agentleFS
soreavissoreavis/research-entity/AGENTS.md

research-entity

AGENTS.md4 starsChanged 3 months agoMITView on GitHub

Guidance for AI agents working on this repository. (If you are looking for the skill itself, it lives in skills/research-entity/ — this file is about maintaining it.) research-entity packages a competitive-intelligence / due-diligence dossier generator as an Agent Skill, distributed to every agent platform that can install a skill or plugin. One skill tree, many thin manifests. Every install lane resolves to either main or a release tag, so both must be good before a version is announced: The only…

  • Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.

    Of the AGENTS.md files we read, the 31,700 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.

  • In this repository2 other files it gives its agents.

The file

We split this AGENTS.md into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Every install lane resolves to either main or a release tag, so both must be good before a version is announced:

    line 31
  2. Very specific:

    Add the CHANGELOG.md section under a new heading (the changelog enforcer gates PRs on this).

    line 34

≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md 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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
AGENTS.md

AGENTS.md

Guidance for AI agents working on this repository. (If you are looking for the skill itself, it lives in skills/research-entity/ — this file is about maintaining it.)

What this repo is

research-entity packages a competitive-intelligence / due-diligence dossier generator as an Agent Skill, distributed to every agent platform that can install a skill or plugin. One skill tree, many thin manifests.

skills/research-entity/SKILL.md   entrypoint — router, arguments, workflow
skills/research-entity/*.md       segment files; load on demand per the SKILL.md structure table
docs/                             end-user guides
scripts/build-skill-zips.sh       builds dist/ zips for upload-based runtimes (claude.ai, ChatGPT)
.claude/CLAUDE.md                 imports this file for Claude Code; not at the root, where a plugin ignores it
.claude-plugin/  .codex-plugin/  .cursor-plugin/  .grok-plugin/
.agents/plugins/  gemini-extension.json

Rules

  • Version lives in lockstep across all 8 manifests and the README badge. There is no release automation yet — when bumping, change every manifest and the badge together; CI's manifests job fails on drift.
  • The SKILL.md description must pass the Agent Skills spec (≤1024 chars, no angle brackets, name equals folder). CI measures it. The claude.ai uploader is stricter (≤200) — scripts/build-skill-zips.sh swaps in a short description in the dist copy only, so the repo skill keeps its rich one.
  • user-invocable and argument-hint are Claude Code extensions, not part of the open spec. They stay in the repo SKILL.md and are stripped from dist zips, where the uploader would reject them.
  • Keep segments modular. New reference content goes in a segment file with a load condition documented in the SKILL.md structure table — not inline in SKILL.md.
  • No personal, employer, or client names anywhere in the skill. Production-case lessons keep their calibration data (figures, dates) but anonymize the researched entity — and anonymization means unsearchable, not just relabelled. A case called "Acme CRM" still leaks if it keeps the real founder names, sibling brands, law firm, or a metric fingerprint precise enough to reverse-search. Strip the identity anchors; keep the pattern. CI's hygiene gate enforces this against a hashed denylist (hashed, so the guard never republishes what it forbids), blocks session-transcript quotes, and scans for generic secret shapes — private keys, credentialed connection strings, auth headers, assigned credential literals. GitHub scans provider tokens on public repos for free, but generic-pattern scanning requires an org-owned repo on Team+ with Secret Protection, which this repo is not, so the gate covers it instead.
  • Every factual claim in a segment file cites a primary source. Source-or-silence; see CONTRIBUTING.md.
  • Run the CI checks locally before committing: the spec measurement, npx --yes markdownlint-cli2 "**/*.md" "!node_modules", and bash scripts/build-skill-zips.sh.

Release checklist

Every install lane resolves to either main or a release tag, so both must be good before a version is announced:

  1. Bump the version in all 8 manifests and the README badge together — CI's manifests job fails on drift.
  2. Add the CHANGELOG.md section under a new heading (the changelog enforcer gates PRs on this).
  3. bash scripts/check.sh — every gate CI runs, including the hygiene guard.
  4. Tag and cut the release with scripts/build-skill-zips.sh output attached; the ChatGPT lane depends on a stable zip download.

The only repo setting with no API is Settings → General → Social preview (docs/assets/hero.jpg) — re-check it by eye after any repo recreate, since it does not survive one.

Download now (ZIP)

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.