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.


[Note: model was just switched from deepseek-v4-pro to gpt-5.5 via OpenAI Codex. Adjust your self-identification accordingly.]

[nachlakes] CARGÁ ESTO PRIMERO:
- /skill hevy
- Leé /home/ubuntu/.hermes/references/minmax-training-plan.md

CONTEXTO:
Soy Ignacio (Chicho), 36, Buenos Aires, entreno en Open Park Club Rosedal con Andrés.
Sigo Jeff Nippard Min-Max 4-day program. Hoy es jueves 15/05/2026.

ESTADO:
- Hevy routines actualizadas en folder 2826578, orden Jeff, pesos W2.
- Schedule fijo: Lun Full Body, Mar Hatha 15:00, Mié Upper, Jue Lower, Vie Arms, Sáb yoga opcional, Dom rest.
- DeepSeek V4 Pro configurado para delegation.
- Esposa embarazada, FPP 26/06.

PRIMERA TAREA — DELEGACIÓN:
Usá delegate_task con DeepSeek para investigar cómo conectar Hermes con estas apps de salud:

1. MacroFactor (nutrición) — tiene API reverse-engineered vía Firestore, repo github.com/sjawhar/macrofactor
2. MyFitnessPal (nutrición) — API OAuth2 en myfitnesspalapi.com
3. Fitbit (actividad, sueño, frecuencia cardíaca) — Web API OAuth2 en dev.fitbit.com
4. Google Health Connect (hub de datos Android) — on-device, sin API cloud directa
5. Google Fit (actividad) — REST API, pero deprecándose, migra a Health Connect/Google Health API
6. Cronometer (nutrición) — sin API pública oficial, hay MCP reverse-engineered
7. Hevy — ya conectado

Para cada app: API type, doc URL, datos accesibles, auth, código Python esqueleto, factibilidad (1-5), costo.

Objetivo: que Pipo sea mi "Very Personal Trainer" — leyendo mi nutrición, peso, actividad y sueño, ajustando entrenamiento y porciones automáticamente.


hevy

---
name: hevy
description: Query workout data from Hevy including workouts, routines, exercises, and history. Use when user asks about their workouts, gym sessions, exercise progress, or fitness routines.
homepage: https://hevy.com
metadata:
  clawdbot:
    emoji: "🏋"
    requires:
      bins: ["hevy"]
      env: ["HEVY_API_KEY"]
---

# Hevy CLI

CLI for the Hevy workout tracking API. Query workouts, routines, exercises, and track progress.

## Setup

Requires Hevy Pro subscription for API access.

1. Get API key from https://hevy.com/settings?developer
2. Set environment variable: `export HEVY_API_KEY="your-key"`

## Troubleshooting / Environment Checks

Use this when the user asks whether Hevy is connected, whether the API key exists, or when `hevy status` fails.

1. Check the CLI wrapper and direct skill CLI:
   ```bash
   command -v hevy
   hevy status
   node ~/.hermes/skills/openclaw-imports/hevy/dist/cli.js status
   ```
2. If `hevy` prints `hevy CLI is not built yet: ~/.openclaw/workspace/skills/hevy/dist/cli.js`, the wrapper is stale from the old OpenClaw skill path. Update `~/.local/bin/hevy` so `SKILL_DIR="$HOME/.hermes/skills/openclaw-imports/hevy"`.
3. Search likely config/env locations for the key before asking the user:
   ```bash
   grep -R "HEVY_API_KEY\|hevyapp\|HEVY" ~/.hermes ~/.openclaw ~/hermes-webui ~/hermes-workspace 2>/dev/null
   ```
   Redact any key values in user-facing output.
4. If the key is expected in Bitwarden, verify session state:
   ```bash
   source ~/.bash_aliases 2>/dev/null || true
   bw-sync || true
   bw status
   ```
   If Bitwarden is `locked` or the saved session expired, you cannot retrieve the key until the user unlocks it (`bw-unlock`) or provides the key securely.
5. Only after `HEVY_API_KEY` is present, verify connection with:
   ```bash
   hevy status
   hevy workouts --limit 5 --kg
   ```

## Workout Program Planning & Load Calibration

Use this skill not only for raw Hevy queries, but also when the user wants to restart training, adopt a published program/spreadsheet/PDF, choose starting loads, estimate rep maxes, or log/calibrate a new routine in Hevy.

