
- If multiple good matches exist, acknowledge this but proceed with the most relevant one
- If no good matches exist, clearly state this and suggest query refinements

For ambiguous queries, request clarification before proceeding with a best-guess match.

IMPORTANT: 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.

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.

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'.

memory

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.

WHEN TO SAVE (do this proactively, don't wait to be asked):
- User corrects you or says 'remember this' / 'don't do that again'
- User shares a preference, habit, or personal detail (name, role, timezone, coding style)
- You discover something about the environment (OS, installed tools, project structure)
- You learn a convention, API quirk, or workflow specific to this user's setup
- You identify a stable fact that will be useful again in future sessions

PRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.

Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.
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.

TWO TARGETS:
- 'user': who the user is -- name, role, preferences, communication style, pet peeves
- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned

ACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).

SKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.

The action to perform.

Which memory store: 'memory' for personal notes, 'user' for user profile.

The entry content. Required for 'add' and 'replace'.

Short unique substring identifying the entry to replace or remove.

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.

The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning.

patch

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.

REPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.
PATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.

Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.

REQUIRED when mode='replace'. File path to edit.

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.

REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text.

Replace all occurrences instead of requiring a unique match (default: false)

REQUIRED when mode='patch'. V4A format patch content. Format:
*** Begin Patch
*** Update File: path/to/file
@@ context hint @@
 context line
-removed line
+added line
*** End Patch

process

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).

list poll log wait kill write submit close

Action to perform on background processes

Process session ID (from terminal background output). Required for all actions except 'list'.

Text to send to process stdin (for 'write' and 'submit' actions)

Max seconds to block for 'wait' action. Returns partial output on timeout.

Line offset for 'log' action (default: last 200 lines)

Max lines to return for 'log' action

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.

Path to the file to read (absolute, relative, or ~/path)

Line number to start reading from (1-indexed, default: 1)

Maximum number of lines to read (default: 500, max: 2000)

Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.

Content search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.

File search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.

Regex pattern for content search, or glob pattern (e.g., '*.py') for file search

'content' searches inside file contents, 'files' searches for files by name

Directory or file to search in (default: current working directory)

Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)

Maximum number of results to return (default: 50)

Skip first N results for pagination (default: 0)

Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file

Number of context lines before and after each match (grep mode only)

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.

TWO MODES:
1. 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.
2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.

USE THIS PROACTIVELY when:
- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'
- The user asks about a topic you worked on before but don't have in current context
- The user references a project, person, or concept that seems familiar but isn't in memory
- You want to check if you've solved a similar problem before
- The user asks 'what did we do about X?' or 'how did we fix Y?'

Don'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.

Search 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.

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).

Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.

Max sessions to summarize (default: 3, max: 5).

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.

Actions: 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.

On delete, pass `absorbed_into=<umbrella>` 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.

Create when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.
Update 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.

After difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.

Good skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.

Pinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin <name>`. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.

create patch edit delete write_file remove_file

The action to perform.

Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file.

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.

Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'patch'). Can be empty string to delete the matched text.

For 'patch': replace all occurrences instead of requiring a unique match (default: false).

Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'.

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.

Content for the file. Required for 'write_file'.

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.

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.

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').

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.

List available skills (name + description). Use skill_view(name) to load full content.

Optional category filter to narrow results

terminal

Execute shell commands on a Linux environment. Filesystem usually persists between calls.

Do NOT use cat/head/tail to read files — use read_file instead.
Do NOT use grep/rg/find to search — use search_files instead.
Do NOT use ls to list directories — use search_files(target='files') instead.
Do NOT use sed/awk to edit files — use patch instead.
Do NOT use echo/cat heredoc to create files — use write_file instead.
Reserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.

Foreground (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.
Background: Set background=true to get a session_id. Two patterns:
  (1) Long-lived processes that never exit (servers, watchers).
  (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.
For 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.
After starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.
Use process(action="poll") for progress checks, process(action="wait") to block until done.
Working directory: Use 'workdir' for per-command cwd.
PTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).

Do NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.

The command to execute on the VM

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.

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.

Working directory for this command (absolute path). Defaults to the session working directory.

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.

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.

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.

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.<name>), not model-selected.

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.

Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3

todo

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.

Writing:
- Provide 'todos' array to create/update items
- merge=false (default): replace the entire list with a fresh plan
- merge=true: update existing items by id, add any new ones

Each item: {id: string, content: string, status: pending|in_progress|completed|cancelled}
List order is priority. Only ONE item in_progress at a time.
Mark items completed immediately when done. If something fails, cancel it and add a revised item.

Always returns the full current list.

Task items to write. Omit to read current list.

Unique item identifier

Task description

Current status

true: update existing items by id, add new ones. false (default): replace the entire list.

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.

Image URL (http/https), local file path, or data: URL to load.

Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image.

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.

List of URLs to extract content from (max 5 URLs per call)

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.

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".

Maximum number of results to return. Defaults to 5.

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).

Path to the file to write (will be created if it doesn't exist, overwritten if it does)

Complete content to write to the file

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.

Peer to query. Built-in aliases: 'user' (default), 'ai'. Or pass any peer ID from this workspace.

New peer card as a list of fact strings. Omit to read the current card.

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.

What to search for in Honcho's memory.

Token budget for returned context (default 800, max 2000).

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.

A natural language question.

Override the default reasoning depth. Omit to use the configured default (typically low). Guide:
- minimal: quick factual lookups (name, role, simple preference)
- low: straightforward questions with clear answers
- medium: multi-aspect questions requiring synthesis across observations
- high: complex behavioral patterns, contradictions, deep analysis
- max: thorough audit-level analysis, leave no stone unturned

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.

