agentleFS
nourhelminourhelmi/crew/skills/roster/SKILL.md

roster

Skill1 starsChanged 48 days agoMITView on GitHub

Set up or tune the models crew routes work to - which subscriptions you have, what each model is for, and what it costs you. Use when the user wants to configure, review or change their crew roster or model choices.

2 other projects use a version of this.

Adopted by

Most recently taken up by

  • Other versions2 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 2

    1. LilithLabs/lilith★ 1same name
    2. berch-t/Kopern-OS★ 0same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository11 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

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

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Write the file, then run crew roster and fix whatever it flags.

    line 49
  2. Very specific:

    If the router is installed and not reading this file, run agent-router roster use --file ~/.config/crew/roster.json (or agent-router init …

    line 50

≈ “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

Crew roster

The roster (crew roster shows it; the file is ~/.config/crew/roster.json) lists every model crew may launch, per role, in preference order. The router reads it to pick a model for each spawn; without a router, spawns take the first model per role.

1. Look before asking

  • crew roster: the current roster, each model's track record, and anything crew can't launch.
  • Which host CLIs exist: command -v claude codex opencode. opencode models lists what the user's OpenCode providers offer.
  • Recent work: crew ls --all shows which models ran as what.

2. Ask, briefly

In one message: which subscriptions and plan sizes they have, which models they've used and what they think of them, and what kind of work they mostly hand off. Their opinions outrank anything below.

3. Draft each entry

{ "model": "codex/gpt-6.1-sol", "effort": "high", "roles": ["advisor", "builder"], "cost": 0.15,
  "about": "the workhorse", "use": "…", "avoid": "…" }
  • model: <host>/<model id> (claude/claude-opus-5-5, codex/gpt-6.1-sol, opencode/opencode-go/kimi-k3). effort: one the model supports.
  • roles: advisor, builder, checker. Give a model only the roles it is good at.
  • cost from 0 to 1: the share of the user's limits one assignment burns. A bigger plan makes a model cheaper and a hungrier model or higher effort makes it dearer. Set the dearest at 1 and scale the rest against it.
  • use / avoid: the router's model judges fit from these alone. Name concrete kinds of work, and always say what a model is worse at. If every entry sounds good at everything, the router scores them all alike and quota headroom decides instead.
  • scope: "small-or-verification" for a cheap model that fumbles interpretation. It is then admitted only for small tasks or pure verification.
  • A model the user hasn't tried: draft from public evidence (agent-router benchmarks list, the provider's docs), say so in about, and let the track record correct it.
  • Order the file by preference within each role; ties go to the earlier entry.

4. Write, wire, try

  1. Write the file, then run crew roster and fix whatever it flags.
  2. If the router is installed and not reading this file, run agent-router roster use --file ~/.config/crew/roster.json (or agent-router init --roster ~/.config/crew/roster.json when it has no config yet).
  3. Take 5–10 real tasks from the user's work across all three roles. Run crew route --role <role> --task "<task>" for each and show the picks side by side. Adjust use/avoid until the picks match what the user would choose. Don't tune to a single task.

From then on the roster learns: checkers spawned with --checks <run> and crew grade build each model's track record, which crew roster shows.

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.