For Ignacio's Jeff Nippard Min-Max setup, read `references/jeff-minmax-hevy.md` before changing routines or schedules. It captures the source-grounded workout order, RIR rules, Open Park substitutions, fixed schedule (Lun Full Body, Mar Hatha, Mié Upper, Jue Lower, Vie Arms, Sáb yoga opcional), transition weeks, therapy Tuesday logistics, and yoga class details.

For a full-session cold start after `/reset`, use `templates/kickstart-fitness-session.md` — copy-paste it into a new chat to load all fitness context immediately.

The complete training plan with all weights, reps, RIR, and exercise notes is at `~/.hermes/references/minmax-training-plan.md` — read it when rebuilding routines or verifying program integrity.

When the user asks for an ongoing training schedule — especially if frustrated — answer with a concrete fixed calendar first: today/tomorrow/next few days, then the recurring weekly template, then only the minimal adjustment rules. Avoid long justification. Use the user's stated current date/time/timezone if provided; otherwise verify with `date` before assigning weekdays/dates.

Recommended workflow:

1. Retrieve the program source if provided (Drive folder, spreadsheet, PDF). For public Google Drive folders, `web_extract` can list filenames; if needed, parse the Drive page HTML for file IDs and download with `https://drive.google.com/uc?export=download&id=<FILE_ID>`.
2. Prefer the spreadsheet for exact programming tables. If Python lacks spreadsheet deps, install/use `openpyxl` and inspect sheets with coordinates so merged cells and headers are clear.
3. Use the PDF for explanatory rules: RIR definitions, warm-up protocol, progression rules, and safety caveats.
4. Query recent Hevy history (`hevy workouts --limit 10 --kg`, exercise history where useful) to distinguish a true beginner from a detrained returning lifter.
5. For returning lifters or beginners, do **not** recommend true 1RM testing by default. Estimate e1RM/training max from submaximal sets inside the program's target rep ranges using RIR:
   ```text
   possible_reps = completed_reps + estimated_RIR
   e1RM ≈ weight × (1 + possible_reps / 30)
   ```
   Treat this as a rough guide; the actionable output is the next-session load for the target rep range and RIR.
6. For heavy compounds (squat variants, incline/bench press, RDL, leg press, hip thrust), keep first-week testing submaximal and avoid failure, especially after a long layoff. Use machines/Smith/hack/leg press substitutions when safer.
7. For isolation exercises, do not over-focus on 1RM; choose loads by target reps + RIR and log them.
8. Recommend logging calibration workouts as normal Hevy workouts with explicit notes like `Program W1D1 — Calibration`, plus per-exercise notes for target reps/RIR, actual RIR, pain, technique, and chosen substitution.
9. After the workout, read the Hevy log and compute next-session recommendations: repeat, raise, or lower load based on whether reps and RIR matched the prescription.
10. When the user adapts a program to a real gym, update the Hevy routine templates to match actual substitutions instead of repeatedly fighting unavailable equipment. Read the logged workout, identify substitutions (e.g. machine missing → dumbbell/cable variant), then update the remaining routines with consistent available variants and clear notes.
11. If the user consistently wants cardio/abs around sessions, encode them in routines as explicit exercises where appropriate (e.g. 10 min elliptical warm-up; knee raise/crunch as an abs slot), but keep the base program intact and avoid adding fatigue that interferes with heavy lower-body work.
12. For hybrid lifting + yoga/stretching plans, make lifting progression the anchor and treat classes as recovery unless the user explicitly wants more conditioning. Put lower-fatigue options (Hatha, stretching, meditation/restorative yoga, easy Pilates) between or after lifting days; avoid recommending dynamic/vinyasa/flow classes early in a strength block if they may interfere with recovery.
13. When parsing gym class PDFs or grid schedules, do not trust plain text order if columns are ambiguous. Extract word coordinates (e.g. PyMuPDF `get_text('words')`), group by y-coordinate, and map words into known day/time columns before making calendar recommendations. Verify suspicious classes against column positions before answering.