Optional focus query to filter context. Omit for full session context snapshot.

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.

A factual statement to persist. Provide this when creating a conclusion. Do not send it together with delete_id.

Conclusion ID to delete for PII removal. Provide this when deleting a conclusion. Do not send it together with conclusion.


[The user sent an image~ Here's what I can see:
Here's a detailed description of everything visible in the image:

**General Layout and Interface:**

*   The image appears to be a screenshot of a mobile email application, likely Gmail, on a smartphone.
*   The overall theme is dark mode.
*   The screen displays a list of emails, with a search bar at the top.
*   At the bottom, there are icons for composing a new email and starting a video call.

**Top Section:**

*   **Time:** "12:55" is displayed in the top left corner, indicating the time.
*   **Status Bar:** The top right corner shows typical status bar icons: signal strength, Wi-Fi, and battery level (62%).
*   **Search Bar:** A dark gray search bar is present with the word "partera" typed in it. There's a star icon to the right of the search term, and an "X" icon to clear the search.
*   **Filter Buttons:** Below the search bar are filter buttons: "Labels," "From," "To," and "Attachment." Each has a downward-pointing arrow, indicating a dropdown menu.

**Email List:**

The email list is sorted by "Most relevant" (dropdown menu visible). The list contains four email entries:

1.  **Maria Florencia Colombo:**
    *   Sender: "Maria Florencia Colombo"
    *   Subject: "RECOLECCION DE CELULAS MADRE E..."
    *   Snippet: "Mi nombre es María Florencia Colo..."
    *   Date: "Apr 23"
    *   Attachments: "image001" and "TARJETAS P..." (+2 more)
    *   Inbox label
    *   A star icon indicating it's a favorite.
    *   A green dot indicating the email is new.
    *   A gray circle with a person icon inside.
2.  **christinakorf:**
    *   Sender: "christinakorf"
    *   Subject: "call Cristina (Partera) @ Mon Apr 6, 20..."
    *   Snippet: "You have been invited to attend an..."
    *   Date: "Apr 3"
    *   Inbox label
    *   A star icon indicating it's a favorite.
    *   A green dot indicating the email is new.
    *   A green circle with the letter "C" inside.
    *   A calendar icon.
3.  **Christina Korf:**
    *   Sender: "Christina Korf"
    *   Subject: "Fwd: RECOLECCION DE CELULAS MA..."
    *   Snippet: "Forwarded message --..."
    *   Date: "Apr 10"
    *   Attachments: "image001" and "TARJETAS P..." (+2 more)
    *   Inbox label
    *   A star icon indicating it's a favorite.
    *   A green dot indicating the email is new.
    *   A green circle with the letter "C" inside.
4.  **Google Calendar:**
    *   Sender: "Google Calendar"
    *   Subject: "Daily Agenda for Ignacio Lagos Ruiz a..."
    *   Snippet: "18:00 – 19:00, 18:00 call Cristina (...)"
    *   Date: "Apr 6"
    *   Inbox label
    *   A star icon indicating it's a favorite.
    *   A green dot indicating the email is new.
    *   A yellow circle with the letter "G" inside.

**Bottom Section:**

*   **Compose Button:** A green circle with a white envelope icon and "99+" in a red badge, indicating a large number of unread emails.
*   **Video Call Button:** A white video camera icon.
*   **Bottom Bar:** A thin white line at the very bottom, likely part of the phone's navigation.

**Colors:**

*   Dominant colors are dark gray/black (background), white (text), and green (accent color for inbox labels and some sender icons).
*   Red is used for the unread email count badge.
*   Yellow is used for the Google Calendar icon.

**Overall Impression:**

The image shows a typical email inbox view, with a focus on emails related to the search term "partera." The user seems to have a large number of unread emails. The interface is clean and modern, with a dark theme.]
[If you need a closer look, use vision_analyze with image_url: /home/ubuntu/.hermes/image_cache/img_c0f28fbfec9c.png ~]

[The user sent an image~ Here's what I can see:
Here's a detailed description of the image:

**Overall Impression:**

The image shows a printed document, likely a schedule or syllabus for childbirth preparation classes. The document is divided into two sections: "Clases Teóricas" (Theoretical Classes) and "Clases Prácticas" (Practical Classes). There are handwritten annotations on the document.

**Layout and Structure:**

*   The document is printed on a light-colored paper with a subtle floral pattern in the upper left corner.
*   The text is primarily in Spanish.
*   Each section (Theoretical and Practical) is organized as a numbered list of classes.
*   Each class entry includes a number, a title, a brief description, and the name of the instructor or responsible party.
*   The "Clases Teóricas" section includes a "fecha" (date) field for each class, which is enclosed in a box.

