Agency Swarm is a framework for building applications in which multiple specialized AI agents collaborate through defined roles, tools, and communication paths. Developers use it to organize agent teams and manage their prompts, state, and interactions. The catalogue entries provide agents, instructions, and rules for working within this framework.
Borrowing it
Nothing to install: this file belongs to VRSEN/agency-swarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/VRSEN/agency-swarm/main/.claude/skills/review/SKILL.mdgit clone --depth 1 https://github.com/VRSEN/agency-swarmWrote 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/skills/vrsen/agency-swarm/review)<a href="https://agentmods.dev/skills/vrsen/agency-swarm/review"><img src="https://agentmods.dev/badge/skills/vrsen/agency-swarm/review/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/skills/vrsen/agency-swarm/review"><img src="https://agentmods.dev/badge/skills/vrsen/agency-swarm/review.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 | $0.00032 | $0.00372 |
| Opus 5.5 | $0.00013 | $0.00149 |
| Sonnet 5.5 | $0.00006 | $0.00074 |
| Haiku 4.5 | $0.00003 | $0.00037 |
Grade A, and why
review 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 15d 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.
What it actually says
Review a change
Every change gets a review against origin/main by a different model than the one that wrote it. A change to AGENTS.md and each release commit get their review from the strongest model available that did not write it.
Steps
- Diff the branch against
origin/main. Read the whole diff, not only the files the author named. - Check the core principle first: does the change use the OpenAI Agents SDK where it could, or does it reimplement SDK behavior? A wrapper around an SDK abstraction names the SDK class in its docstring and supports every feature of that class or lists each gap with a reason (AGENTS.md 1.3).
- Check the repository rules in
AGENTS.md: typing at boundaries, no fallbacks that accept several types, imports at the top, file and method size, one name for one thing, lockfiles updated with dependency changes. - Check tests: a change to existing behavior has a test that fails without it; agent messaging and OpenClaw runtime changes are tested through real framework objects, not generic mocks; coverage stays at 90% or more.
- Run
make check. For a runtime, interface or integration change, also runmake tests. For a broad or risky change, runmake ci. - Report findings ranked by severity, each with the file, the line and the concrete failure it causes. Say plainly when nothing was found.
A review comment, approval or merge on GitHub is a public action: the maintainer approves the exact text or action first (AGENTS.md 2.2).
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.
- 15d ago First seen · 20 lines · 32 tokens per session scan A e441dfedaa87
review is a skill published in the GitHub repository VRSEN/agency-swarm (4,617 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 372 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 2026-10-13.
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