### Schedule Output Shape

For ongoing plans, prefer this shape:

```text
Hoy — <date> — <program day / recovery>
Mañana — <date> — <exact class or rest>
Pasado — <date> — <exact class or rest>
Sábado — <date> — <catch-up rule or class>
Domingo — <date> — <rest>

Desde el lunes / Semana N:
Lun — D1 ...
Mar — recovery class ...
Mié — D2 ...
Jue — D3 ...
Vie — D4 ...
Sáb — catch-up or optional recovery class
Dom — rest
```

Keep it direct and operational; no motivational filler.

## Commands

### Status

```bash
# Check configuration and connection
hevy status
```

### Workouts

```bash
# List recent workouts (default 5)
hevy workouts
hevy workouts --limit 10

# Fetch all workouts
hevy workouts --all

# Show detailed workout
hevy workout <workout-id>

# JSON output
hevy workouts --json
hevy workout <id> --json

# Show weights in kg (default is lbs)
hevy workouts --kg
```

### Routines

```bash
# List all routines
hevy routines

# Show detailed routine
hevy routine <routine-id>

# JSON output
hevy routines --json
```

### Exercises

```bash
# List all exercise templates
hevy exercises

# Search by name
hevy exercises --search "bench press"

# Filter by muscle group
hevy exercises --muscle chest

# Show only custom exercises
hevy exercises --custom

# JSON output
hevy exercises --json
```

### Exercise History

```bash
# Show history for specific exercise
hevy history <exercise-template-id>
hevy history <exercise-template-id> --limit 50

# JSON output
hevy history <exercise-template-id> --json
```

### Creating Routines

```bash
# Create routine from JSON (stdin)
echo '{"routine": {...}}' | hevy create-routine

# Create routine from file
hevy create-routine --file routine.json

# Create a routine folder
hevy create-folder "Push Pull Legs"

# Update existing routine
echo '{"routine": {...}}' | hevy update-routine <routine-id>

# Create custom exercise (checks for duplicates first!)
hevy create-exercise --title "My Exercise" --muscle chest --type weight_reps

# Force create even if duplicate exists
hevy create-exercise --title "My Exercise" --muscle chest --force
```

**⚠ Duplicate Prevention:** `create-exercise` checks if an exercise with the same name already exists and will error if found. Use `--force` to create anyway (not recommended).

**Routine JSON format:**
```json
{
  "routine": {
    "title": "Push Day 💪",
    "folder_id": null,
    "notes": "Chest, shoulders, triceps",
    "exercises": [
      {
        "exercise_template_id": "79D0BB3A",
        "notes": "Focus on form",
        "rest_seconds": 90,
        "sets": [
          { "type": "warmup", "weight_kg": 20, "reps": 15 },
          { "type": "normal", "weight_kg": 60, "reps": 8 }
        ]
      }
    ]
  }
}
```

**Routine creation API pitfalls learned in practice:**

- Consult/ground against https://api.hevyapp.com/docs/ before write operations; the API is young and stricter than some local TypeScript types.
- `POST /v1/routine_folders` body is exactly:
  ```json
  { "routine_folder": { "title": "Folder Name" } }
  ```
- `POST /v1/routines` accepts `folder_id` on create, but `PUT /v1/routines/{id}` does **not** accept `routine.folder_id`; you cannot move a routine between folders via PUT.
- `DELETE /v1/routines/{id}` is not supported. If a duplicate is created, the API cannot delete it; at best rename it via PUT (for example `ZZ IGNORE — duplicate ...`) and tell the user to delete manually in the app.
- `routine.exercises[].sets[].rpe` is rejected on routine creation (`"rpe" is not allowed`) even though workout/history types may include RPE. Omit `rpe` from routine template sets.
- `rep_range` on routine sets is accepted and useful for program templates:
  ```json
  { "type": "normal", "weight_kg": null, "reps": null, "rep_range": { "start": 6, "end": 8 } }
  ```
- `POST /v1/routines` may return `{ "routine": [ ... ] }` rather than a bare object; unwrap arrays carefully and verify by listing/getting routines afterward.
- Some `hevy ... --json` CLI commands print status text before the JSON payload (for example `Fetching exercise templates...`). When scripting, strip everything before the first `[` or `{` before `json.loads`, or call the REST API directly.
- Avoid blind retry after a 400/partial failure: first list routines/folders to see what was already created. Folder creation can succeed even when subsequent routine creation fails, and retrying without idempotency creates clutter.
- Before creating program routines, fetch all exercise templates and verify every `exercise_template_id` with `GET /v1/exercise_templates/{id}`; invalid IDs cause the whole routine create to fail.
- For published programs, put RIR/progression cues in exercise `notes`; Hevy routine templates do not have a first-class RIR field.