**Text and Content:**

*   **Title:** "• CLASES TEÓRICAS" (Theoretical Classes) and "• CLASES PRÁCTICAS" (Practical Classes)
*   **Instructor (Practical Classes):** "Profesora: M. Laura Freile"
*   **Theoretical Classes:**
    *   **1:** "Motivos de Consulta" (Reasons for Consultation), "Contracciones - Pre-parto" (Contractions - Pre-labor), "Señales de alarma" (Alarm signals), "Lic. Cristina Rodríguez y Equipo"
    *   **2:** "Trabajo de Parto" (Labor), "Parto y Post-parto inmediato" (Labor and immediate Postpartum), "Lic. Cristina Rodríguez y Equipo"
    *   **3:** "Pediatría" (Pediatrics), "Recepción al nacer, Adapt. a la vida extrauterina, rol del acompañante, primeras interv., estudios y controles, recomendaciones en cuidados del RN" (Reception at birth, Adapt. to extrauterine life, role of the companion, first interventions, studies and controls, recommendations in RN care), "Dr. Santiago López García"
    *   **4:** "Taller de Lactancia" (Breastfeeding Workshop), "Beneficios de una buena lactancia, preparación, cuidados, técnicas de amamantamiento" (Benefits of good breastfeeding, preparation, care, breastfeeding techniques), "Lic.Alicia Mollo"
    *   **5:** "Analgesia y anestesia" (Analgesia and anesthesia), "Espacio para consultas e inquietudes grales." (Space for consultations and general concerns), "Lic. Cristina Rodríguez y Equipo"
*   **Practical Classes:**
    *   **1:** "Respiración y Pujo" (Breathing and Pushing)
    *   **2:** "Diferentes posturas para facilitar el libre movimiento de la pelvis" (Different postures to facilitate free movement of the pelvis)
    *   **3:** "Ejercicios para relajar y aliviar molestias ocasionadas por los cambios del embarazo" (Exercises to relax and relieve discomfort caused by pregnancy changes)
    *   **4:** "CLASE PRÁCTICA SOLAS SIN ACOMPAÑANTE:" (PRACTICAL CLASS ALONE WITHOUT COMPANION), "Ejercicios para la pelvis y el periné" (Exercises for the pelvis and perineum)
    *   **5:** "Ejercicios con elementos y relajación" (Exercises with elements and relaxation)

**Visual Elements:**

*   **Numbers:** Each class is numbered with a bold, black number inside a white circle.
*   **Image:** A silhouette of a pregnant woman is visible in the lower right corner, likely associated with the practical classes.
*   **Handwritten Annotations:**
    *   Dates are written in the "fecha" boxes for the theoretical classes.
    *   Times are written next to the dates, with some classes marked with an asterisk.
    *   The text "tods 20 30" is written at the top of the practical classes section.
    *   The numbers "1830" and "1941" are written next to the dates.

**Colors:**

*   The document is primarily black and white, with the exception of the light-colored paper.
*   The handwritten annotations are in blue ink.

**Overall, the image presents a schedule for childbirth preparation classes, with a clear distinction between theoretical and practical sessions. The handwritten annotations suggest that someone has marked the dates and times of the classes they plan to attend.**]
[If you need a closer look, use vision_analyze with image_url: /home/ubuntu/.hermes/image_cache/img_c9b725b55544.jpeg ~]

[The user sent an image~ Here's what I can see:
Here is a detailed description of the image:

**Overall Impression:**

The image shows a close-up shot of a printed document, likely a guide or checklist for pregnant women preparing for hospitalization. The document is in Spanish. The paper is white, and the text is primarily black, with the title in a larger, orange font.

**Text and Layout:**

*   **Title:** The title "GUIA PARA LA EMBARAZADA" (Guide for the Pregnant Woman) is prominently displayed at the top in a large, orange font.
*   **Subheadings:** The document is divided into sections with subheadings:
    *   "Documentación para la internación" (Documentation for hospitalization)
    *   "¿Qué poner en el bolso?" (What to put in the bag?)
    *   "Para la mamá:" (For the mother:)
    *   "Para acompañante:" (For the companion:)
    *   "Para el bebe:" (For the baby:)
*   **Checklist Items:** Each section contains a list of items or instructions, preceded by a green checkmark symbol.
*   **Specific Items/Instructions:**
    *   Documentation: Lists required documents like ID, insurance card, authorization for hospitalization, etc.
    *   What to pack for the mother: Includes items like nursing gowns, robe, bras, slippers, underwear, and a note that basic hygiene items are provided by the hospital.
    *   What to pack for the companion: Suggests pajamas or comfortable clothes for overnight stays.
    *   What to pack for the baby: Mentions that babies are dressed in hospital clothes during the stay and only need clothes for the discharge, appropriate for the season. It also lists cotton outfits (batita, onesie with closed feet, socks, and hat) and a small wool or yarn sack.
*   **Disclaimer:** At the bottom, there's a disclaimer stating that diapers, pads, or other hygiene items for the baby should not be brought.

**Colors:**

*   **White:** The background color of the paper.
*   **Black:** The primary color of the text.
*   **Orange:** Used for the title.
*   **Green:** Used for the checkmark symbols.

**Other Visual Details:**

*   The paper appears to be slightly wrinkled or creased.
*   The image is well-lit, but there are some shadows.
*   The focus is sharp, making the text easily readable.
*   The edges of the paper are visible, suggesting it's a physical document.

**Overall, the image presents a practical guide for expectant mothers, providing a clear and organized list of essential documents and items to prepare for their hospital stay.**]
[If you need a closer look, use vision_analyze with image_url: /home/ubuntu/.hermes/image_cache/img_7f6e7fcf3cd1.jpeg ~]

[nachlakes] Hola  cree este chat para que podamos hablar de temas de la bebe, la paternidad y maternidad, y que me ayudes a tener consistencia respecto de mi familia, que no se sientan dejados de lado etc. Hoy es domingo 17/05/2026, fecha probable de parto el 26/06/2026, busca en tu memoria al respecto, tanto en hermes como en chatgpt si es posible. Vamos a tener a la bebé en la clínica de los arcos en Juan b justo, con el Dr Senyk. Ahora mismo hoy día debo de tener lo siguiente listo :
I need you to: Have all documents handy (for thehospital), research pediatrician, pack the things you would like to take to the hospital, pick up the car and place the car seat already

Deadline: 17th of May
Pediatrician
Appointment EKG
Contacting dra Cecilia (pediadra hospital) regarding piel sobre piel
Research trash can Pañales
Research baby camera
For documents see this list attached and check my Gmail inbox as well to see what you find. Bueno pipo empecemos. Te parece mejor si te usamos a vos con chatgpt de ppal y deepseek de delegado para todo el trabajo pesado? Gracias capo


Chicho, perfecto. Vamos con todo. Déjame organizar y empezar en paralelo.

