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.
npx skills add randommonicle/claude-skills --skill committee-reviewgit clone --depth 1 https://github.com/randommonicle/claude-skillsWrote 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/randommonicle/claude-skills/committee-review)<a href="https://agentmods.dev/skills/randommonicle/claude-skills/committee-review"><img src="https://agentmods.dev/badge/skills/randommonicle/claude-skills/committee-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/randommonicle/claude-skills/committee-review"><img src="https://agentmods.dev/badge/skills/randommonicle/claude-skills/committee-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 | $0.00108 | $0.01927 |
| Opus 5 | $0.00054 | $0.00963 |
| Sonnet 5 | $0.00022 | $0.00385 |
| Haiku 4.5 | $0.00011 | $0.00193 |
Grade A, and why
committee-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 12d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Committee review
Three senior-reviewer lenses over one evidence base, then a vote. The output is not a pile of findings; it is a ratified work programme with priorities, dissents, and a paper trail. First run: PropOS, 2026-07-22 (docs/REVIEW_2026-07-22_three_fable.md in that repo).
When to run, and cost
Explicit user opt-in only. A full run is roughly 1M+ subagent tokens plus the main loop, and two to three hours wall clock. Scaled-down variant for smaller scopes: one adversarial reviewer plus two chair helpers, same evidence and vote discipline, chair provides the second ballot lens-switched. Never launch this because a task "would benefit"; the user asks for it.
Before launching, ask the user four scope questions (recommend the first option of each): live-DB access (read-only) or repo-only; what may be executed as evidence; where the report lands; whole-repo equal weight or recency-weighted.
Roles
- Neutral chair (the main agent). Runs the evidence pass, does a first-hand review of the highest-consequence recent code, consolidates, runs the vote, writes the report. The chair must read enough code directly to vote credibly; chairing is not clerking.
- Positive reviewer (subagent, strongest available model). What is well-engineered and must be protected; patterns worth extending. Honesty overrides role. Positive is not soft: on the first run this lens uniquely found encoding corruption, a docstring claiming features that did not exist, and built-but-unwired CI gates.
- Adversarial reviewer (subagent, strongest available model). Hostile due diligence: assume the docs overclaim; attack security, money paths, statutory logic, test honesty. House discipline: no FUD; every claim carries file:line or a query result.
- Helpers: each reviewer may spawn read-only helpers. Use an agent type that structurally cannot spawn or write (Explore-class) rather than trusting an instruction. Chair's helper split that worked: database integrity / application code / docs-vs-reality. Two tiers, both worth keeping: up to three strong-model helpers for judgement work (schema drift, code quality, compliance reasoning), and up to three fast/cheap-tier helpers for genuinely mechanical sweeps only: digesting oversized advisor or log dumps, verifying a long list item-by-item, counting and grep fan-outs. The cheap tier is an option, not a default; if a task needs judgement, it gets the strong model or it waits.
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.
- 12d ago First seen · 134 lines · 0 tokens per session scan A 02ac55be3263
committee-review is a skill published in the GitHub repository randommonicle/claude-skills (23 stars, last pushed 6d ago), licensed Apache-2.0. It adds 108 tokens to every session and 1,927 once invoked, about $0.0005 per session on Opus 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.
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