### Other

```bash
# Total workout count
hevy count

# List routine folders
hevy folders
```

## Usage Examples

**User asks "What did I do at the gym?"**
```bash
hevy workouts
```

**User asks "Show me my last chest workout"**
```bash
hevy workouts --limit 10  # Find relevant workout ID
hevy workout <id>         # Get details
```

**User asks "How am I progressing on bench press?"**
```bash
hevy exercises --search "bench press"  # Get exercise template ID
hevy history <exercise-id>              # View progression
```

**User asks "What routines do I have?"**
```bash
hevy routines
hevy routine <id>  # For details
```

**User asks "Find leg exercises"**
```bash
hevy exercises --muscle quadriceps
hevy exercises --muscle hamstrings
hevy exercises --muscle glutes
```

**User asks "Create a push day routine"**
```bash
# 1. Find exercise IDs
hevy exercises --search "bench press"
hevy exercises --search "shoulder press"
# 2. Create routine JSON with those IDs and pipe to create-routine
```

## Notes

- **Duplicate Prevention:** `create-exercise` checks for existing exercises with the same name before creating. Use `--force` to override (not recommended).
- **API Limitations:** Hevy API does NOT support deleting or editing exercise templates - only creating. Delete exercises manually in the app.
- **API Rate Limits:** Be mindful when fetching all data (--all flag)
- **Weights:** Defaults to lbs, use --kg for kilograms
- **Pagination:** Most commands auto-paginate, but limit flags help reduce API calls
- **IDs:** Workout/routine/exercise IDs are UUIDs, shown in detailed views

## API Reference

Full API docs: https://api.hevyapp.com/docs/

### Available Endpoints
- `GET /v1/workouts` - List workouts (paginated)
- `GET /v1/workouts/{id}` - Get single workout
- `GET /v1/workouts/count` - Total workout count
- `GET /v1/routines` - List routines
- `GET /v1/routines/{id}` - Get single routine
- `GET /v1/exercise_templates` - List exercises
- `GET /v1/exercise_templates/{id}` - Get single exercise
- `GET /v1/exercise_history/{id}` - Exercise history
- `GET /v1/routine_folders` - List folders

### Write Operations (supported but use carefully)
- `POST /v1/workouts` - Create workout
- `PUT /v1/workouts/{id}` - Update workout
- `POST /v1/routines` - Create routine
- `PUT /v1/routines/{id}` - Update routine
- `POST /v1/exercise_templates` - Create custom exercise
- `POST /v1/routine_folders` - Create folder

The CLI focuses on read operations. Write operations are available via the API client for programmatic use.

To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'