Primero, sobre modelos: me parece bien el setup que proponés — yo coordino, y delego research pesado a subagentes. Ya estoy en eso.

Dame unos segundos que arranco con todo esto.

Explanation:
The user (Ignacio/Chicho/nachlakes) has created a new Discord thread/channel for preparing for his baby's arrival. He's asking me to:

1. Help organize everything related to the baby, parenthood, family consistency
2. Today's deadline (May 17, 2026):
   - Have all documents ready for the hospital
   - Research pediatrician
   - Pack things for the hospital
   - Pick up car and place car seat

3. Specific tasks:
   - Pediatrician research
   - EKG appointment
   - Contact Dr. Cecilia (hospital pediatrician) about skin-to-skin
   - Research trash can for diapers
   - Research baby camera
   - Check documents from the images and his Gmail inbox

4. He's asking about model strategy: using me with ChatGPT as primary and DeepSeek as delegate for heavy lifting.

First, let me look at the images more carefully. He sent 3 images:
1. Gmail inbox searching for "partera" - shows emails from Maria Florencia Colombo (stem cell collection), Christina Korf (midwife calls), Google Calendar
2. A birth preparation class schedule - theoretical and practical classes by Lic. Cristina Rodriguez and team, M. Laura Freile for practical
3. Hospital guide "GUIA PARA LA EMBARAZADA" - documentation, what to pack

Let me address his points:

1. First, regarding model strategy - I'm currently running on deepseek-v4-pro. I think his suggestion makes sense: use me as the primary coordinator/thinker, and delegate heavy research tasks to subagents.

2. I need to:
   - Search his Gmail for relevant documents
   - Research pediatricians
   - Research diaper trash cans (pañaleras)
   - Research baby cameras
   - Check his memory for any related info

Let me start by loading the relevant skills and searching memory/sessions for any baby-related context.

I should load the gog skill for Gmail access, and use web_search for research tasks.

Let me be efficient and parallelize what I can.

Question: bebé OR baby OR partera OR pediatra OR arcos OR Senyk OR embarazo OR FPP OR junio 2026

Question: bebé embarazo partera pediatra arcos Senyk clínica


Question: bebé OR baby OR partera OR pediatra OR arcos OR Senyk OR embarazo OR FPP OR junio 2026

Summary:
# Conversation Summary

**Search focus requested:** bebé OR baby OR partera OR pediatra OR arcos OR Senyk OR embarazo OR FPP OR junio 2026

**Result: None of these terms appear anywhere in the conversation transcript.**

## What Actually Happened

The conversation was entirely about **Banco Patagonia's web onboarding application** — a digital banking customer acquisition flow. The user (or a prior session) was debugging/analyzing the bank's React-based landing page, specifically investigating:

1. **Marketing campaign content**: Terms like "beneficio" (found 84 times), "descuento", "SportClub", "bonificación", "LKL" appeared throughout the minified JavaScript bundles. The page promoted "Tarjetas de crédito Black", "Caja de ahorro en pesos y dólares", "100% bonificado por 6 meses", and "Hasta $75.000 de bienvenida."

2. **Banner/advertising infrastructure**: The code revealed a dynamic banner system (`benefitBanners`, `heroBanners`) that fetches base64-encoded images from an API keyed by campaign parameters (`o=8072`, `c=6706`). The `benefitBannerDescription` array stores alt-text descriptions for carousel images.

3. **Screenshot capture**: Using headless Chromium (profile `openclaw`, port 9222), the agent loaded a Facebook UTM-tagged URL (`utm_source=facebook&utm_medium=cpc&utm_campaign=AR%2F29-55%2F%20Intereses%3A%20LKL%202%25%20y%205%25%20Alta%20renta&utm_content=Imagen%20-%20Beneficios%20SportClub%20-%20Copy%20v1%20-%20ABR26`) and captured a 1400×1600 PNG screenshot saved to `/home/ubuntu/bp_sportclub.png` (663 KB).

4. **Technical environment**: Node.js v25.6.1, Chromium via snap, no Playwright/Selenium installed system-wide, Python 3.12 without PIL/OpenCV/pytesseract. D-Bus access errors and GCM registration failures appeared in Chromium logs but didn't block the page capture.

## Key Technical Details
- **URL analyzed**: `https://www.bancopatagonia.com.ar/...` with Facebook Ads UTM parameters for a SportClub benefit campaign targeting "LKL 2% y 5% Alta renta" audience
- **Campaign parameters**: `c=6706` (campaign channel), `o=8072` (official channel)
- **Screenshot file**: `/home/ubuntu/bp_sportclub.png` (1400×1600, RGB PNG)
- **Browser profile used**: `openclaw` (headless Chromium on port 9222)

## Unresolved
- Vision API quota was exhausted (Gemini free tier limit hit), preventing AI-powered screenshot analysis
- No OCR tools were available to extract text from the screenshot
- The pregnancy/baby search terms (**bebé, baby, partera, pediatra, arcos, Senyk, embarazo, FPP, junio 2026**) were **completely absent** from this conversation — they appear to belong to a different session or topic that was not part of this transcript

Summary:
The conversation transcript provided does not contain any information regarding the search topics (**bebé, baby, partera, pediatra, arcos, Senyk, embarazo, FPP, or junio 2026**).

