agentleFS
wesenwesen/skills/transcript-doc-friction-analysis/SKILL.md

transcript-doc-friction-analysis

Skill4 starsChanged 4 months agoNo licenceView on GitHub

Analyze coding-agent session transcripts (Claude Code, Codex, Pi) with go-minitrace to find where documentation, skills, or agent instructions caused friction — and turn the evidence into concrete doc/skill fixes. Use when asked to analyze why an agent session struggled, audit skill usage or doc consumption, find what documentation is missing or ignored, measure token cost of knowledge acquisition, mine a build session for AGENT.md guards, or re-measure doc-consumption after documentation changes. Also load BEFORE hand-writing SQL/JS against minitrace archives for any docs/skills-improvement question — this skill ships tested docmetrics query commands (doc-consumption, source-probes, api-calls, failure-triage, episodes) that answer the common questions in one call each. Triggers on - "analyze the transcript", "doc friction", "why did the agent grep the source", "improve our skills/docs based on sessions", "which skills fired", "session post-mortem", "documentation optimization".

The file

We split this skill into the rules it gives an agent

7 rules. About 0 are common, 5 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

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  • It worked
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What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.