agentleFS
LuisoteeLuisotee/agent-mesh/skills/find-peers/SKILL.md

find-peers

Skill0 starsChanged 16 days agoMITView on GitHub

Finds open-source projects that share a repo's core mechanisms, to learn from their code and history: searches GitHub, awesome lists and the web, ranks by mechanism overlap rather than stars, shows you the shortlist, and on your yes registers each as an agent-mesh peer, unbriefed. Use it, typed or not, when the user wants similar projects, competitors or prior art ("find peer projects", "who else solves this", "find projects to learn from"). Not for asking registered peers (/harvest-peers for a round, /delegate-claude or /delegate-agy for one question), or a mesh worker (your task came by a `📬 [mesh]` doorbell).

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are 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.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Characterize the repo: Read its README, CLAUDE.md / AGENTS.md and design docs.

    line 19

≈ “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.

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

/find-peers

Find the projects worth learning from for the user's request: $ARGUMENTS when the user typed the command, otherwise the request that brought you here. The repo is the one named, else the current one.

The goal is a few projects that solve the same problems by similar mechanisms, read from their code, not a popularity list. A peer is worth registering when asking it would change a design decision here.

  1. Characterize the repo. Read its README, CLAUDE.md / AGENTS.md and design docs. Write down, for yourself:
    • the problem it solves, in one sentence;
    • its 4 to 6 core mechanisms (how it does what it does, e.g. "delivers messages into running agent sessions", "a worktree per worker"), each named in plain words someone else's README would use;
    • its constraints (language, no dependencies, platform) and the alternatives it already rejected. Run mesh peers (if mesh is installed): skip projects already registered. The default group for new ones is the one the repo's CLAUDE.md / AGENTS.md or a group's about names for this repo; else ask in step 6. The user's focus, when given, narrows the mechanisms.
  2. Search several surfaces, a few queries per mechanism, not one for the whole repo:
    • gh search repos "<mechanism words>" --sort updated --limit 30, and by topic (--topic <t>, from the topics of the first good hits);
    • gh search code for a distinctive API, file name or config key the mechanism implies;
    • the awesome lists for the domain (gh search repos awesome <domain>), and the plugin directories or marketplaces when the repo is itself a skill, plugin or extension;
    • the package registry for its language;
    • a web search: "alternatives to <closest known project>", the problem in the user's words, and the "how it compares" sections of the hits, which name their competitors. Collect 10 to 25 candidates.
  3. Signs of life: gh repo view <owner/repo> --json description,stargazerCount,pushedAt,isArchived,licenseInfo,repositoryTopics. Drop dead ones (archived, or no push in a year) unless one is worth keeping as a contrast, and say so. Note a missing license: such a project is fine to read, not to copy from.
  4. Read before ranking. For each survivor, read its README, then its source tree: a shallow clone (git clone --depth 50) in a scratch directory, never inside the repo. Find the file that implements each shared mechanism. A README claims; the code shows.
  5. Rank by mechanism overlap, not stars: a table with a row per candidate and a column per mechanism (yes / partial / no, with the file that shows it), plus what it would teach that the current peers don't, and its signs of life. Stars only break ties. Keep 3 to 8.
  6. Show the shortlist and wait. The table, then for each pick one line on what to ask it, and the group name you suggest. Say the cost: a peer's clone lives under $MESH_PEERS_DIR/<group>/ (default ~/research), and its first research round (/harvest-peers) is a large task (a few hundred thousand tokens). Ask which to add. Register nothing and clone nothing under $MESH_PEERS_DIR before the user's yes: step 4's scratch clones are for reading only (delete them once you've ranked).
  7. On the yes, register each with mesh (needs agent-mesh installed; peers add needs no herdr):

    mesh peers add https://github.com/<owner>/<repo> --group <group> --about "<what it is; what to ask it about>"   # at most 400 characters
    mesh peers edit <group> --about "<what the group is, whose peers they are>"                                   # a new group only
    • Names: each peers add prints the asker's name (<repo>-research, the repo part cut to 20 characters): report that name, never one you built from the repo's name. Pass --name <short>-research when a basename is long, or when two shortlisted projects share one (the second add would fail).
    • Give no --body: it would ask each peer in turn and wait for it.
  8. Report and stop: the peers added, by name and group, with what each is for. Say they haven't been briefed yet, and that /harvest-peers gives them their first research round when the user wants it. Don't start it yourself: the user decides when to spend that.

Without agent-mesh, do steps 1 to 6 and give the shortlist with the clone commands: the search and the ranking are the part that works anywhere.

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.