Instead, the session focused entirely on technical configurations for an AI agent environment. Below is a summary of the actual events:

### 1. User Goals and Requests
*   The user (**Igna/Chicho**) wanted to optimize his AI model selection and integrate development tools (**GitHub, Vercel, and Supabase**) into his agent's workflow.
*   He specifically requested the installation of **Vercel** and **Supabase** skills/MCPs.

### 2. Actions Taken and Outcomes
*   **Model Switching:** The user switched the active model to `openai-codex/gpt-5.4`. The assistant explained that **Smart Model Routing** was enabled, which automatically uses cheaper models (like Qwen) for simple queries and premium models for complex tasks.
*   **GitHub Auth:** The assistant verified GitHub authentication for the user `ignaciolagosruiz`, confirming active tokens and scopes (`repo`, `workflow`, etc.).
*   **Vercel Integration:**
    *   The assistant installed the **Vercel CLI** via npm.
    *   The user provided a Vercel Personal Access Token (`vcp_...`).
    *   The assistant successfully logged in and listed the user's projects (`myristica`, `bibliothek`).
*   **Supabase Integration:**
    *   The assistant attempted to install the **Supabase CLI**.
    *   After failures with `npm` and `brew` (due to OS compatibility issues on Linux ARM64), the assistant initiated an installation via `go install github.com/supabase/cli@latest`.

### 3. Key Decisions and Solutions
*   **Vercel Strategy:** The assistant recommended using the **CLI + Skill** approach over the **Native MCP** because the CLI provides broader power (deployments, env vars, DNS) compared to the limited read-only capabilities of the current Vercel MCP beta.
*   **Model Awareness:** Acknowledged that Google Gemini 2.0 models would be deprecated on **June 1, 2026**, advising a move to the 3.1 series.

### 4. Technical Details
*   **Vercel Token:** `vcp_6uq7DWspuyOe1ZVbQkGzvPSnkVPB2fu6CCMWk3RqfNNLnSsRvN46Pi7p` (configured for `ignaciolagosruiz`).
*   **Environment:** Linux ARM64 (Ubuntu).
*   **Config File:** `~/.hermes/config.yaml` was inspected for toolset and model routing settings.

### 5. Unresolved Items
*   The **Supabase** installation was still in progress (downloading dependencies via Go) when the transcript ended.
*   The assistant noted that the **Vercel MCP** (Option A) was skipped in favor of the CLI but could be added later if needed for specific IDE integrations.

Summary:
This conversation took place on **May 15, 2026**, between the user (Ignacio/Chicho) and an AI assistant (Hermes/Pipo). While the primary technical task was researching health app integrations, the conversation was grounded in the context of the user's personal life and fitness planning.

### 1. User Goals and Context
The user sought to research and plan the integration of various health and nutrition apps (MacroFactor, MyFitnessPal, Fitbit, Google Health Connect, etc.) with his AI agent to create a "Very Personal Trainer" capable of adjusting his training and nutrition automatically.

A critical piece of context provided was that the user's wife (**Christina/Tina**) is **pregnant**, with a **Probable Due Date (FPP) of June 26, 2026**.

### 2. Actions Taken and Outcomes
*   **Context Loading:** The assistant loaded the user's training plan from `~/.hermes/references/minmax-training-plan.md`.
*   **Delegated Research:** The assistant used a delegation tool to perform a technical deep-dive into the APIs of seven different health platforms.
*   **Technical Report:** A comprehensive report was generated detailing API types, authentication methods (OAuth2, Firebase, etc.), data accessibility, and Python code skeletons for each app.

### 3. Key Decisions and Conclusions
*   **Training Alignment with FPP:** The user’s training program (Jeff Nippard Min-Max) was specifically scheduled so that **Week 7 (a deload/lighter week)** coincides with the **due date week (starting June 22, 2026)**. This was noted as "perfect" for the arrival of the baby.
*   **Integration Priority:** The assistant recommended a priority stack of **Hevy + Fitbit + MacroFactor** to manage the user's fitness during the transition to fatherhood.
*   **Flexibility Rule:** A specific rule was established for the final two weeks before the due date: the schedule will become flexible, with the user simply performing the next workout in order as time permits.

### 4. Technical Details
*   **FPP:** June 26, 2026.
*   **Hevy Folder ID:** 2826578 (contains the "Jeff Nippard Min-Max 4x — Start 2026-05" routines).
*   **Integration Targets:**
    *   **MacroFactor:** Reverse-engineered via Firestore (`github.com/sjawhar/macrofactor`).
    *   **Fitbit:** Web API OAuth2 (recommended for sleep/activity tracking).
    *   **Health Connect:** Android on-device API (requires a bridge app).

### 5. Unresolved or Notable Items
*   **DeepSeek Delegation:** The user noted a switch in the underlying model from DeepSeek-V4-Pro to GPT-5.5. The assistant noted that while the task was delegated, the tool output identified as GPT-5.5, leaving the specific delegation configuration for future review.
*   **Health Connect Bridge:** It was noted that a "companion app" or bridge would need to be built to get data from the user's Pixel/Android device to the cloud-based Hermes agent.

Summary:
No se mencionaron en esta conversación los términos **bebé**, **baby**, **partera**, **pediatra**, **arcos**, **Senyk**, **embarazo**, **FPP** ni **junio 2026**. La conversación trató únicamente sobre:

- **Propósito del canal de Discord**: nachlakes preguntó si recordaba para qué se creó el canal. El asistente respondió que era el canal **#hevy**, dedicado al tracking de workouts con la app Hevy.
- **Conexión a Hevy**: nachlakes pidió que se conectara a Hevy si encontraba la API key. El asistente revisó el skill `hevy` instalado, buscó la variable `HEVY_API_KEY` en archivos `.env` y configuraciones de `~/.hermes/`, `~/.openclaw/`, `hermes-workspace/`, `hermes-webui/`, sin encontrarla.
- **Bitwarden bloqueado**: intentó obtener la clave desde Bitwarden pero el vault estaba bloqueado (`status: locked`). La sesión guardada en `~/.bw/session` había expirado. No se pudo desbloquear ni recuperar la API key de Hevy.
- **Resultado**: la API key de Hevy no estaba configurada en ninguna variable de entorno ni archivo `.env` visible, y no se pudo acceder a Bitwarden para recuperarla. Quedó pendiente.

