crew-consult
Skill1 starsChanged 4 months agoMITView on GitHub
Use when the user wants a multi-model second opinion on a design doc, plan, or spec before building.
In this repository10 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- llms.txtllms.txt
- biascheckSkill
- crew-reviewSkill
- experimentSkill
- multiplechoiceSkill
- priorartSkill
- secure-researchSkill
- unbiasSkill
The file
Files
crew-consult
Overview
Independent, multi-model review of a design or plan document. Report-only — it prints a report and
never edits the doc or triggers a build; acting on the verdict is the user's separate decision. The
review is produced by one script (crew.mjs): run the script. Do not perform the review yourself or
spawn your own reviewer subagents. Your job is to run it and surface its report faithfully.
Usage
Require a file: /crew-consult <file> [--item <label>] [--read family[:tune] ...] [--judge family[:model]] [--timeout <sec>]
Resolve the script
The script lives at <this skill's base directory>/../../scripts/crew.mjs. Resolve it from the
base directory the harness gives you for this skill — never guess or hardcode another location.
Run it
Echo the exact command, then run it from the current repo root:
node <resolved-path>/crew.mjs consult <file> [flags…]
Families: claude · openai (codex CLI) · google (agy CLI). With no flags the script detects
installed CLIs and prefers a cross-family crew automatically.
Tiers are fast / balanced / extra (aliases cheap / standard / most-capable); reviewers get
balanced at minimum. For codex, read codex debug models, choose a model and effort by capability,
and pass --read openai:<model>@<effort>; never copy a model id from a table or an old session.
After it finishes
- Surface compactly: the anchor (the doc's sha256 digest), the CREW composition line, each read's verdict and findings, and the judge's verdict with its discarded/systemic lists.
- Preserve the report-only boundary: do NOT act on the verdict — no edits to the doc, no follow-up build. Acting is the user's separate decision.
- On exit 1/2, relay the stderr message verbatim (it names the missing CLI, bad file, or usage problem) AND surface any report that printed on stdout — an exit 1 with all reads skipped still prints a useful report there.
- The run appends telemetry to
~/.preflight/modelcalls.jsonland writes nothing into the reviewed repo.
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
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.

