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效率与协作 · SKILL.md

observer

observer

【效率与协作】Monitor a session passively to infer the user's workflow from what actually happened, staying dormant until a trigger phrase activates observation for workflow-architect.

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技能说明

GitHub

name: observer description: Monitor a session passively to infer the user's workflow from what actually happened, staying dormant until a trigger phrase activates observation for workflow-architect. license: MIT compatibility: Hermes Agent — uses session_search and context scanning metadata: tags: workflow, observation, discovery, passive spec-version: '1.0'

Observer — Passive Workflow Discovery

This is the passive observation mode of workflow-architect. Unlike the interviewer, which asks questions, the observer watches and infers.

How It Works

  1. The observer loads silently via /workflow-architect passive
  2. It does nothing until a trigger phrase is detected
  3. On trigger, it scans the current session's message history using a structured inference prompt
  4. The output is a workflow state model (same structure as the interviewer produces), which feeds into the bundle-builder sub-skill

Trigger Phrases

The observer activates when the user says any of these:

  • "catalog my workflow"
  • "what's my workflow"
  • "analyze my process"
  • "figure out what I do"
  • "work it out from what I just did"

If none of these are detected in the user's message, the observer remains dormant. Do not announce its presence — the user may not remember loading it in passive mode.

Activation Protocol

When a trigger phrase is detected:

  1. Check session depth. Count substantive user messages (excluding greetings, meta-comments about the agent, and one-word replies). If fewer than 5 substantive messages, respond:

    "I don't have enough session context to work with yet. I've seen about
    [N] substantive turns, and I need more to find reliable patterns.
    Try active interrogation mode instead: /workflow-architect"
    
  2. If enough context exists, run inference. Use the following structured prompt against the session context. You may use session_search or browser console to review the session transcript if needed.

    You are analyzing a session transcript to extract workflow patterns.
    Look at the user's messages and your responses. Identify:
    
    1. ENTRY PATTERNS — How did the session start? What was the user's
       first request? Was it a check-in, a specific task, a question?
    
    2. PHASES — Where did the session shift focus? What triggered each
       shift? (A new request, a status check, a tool output?)
    
    3. BRANCHING — Were there decision points where the user could have
       gone in different directions? What determined the direction taken?
    
    4. TOOLS — What tools did the user reach for? What commands did they
       ask you to run? What contexts did they reference?
    
    5. PAIN POINTS — Were there moments of friction? (Repeated corrections,
       stops-and-restarts, "no, not that" type corrections)
    
    6. EXIT — How did the session end (or approach ending)? Was it a
       natural completion, an interruption, or something else?
    
    Return your findings as a structured JSON document matching the
    workflow-architect state model format.
    
  3. If the session context is available via session_search, also pull the 2-3 most recent related sessions for cross-session pattern detection. A single session may not reveal the full workflow; multiple sessions do.

  4. Present findings to the user:

    "I looked through this session (and [N] recent related sessions) and
    found some patterns in how you work:
    
    [Summary of inferred workflow, similar to interviewer's convergence summary]
    
    Does this look right? If yes, I'll generate a bundle. If not, tell me
    what I got wrong — or switch to active mode for a more thorough conversation."
    
  5. If the user confirms, load skills/bundle-builder/SKILL.md and follow its instructions to generate the output bundle.

Limitations

  • Single-session bias: One session may not represent your full workflow. The observer does its best work with multiple sessions of context.
  • Action-inference gap: The observer only sees what happened, not what you intended or considered and rejected. Active interrogation captures richer intentional data.
  • Silent mode: The observer does not announce itself when loading. This is intentional — passive mode is meant to be invisible until triggered.

When not to use

Do not use this skill for interactive discovery (load the interviewer instead), or when the session context is too thin to infer reliable patterns — active interrogation is more effective there. It is for passively observing an existing session only.

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