     1|# Ignacio — Min-Max Training Plan (Full Context)
     2|# Generated 2026-05-14 after extensive multi-session planning
     3|# This file captures everything discussed so the next session starts clean.
     4|
     5|## Program Source
     6|- **Program:** Jeff Nippard's Min-Max Program (12 weeks, 2 blocks of 6 weeks)
     7|- **PDF:** Downloaded from Google Drive, verified page by page (78 pages)
     8|- **Key rules from Jeff:**
     9|  - 4 workouts/week: Full Body → Upper → Lower → Arms/Delts
    10|  - Week 1 = intro/lighter (RIR 1-2), Week 2+ = intensity (RIR 0-1), Week 7 = deload
    11|  - 5-10 min light cardio warmup before each session
    12|  - For squat, barbell incline, leg press, Smith lunge: RIR 0 = clean rep limit, no failed rep
    13|  - For other exercises: RIR 0 = true failure, attempt the rep
    14|  - Substitutions allowed: same muscle + same movement pattern
    15|  - Keep exercises constant for technique mastery and progression
    16|  - 1-2 rest days after Full Body and after Arms/Delts
    17|  - Yoga/extra = low intensity only to protect recovery
    18|
    19|## Gym
    20|- **Open Park Club Rosedal** (Palermo, Buenos Aires)
    21|- **Hours:** Mon-Sat (opens ~7am, classes run into evening), Sun closed
    22|- **Equipment limitations:** No lying leg curl, no T-bar row, no machine shrug, no standing calf raise,
    23|  no machine hip thrust, no machine lateral raise
    24|- **Class grid extracted** from schedule PDF using word-coordinate parsing (PyMuPDF)
    25|
    26|## Available Yoga/Stretching Classes (relevant)
    27|### Fixed in schedule
    28|- **Martes 15:00** — Yoga Hatha — Cintia Cavalero
    29|- **Jueves 15:00** — Yoga Hatha — Cintia Cavalero
    30|- **Sábado 16:00** — Yoga y Meditación — Cintia Cavalero (optional)
    31|
    32|### Avoid (interfere with strength recovery)
    33|- Yoga Dinámico (Tue/Thu 18:00)
    34|- Yoga Vinyasa / Vinyasa Flow (various)
    35|- Yoga Flow when legs/back loaded
    36|
    37|## Hevy Setup
    38|- **Folder:** "Jeff Nippard Min-Max 4x — Start 2026-05" (ID: 2826578)
    39|- **4 routines in folder,** updated to Block 1 with W2 weights, Jeff order, substitutions:
    40|  - `Min-Max 4x D1 — Full Body (Block 1)` (ID: 78e21872-...)
    41|  - `Min-Max 4x D2 — Upper (Block 1)` (ID: 2164feea-...)
    42|  - `Min-Max 4x D3 — Lower (Block 1)` (ID: d7b6da14-...)
    43|  - `Min-Max 4x D4 — Arms/Delts (Block 1)` (ID: f3051fb9-...)
    44|- **API key:** In Bitwarden, cached at `/tmp/hevy_key_runtime` during sessions
    45|- **CLI:** `~/.local/bin/hevy` (wraps `~/.hermes/skills/openclaw-imports/hevy/dist/cli.js`)
    46|- **Backup:** `/tmp/hevy_minmax_backup_before_block1_update.json`
    47|
    48|## Substitutions (all Jeff-approved: same muscle, same pattern)
    49|```
    50|Lying Leg Curl       → Seated Leg Curl (Machine)
    51|Pull-Up Wide Grip    → Lat Pulldown (Cable) wide grip
    52|Chest-Supported T-Bar → Seated Row (Machine)
    53|Machine Shrug        → DB Shrug
    54|Cable Crunch         → Machine Crunch
    55|Reverse Pec Deck     → Face Pull (Cable)
    56|Machine Hip Thrust   → Barbell Hip Thrust
    57|Machine Lateral Raise → DB Lateral Raise
    58|Standing Calf Raise  → Calf Press (Machine)
    59|```
    60|
    61|## Fixed Schedule (from 2026-05-25 onward)
    62|```
    63|Lunes      — D1 Full Body
    64|Martes     — Yoga Hatha 15:00 (fijo)
    65|Miércoles  — D2 Upper
    66|Jueves     — D3 Lower
    67|Viernes    — D4 Arms/Delts
    68|Sábado     — Yoga y Meditación 16:00 (opcional / buffer de pesas)
    69|Domingo    — Rest total
    70|```
    71|
    72|## Rules
    73|1. **Min-Max > Yoga.** If conflict, cancel yoga, keep pesas.
    74|2. **Saturday = buffer.** If all 4 pesas done → yoga. If missed one → recover pesas Saturday.
    75|3. **Tuesday Hatha = fixed recovery.** Therapy Tuesdays: go straight from therapy to gym. Arrive ~14:20-14:30, Hatha 15:00.
    76|4. **Thursday Hatha = backup only.** Not fixed because Thursday = Lower day.
    77|5. **Yoga frequency:** 1× per week minimum (Martes Hatha). Sábado optional.
    78|6. **If chaotic weeks:** Do workouts in order (FB → Upper → Lower → Arms), don't skip any, carry overflow to next week.
    79|
    80|## Transition Weeks (May 2026)
    81|```
    82|Jue 14/05 — Hatha Yoga 15:00
    83|Vie 15/05 — W2D1 Full Body
    84|Sáb 16/05 — Yoga y Meditación opcional
    85|Dom 17/05 — Rest
    86|
    87|Lun 18/05 — W2D2 Upper