No hubo ninguna discusión sobre embarazo, fecha de parto, pediatras, parteras, ni nada relacionado con un bebé esperado para junio de 2026.

Summary:
The conversation was entirely about tracking historical building expenses (expensas) and electricity bills for the property at Céspedes 2491, unit 07-A. No references to bebé, baby, partera, pediatra, arcos, Senyk, embarazo, FPP, or junio 2026 appeared at any point.

**Summary of what occurred:**
- The user asked the assistant to continue with Edenor analysis and show findings before moving on to Metrogas.
- The assistant searched Gmail for Edenor notifications, retrieved a long list of emails (factura disponible, vencimiento, pago exitoso) dating back to July 2024.
- The assistant processed payment confirmations to extract amounts, including:
  - Feb 2026: $67,268.46
  - Jan 2026: $77,709.56
  - Dec 2025: $24,017.76 (likely Dec 2024, from earlier)
  - Other payments ranged from ~$9,000 to ~$77,000
- No further analysis or Metrogas step was completed in this transcript segment; the assistant was still gathering and sorting Edenor data.

**Key details preserved:**
- Edenor account: 6489587070, address CESPEDES 2491 7 A, Ciudad Autonoma de Bs As.
- Gmail label "CATEGORY_UPDATES" / "INBOX" used to retrieve Edenor emails.
- Extracted payment amounts for specific dates.

**Search topic outcome:** The conversation transcript contains none of the keywords (bebé, baby, partera, pediatra, arcos, Senyk, embarazo, FPP, junio 2026). No related information, decisions, or unresolved items regarding these topics.


gog

---
name: gog
description: Read-only Google Workspace CLI for Gmail, Calendar, Drive, Contacts, Sheets, and Docs.
homepage: https://gogcli.sh
metadata: {"clawdbot":{"emoji":"🎮","requires":{"bins":["gog"]}}}
---

# gog

Use `gog` for **read-only** Google Workspace access.

Never send mail, create events, update sheets, edit docs, delete files, or do any write operation through this tool.

The server already has a stored authorized-user credential at `~/.config/gog/credentials.json`.

## Auth

- Status: `gog auth list --json`
- Status alias: `gog auth status --json`

If auth is broken, report the failure and ask the human to re-authorize outside the bot flow. Do not try to create or rotate credentials from inside the bot.

## Common commands

- Gmail search: `gog gmail search 'newer_than:7d' --max 10 --json --results-only --no-input`
- Gmail get message: `gog gmail get <messageId> --json --no-input`
- Calendar list: `gog calendar list --max 20 --json --results-only --no-input`
- Calendar events: `gog calendar events primary --from 2026-03-01 --to 2026-03-31 --json --results-only --no-input`
- Contacts / People search: `gog people search "ignacio" --max 10 --json --results-only --no-input`
- Drive search: `gog drive search "invoice" --max 10 --json --results-only --no-input`
- Drive metadata: `gog drive get <fileId> --json --no-input`
- Docs get: `gog docs get <documentId> --json --no-input`
- Sheets values: `gog sheets values <spreadsheetId> "Tab!A1:D20" --json --no-input`

## Notes

- Prefer `--json` for scripting.
- Prefer `--results-only` when you only need the main list.
- Use `people`, not `contacts`, for search.
- If Gmail/Calendar/Drive/People calls work, the credential is live.

## Attachment extraction workaround

`gog gmail` currently exposes `search` and `get`, but not a direct attachment-download subcommand.
When you need Gmail attachments (for example invoice PDFs), use this workflow:

1. Fetch the full raw message:
   - `gog gmail get <message_id> --format raw --json --no-input`
2. Read the `raw` field and decode it as **Gmail URL-safe base64**:
   - replace `-` → `+`
   - replace `_` → `/`
   - add `=` padding to a multiple of 4
3. Parse the decoded bytes as a MIME email and walk the parts.
4. For each `application/pdf` (or other attachment part), call `part.get_payload(decode=True)` and write the bytes to disk.

Python sketch:

```python
import base64, json
from email import policy
from email.parser import BytesParser

raw_json = json.loads(stdout_from_gog)
raw_b64 = raw_json["raw"].replace('-', '+').replace('_', '/')
raw_b64 += '=' * ((4 - len(raw_b64) % 4) % 4)
raw_bytes = base64.b64decode(raw_b64)
msg = BytesParser(policy=policy.default).parsebytes(raw_bytes)

for part in msg.walk():
    if part.get_content_type() == 'application/pdf' and part.get_filename():
        data = part.get_payload(decode=True)
        with open(part.get_filename(), 'wb') as f:
            f.write(data)
```

## PDF parsing in constrained environments

If the Hermes Python environment lacks `pip` or PDF libraries, prefer `uv run --with ...` for one-shot tooling instead of trying to install globally.
Example:

- `uv run --with pypdf python extract_pdf.py`

This worked well for extracting text from invoice PDFs saved from Gmail raw messages.


