agentleFS
briandconnellybriandconnelly/skills/agent-friendly-docs/SKILL.md

agent-friendly-docs

Skill1 starsChanged 39 days agoNo licenceView on GitHub

Use when designing, structuring, auditing, or reviewing the documentation surface of a repository that AI coding agents read while working — instruction files, README, docs/, ADRs, per-directory context files, and code-adjacent comments. Symptoms include agents re-deriving project context every session, reading stale or wrong docs, instruction files bloated with reference material, README token bloat, ADRs mistaken for current policy, embedded commands that fail as written, duplicated content drifting apart, and repo-wide context trapped in code comments. Covers layering and placement, discoverability and read paths, one authoritative home per fact, token economy, freshness mechanisms, and runnable examples. Not for published docs sites or llms.txt, generic prose quality, GitHub repo safety configuration, or rules-vs-context audits of instruction-file content.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 10 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈10

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    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.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
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.