    88|Mar 19/05 — Hatha Yoga (terapia 12:30-13:45, directo al gym)
    89|Mié 20/05 — W2D3 Lower
    90|Jue 21/05 — W2D4 Arms/Delts
    91|Vie 22/05 — Rest / caminata
    92|Sáb 23/05 — Yoga opcional
    93|Dom 24/05 — Rest
    94|
    95|→ FIXED SCHEDULE STARTS LUN 25/05 ←
    96|```
    97|
    98|## Block 1 Schedule through due date
    99|```
   100|W3: 25/05 FB → 26/05 Hatha → 27/05 Upper → 28/05 Lower → 29/05 Arms → ...
   101|W4: 01/06 FB → ...
   102|W5: 08/06 FB → ...
   103|W6: 15/06 FB → ...
   104|W7: 22/06 FB → ... (deload, lighter week — PERFECT, due date is Fri 26/06)
   105|```
   106|
   107|## Training Partners
   108|- **Andrés:** Gym partner, trains together at Open Park
   109|- **Christina (Tina):** Wife, pregnant, due ~26/06/2026
   110|- **Chicho expects to be a father.** From last 2 weeks pre-due date: flexible schedule, just do next workout in order.
   111|
   112|## Key Dates
   113|- **2026-05-13:** W1D4 completed (Arms/Delts calibration)
   114|- **2026-05-14 (today):** Hatha Yoga 15:00
   115|- **2026-05-15:** W2D1 Full Body
   116|- **2026-05-19:** Therapy 12:30-13:45, then Hatha 15:00
   117|- **2026-05-25:** Fixed schedule starts (W3D1 Full Body)
   118|- **2026-06-26:** Probable due date (falls on W7 deload week Friday — ideal)
   119|
   120|## Hevy Routine D1 — Full Body weights (W2)
   121|```
   122|Warm Up     — 10 min cardio liviano
   123|Seated Leg Curl    — W: 35×10, N: 55×6-8, N: 55×6-8 (RIR 0/0)
   124|Smith Squat        — W: 20×8 / 35×5, N: 45×6-8, 47.5×6-8 (RIR 1/0)
   125|Barbell Incline    — W: 20×8 / 22.5×4, N: 22.5×6-8, 25×6-8 (RIR 1/0)
   126|Incline DB Y-Raise — W: 2×10, N: 3×8-10 (RIR 0)
   127|Lat Pulldown Wide  — W: 25×8, N: 40×6-8, 40×6-8 (RIR 1/0)
   128|Calf Press         — W: 50×10, N: 80×6-8 (RIR 0)
   129|Knee Raise         — W: 10, N: 8-12, 8-12 (extra, not Jeff)
   130|```
   131|
   132|## Hevy Routine D2 — Upper weights (W2)
   133|```
   134|Close-Grip Pulldown — W: 25×8 / 35×5, N: 40×8-10, 42.5×8-10 (RIR 1/0)
   135|Seated Row Machine  — W: 35×8 / 40×5, N: 50×8-10, 50×8-10 (RIR 1/0)
   136|DB Shrug            — W: 10×10, N: 16×6-8 (RIR 0)
   137|Machine Chest Press — W: 25×10 / 30×5, N: 30×8-10, 32.5×8-10 (RIR 1/0)
   138|Cable Lateral Raise — W: 2.5×8, N: 2.5×8-10, 2.5×8-10 (RIR 0/0)
   139|Machine Crunch      — W: 15×10, N: 25×6-8, 30×6-8 (RIR 0/0)
   140|Face Pull           — W: 10×10, N: 20×8-10 (RIR 0)
   141|Knee Raise          — extra
   142|```
   143|
   144|## Hevy Routine D3 — Lower weights (W2)
   145|```
   146|Leg Extension       — W: 40×10, N: 60×8-10, 65×8-10 (RIR 0/0)
   147|Barbell RDL         — W: 30×8 / 40×5, N: 50×6-8, 55×6-8 (RIR 2/1)
   148|Barbell Hip Thrust  — W: 50×10 / 60×5, N: 70×6-8, 70×6-8 (RIR 1/0)
   149|Leg Press           — W: 40×10 / 60×6, N: 80×6-8 (RIR 0)
   150|Calf Press          — W: 50×10, N: 80×8-10, 80×8-10 (RIR 0/0)
   151|Knee Raise          — extra
   152|```
   153|
   154|## Hevy Routine D4 — Arms/Delts weights (W2)
   155|```
   156|Bayesian Cable Curl — W: 2.5×10, N: 5×6-8, 5×6-8 (RIR 0/0)
   157|Overhead Triceps Ext — W: 5×10, N: 7.5×8-10, 10×8-10 (RIR 0/0)
   158|Zottman Curl        — W: 4×10, N: 6×8-10 (RIR 0)
   159|Cable Kickback      — W: 2.5×10, N: 3.75×8-10, 3.75×8-10 (RIR 0/0)
   160|DB Wrist Curl       — W: 4×10, N: 8×8-10, 8×8-10 (RIR 0/0)
   161|Alternating DB Curl — W: 4×10, N: 6×6-8 (RIR 0)
   162|Dead Hang           — 30s + 30s
   163|Barbell Wrist Ext   — W: 10×10, N: 15×8-10, 15×8-10 (RIR 0/0)
   164|DB Lateral Raise    — W: 4×10, N: 6×8-10, 6×8-10 (RIR 0/0)
   165|Knee Raise          — extra
   166|```
   167|
   168|## Progression Rules
   169|- Hit top of rep range on all working sets → increase load next session
   170|- Adding weight drops you below range → keep weight, extend rep range slightly
   171|- RIR target wins over exact load
   172|- Log weights + reps + actual RIR in Hevy notes
   173|- After each W1D1/D2/D3/D4: read logs, compute next session recommendations
   174|
   175|## Important Notes for Next Session Agent
   176|- User prefers DIRECT, CONCISE responses
   177|- Hevy skill at: `openclaw-imports/hevy`
   178|- Reference file: `openclaw-imports/hevy/references/jeff-minmax-hevy.md`
   179|- Reminder: "Pesos sugeridos desde W2 según W1. Ajustar en el momento para cumplir RIR: si quedás fuera del rango, corregí carga."
   180|- DeepSeek V4 Pro configured as delegation model for subagents
   181|- Session search available for past context
   182|