## Explicit Observations

[2026-04-10 04:04:56] 1400466012054491186 does not use DeepSeek or Kimi as a daily driver, only for workers/sub-agents.
[2026-04-10 04:04:56] 1400466012054491186's APIs configuration includes DeepSeek, Kimi K2.5, Mistral, OpenRouter, and ClawRouter.
[2026-04-10 04:04:56] 1400466012054491186 prefers direct answers and wants clear boundaries when discussing Discord/platforms.
[2026-04-10 04:04:56] 1400466012054491186's stack on April 3rd, 2026, used Hermes, with default claude-sonnet-4-6 via Anthropic OAuth until April 4th, 2026, and fallback gpt-5.4 via OpenAI Codex (Pro until approximately April 9th, 2026).
[2026-04-10 04:04:56] 1400466012054491186 has Tailscale installed on their personal computer for cross-device access.
[2026-04-10 04:04:56] 1400466012054491186's stack switches on April 5th, 2026, at 07:00 UTC.
[2026-04-10 04:04:56] 1400466012054491186 does not prioritize Apple skills.
[2026-04-10 04:04:56] 1400466012054491186's Gmail address is ignaciolagosruiz@gmail.com, with gog configured for read-only access.
[2026-05-13 19:00:21] 1400466012054491186 likes local Colegiales parks near Parque Ferroviario.
[2026-05-14 15:12:23] Ignacio expects to become a father around 2026-06-26 (probable due date) and needs training plans to remain flexible around that date.
[2026-05-14 15:12:28] Ignacio has therapy on two Tuesdays per month; therapy can run 12:30–13:45 and the therapist is about 35–45 minutes from Open Park Club Rosedal, making direct therapist-to-gym travel preferable to going home first.
[2026-05-15 18:18:52] 1400466012054491186 likes Palermo parks and local Colegiales parks near Parque Ferroviario.
[2026-05-17 15:59:36] 1400466012054491186's Gmail address is ignaciolagosruiz@gmail.com, which is configured for read-only access via gog.
[2026-05-17 15:59:36] 1400466012054491186 likes Palermo parks and local Colegiales parks near Parque Ferroviario.
[2026-05-17 15:59:36] 1400466012054491186 requires API documentation, OpenAPI, and valid examples before testing, with no false provenance.
[2026-05-17 15:59:36] 1400466012054491186 prefers Asana over Trello.
[2026-05-17 15:59:36] 1400466012054491186 follows the Jeff Min-Max fitness plan in Hevy.
[2026-05-17 15:59:36] 1400466012054491186 has Tailscale installed on their personal computer, enabling cross-device access.
[2026-05-17 15:59:36] 1400466012054491186 prefers YouTube Music over Spotify.
[2026-05-17 15:59:36] 1400466012054491186's dog's name is Chivi (with a 'v').
[2026-05-17 15:59:36] 1400466012054491186 does not want Pipo to interact with other people via WhatsApp or other messaging platforms at this time.

## Deductive Observations

[2026-05-13 17:06:51] 1400466012054491186 is a member of Open Park Club Rosedal in Buenos Aires.
   Premises:
   - [id:qjTl4BVoYs2PzJVo4a6U5] 1400466012054491186 received a schedule of classes from 'Open Park Club Rosedal' for the month of May.
   - [id:obRZsUSWjgqNmtG8IyaTL] 1400466012054491186 mentioned that there is a sauna at the gym that they like to use.

[2026-05-14 22:41:47] Ignacio is an expecting father; his wife Christina Monika Korf is pregnant with a due date of June 26, 2026.
   Premises:
   - mi esposa esta embarazada y le vendría muy bien usar la pileta... fecha probable de parto 26/06
   - esposa, christina monika apellido korf, dni 96436792

[2026-05-16 02:08:58] Ignacio is undertaking a home renovation project (installing a wooden wall panel with LED lighting) in May 2026 as part of preparations for his baby's arrival in late June 2026.
   Premises:
   - Ignacio's wife Christina is due on June 26, 2026.
   - Ignacio is currently organizing his living room and planning a DIY wood panel/LED installation.
   - Ignacio asked for IKEA-style instructions and a shopping list for the renovation.

