Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/askwigconsulting/cohortWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/askwigconsulting/cohort/crew)<a href="https://agentmods.dev/commands/askwigconsulting/cohort/crew"><img src="https://agentmods.dev/badge/commands/askwigconsulting/cohort/crew/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/askwigconsulting/cohort/crew"><img src="https://agentmods.dev/badge/commands/askwigconsulting/cohort/crew.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.02923 |
| Opus 5.5 | $0.00012 | $0.01169 |
| Sonnet 5.5 | $0.00006 | $0.00585 |
| Haiku 4.5 | $0.00003 | $0.00292 |
Grade A, and why
crew scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The standard development protocol, widened across vendors: the coordinating session
researches, plans, and signs off; tiered subagents implement. /crew wraps the existing
loops — it uses /plan's decomposition discipline for phase 2 and /build's
implement–test–verify–commit discipline inside each worker — and adds the model-routing
and signoff layer on top, routing each task to the cheapest capable model from any
vendor (the same token-optimization reason /scout reviews across vendors; /crew is
the build sibling of /scout). It is human-invoked; the human gates (commit review, PR
review) are unchanged.
The line: external engines never touch the main tree
This is the invariant, not a preference:
- Claude subagents write directly. A Claude worker is inside the trust boundary; it edits files in a disjoint footprint (or its own worktree) and the coordinator verifies the diff. Route by tier: fable architecture-critical, opus complex, sonnet well-scoped, haiku mechanical.
- External engines (Grok, ChatGPT) never touch the main working tree. They reach the
code only inside an isolated, throwaway worktree, two ways:
- OS-sandboxed CLI doer (preferred when the CLI is installed) — the engine's own
agentic CLI edits a fresh worktree directly, with real file access, every write
OS/kernel-confined there:
cohort engine work gptruns Codex under its ownworkspace-writesandbox;cohort engine work grokruns xAI's Grok Build under a Cohort-imposed bubblewrap jail — it ships a sandbox of its own, but a vendor confining itself is the vendor's claim, not Cohort's guarantee. An engine with no available sandbox is refused — never run unconfined. - Gated patch proposal (API fallback) —
cohort engine propose <engine> [--agentic]has the engine return (or explore-then-return) a candidate diff that Cohort — never the engine — parses and applies in a worktree behind the egress/secret/footprint gates. This is the API-direct path used when the local CLI isn't present; with grok-cli + bwrap installed,engine propose/review/consult grokprefer the sandboxed CLI automatically (local file access), and fall back to this API path with a printed note otherwise. Either way the coordinator verifies the diff and integrates, and it still passes the unchanged human PR review.
- OS-sandboxed CLI doer (preferred when the CLI is installed) — the engine's own
agentic CLI edits a fresh worktree directly, with real file access, every write
OS/kernel-confined there:
That is the difference between "let an external model do the work" being useful and being an RCE: it writes only where a bad run is thrown away — never your working tree, which changes only when Claude integrates a reviewed diff.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +1 lines 04d88185de41
- 1mo ago First seen · 208 lines · 30 tokens per session scan A 06187129e222
crew is a command published in the GitHub repository askwigconsulting/cohort (2 stars, last pushed 19d ago), licensed MIT. It adds 30 tokens to every session and 2,923 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.