Investigar cómo conectar Hermes Agent con apps de salud/nutrición/actividad para convertir a Pipo en un 'Very Personal Trainer'. Entregar un informe técnico por app: MacroFactor, MyFitnessPal, Fitbit, Google Health Connect, Google Fit, Cronometer, Hevy.

Context: Usuario: Ignacio/Chicho, 36, Buenos Aires, Android/Pixel, no Apple, usa Hevy ya conectado, sigue Jeff Nippard Min-Max 4-day program en Open Park Club Rosedal. Objetivo: Hermes/Pipo lea nutrición, peso, actividad y sueño, y ajuste entrenamiento/porciones automáticamente. Debés investigar con web. Para cada app incluir: API type, doc URL/repo URL, datos accesibles, auth, código Python esqueleto, factibilidad 1-5, costo. Apps a cubrir: 1) MacroFactor — API reverse-engineered vía Firestore, repo github.com/sjawhar/macrofactor. 2) MyFitnessPal — API OAuth2 en myfitnesspalapi.com. 3) Fitbit — Web API OAuth2 dev.fitbit.com. 4) Google Health Connect — on-device Android, sin API cloud directa. 5) Google Fit — REST API deprecándose, migración a Health Connect/Google Health API. 6) Cronometer — sin API pública oficial, hay MCP reverse-engineered. 7) Hevy — ya conectado. Sé concreto, técnico, directo, en español. No inventes: si algo es incierto, marcá 'requiere validación'. Incluí skeleton Python usable con placeholders, preferentemente requests/httpx/authlib/firebase-admin cuando corresponda. También recomendá arquitectura de integración para Hermes (cron jobs, token storage, local Android bridge si hace falta, schema de datos).