- Name: Ignacio Lagos Ruiz (Nickname: Igna, Chicho)
- Email: ignaciolagosruiz@gmail.com (read-only via 'gog')
- Location: Buenos Aires, Argentina (UTC-3)
- Address: Céspedes 2491, Colegiales, Buenos Aires (near Parque Ferroviario)
- Family: Wife Christina Monika Korf (DNI 96436792, Pregnant, FPP June 26, 2026); Sister Eleonora Civit Ruiz
- Occupation: Software Engineer / Developer
- Skills: Android debugging (ADB), Full-stack Dev (Node.js, Vercel, Supabase), Networking (Tailscale, SSH tunnels), DevOps (Oracle Cloud, Ubuntu ARM64), Web Scraping, Browser Automation (CDP), Reverse Engineering (Web/APIs)
- Tech Stack: Ubuntu 24.04 ARM64, Oracle Cloud, Vercel, Supabase, GitHub, Tailscale, Windows (via SSH reverse tunnel for CDP/browser automation), Baileys (WA integration)
- AI Stack: Hermes WebUI (custom), Gemini 3.0 Flash (vision via OpenRouter), DeepSeek/Kimi (worker models), OpenAI/ElevenLabs (TTS), Mistral
- Current Projects: Argentinian government budget execution bot ('Presupuesto Abierto' API); Myristica WhatsApp bot (persona 'Pipo'); DIY Living Room Wall Panel (wood + LED)
- Mobile Devices: Google Pixel 9a (Daily Driver); Google Pixel 6a (Dedicated Tailscale exit node for local Argentina scraping)
- Pet: Dog named Chivi (with a 'v')
- Gym: Member of Open Park Club Rosedal in Palermo (Mon-Sat); researching SportClub pools for his wife
- Therapy: Two Tuesdays per month, 12:30–13:45 (Preferred travel: Therapist to Gym)
- Fitness: Jeff Nippard's Min-Max 4-day program with friend Andrés; Uses Hevy; Sunday rest. Fixed exercise substitutions.
- Recurring Activity: Hatha yoga on Thursdays at 3 PM
- INSTRUCTION: No Apple devices, no Spotify, no Trello. Use YouTube Music and Asana.
- INSTRUCTION: Low verbosity; prioritize high-signal, direct responses. Avoid filler and AI safety disclaimers.
- INSTRUCTION: Text responses by default; audio only on request with Argentinian accent.
- INSTRUCTION: Do not let 'Pipo' (AI persona) interact with others on messaging platforms for now.
- INSTRUCTION: Remove "W1: " prefix from all exercise descriptions in Hevy.
- INSTRUCTION: Ensure "Knee Raise Parallel Bars" is included in all gym routines.
- TRAIT: Architect of personal infrastructure; builds custom abstractions for total control.
- TRAIT: Cost-conscious and pragmatic AI operator; manages worker models and token usage with high granularity.
- TRAIT: Data-driven fitness; applies engineering principles to training and systematic tracking.
- TRAIT: Repurposes legacy hardware (e.g., Pixel 6a) for niche infrastructure needs.
- TRAIT: Hacker-first mentality; prioritizes technical flow and utility over procedural warnings.
- TRAIT: High-bandwidth, blunt communicator; rejects filler and friction.
- TRAIT: High-agency problem solver; expects "Plan B/C/D" solutions without excuses.
- TRAIT: Systematizes personal and family logistics using engineering logic (e.g., fitness, discounts, home DIY, health research).
- TRAIT: Extends technical workflows into the physical world (e.g., AI-calculated DIY instructions, custom web-hosted build tools).
- PREFERENCE: Demands empirical, verified evidence ('factos factos').
- PREFERENCE: Favors structured, visual documentation (e.g., 'IKEA-style' instructions) and actionable checklists.
- PREFERENCE: Uses sauna for 15 minutes post-workout recovery.
- PREFERENCE: Favors local (Argentinian) residential IPs for scraping.


Bien, ya tengo contexto. FPP 26/06/2026, Clínica Los Arcos, Dr. Senyk, Tina DNI 96436792. Vamos en paralelo:

Explanation:
Good. Now I have context. Let me organize what I know:

1. FPP: June 26, 2026
2. Clínica: Los Arcos, Juan B. Justo
3. Doctor: Senyk
4. Wife: Christina Monika Korf, DNI 96436792
5. Partera: Cristina (from the images)
6. Pediatra hospital: Dra. Cecilia

Now I need to:
1. Search Gmail for all relevant emails (partera, documentos, internación, arcos, Senyk, pediatra)
2. Research pediatricians
3. Research trash cans for diapers
4. Research baby cameras
5. Check documents from the images

Let me search Gmail with multiple queries in parallel, plus delegate the research tasks.

todo

Buscar emails en Gmail: partera, documentos, internación, arcos, Senyk, pediatra, Cecilia

Extraer y organizar documentos requeridos de las imágenes + emails

Research pediatras en CABA/Zona Colegiales-Palermo

Contactar Dra. Cecilia (pediatra hospital) sobre piel con piel

Research tacho de pañales (pañalera)

Research cámara para bebé

Turno EKG

Checklist final: documentos, bolso, auto, butaca


Research tacho de pañales (pañalera)

Research cámara para bebé

Turno EKG


Explanation: Let me now search Gmail with multiple queries and delegate the research tasks.

from hermes_tools import terminal
import json

# Search Gmail for multiple relevant queries in parallel
queries = [
    "partera",
    "Senyk OR arcos",
    "internacion OR internación",
    "pediatra OR pediatric",
    "documentos OR documentacion OR documentación",
    "Cecilia",
    "células madre OR celulas madre",
    "preparto OR parto OR obstetric",
    "Korf"
]

results = {}
for q in queries:
    cmd = f"gog gmail search '{q}' --max 10 --json --results-only --no-input 2>&1"
    out = terminal(cmd)
    results[q] = out["output"]

print(json.dumps(results, indent=2, ensure_ascii=False))

Context: Research pediatricians (pediatras) in Buenos Aires, Argentina - specifically in Colegiales/Palermo area or near Clínica Los Arcos (Juan B. Justo). Look for: neonatologists/pediatricians who attend at Clínica Los Arcos, recommendations, reviews, how to choose a pediatrician for a newborn. The baby is due June 26, 2026. Search in Spanish. Return organized findings with names, contact info, and notes.

Research pediatricians in CABA (Buenos Aires) near Colegiales/Palermo, especially those attending at Clínica Los Arcos. Search in Spanish.

Context: Research 'pañalera' or 'tacho de pañales' (diaper trash can/disposal system) available in Argentina. Look for: best models, prices, where to buy (Mercado Libre, Easy, etc.), reviews. The user is in Buenos Aires, Argentina. Search in Spanish. Compare at least 3-4 options.

Research diaper trash cans (pañaleras) available in Argentina - best models, prices, where to buy.

Context: Research baby cameras/monitors (cámaras para bebé / monitores de bebé) available in Argentina. Look for: best models, prices, where to buy (Mercado Libre, Easy, etc.), features (night vision, audio, app connectivity, WiFi vs non-WiFi). The user is in Buenos Aires, Argentina. Search in Spanish. Compare at least 3-4 options.

Research baby cameras/monitors available in Argentina - best models, prices, where to buy.