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 mvschwarz/openrig --skill review-teamgit clone --depth 1 https://github.com/mvschwarz/openrigWrote 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/mvschwarz/openrig/review-team)<a href="https://agentmods.dev/skills/mvschwarz/openrig/review-team"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/review-team.svg" alt="Measured on agentmods" 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 | $0.00054 | $0.01964 |
| Opus 5 | $0.00027 | $0.00982 |
| Sonnet 5 | $0.00011 | $0.00393 |
| Haiku 4.5 | $0.00005 | $0.00196 |
Grade A, and why
review-team 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 3d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Team
You are part of the review pod. Your value is fresh scrutiny that implementation and QA do not have.
Proportionality — right-size the review to the change (read first)
Review rigor scales to stakes and change size. A small, low-stakes diff gets a fast, focused pass; the full deep protocol (context proof, confidence scores, independent → cross-exam → convergence → roundtable) is for architecture / security / high-blast-radius changes. Don't run the heavy machinery on a one-line fix — that's ceremony, and it delays the ship it exists to protect. Catch what matters, then let good work through. The point of review is better product shipped, not review performed.
Startup sequence
Before you announce a review position:
- load
openrig-user,review-team,systematic-debugging, andverification-before-completion - run
rig whoami --json - inspect the current rig state so you know whether you are reviewing a diff, a working tree, verification output, or only startup behavior
If there is no real review target yet, say that plainly and stay ready.
Context priming — always do this first
Before reviewing ANY code, you must understand the codebase context. Never review cold.
- Read the project's
CLAUDE.mdor equivalent conventions doc - Read the as-built architecture docs for the subsystems you're reviewing
- Read the relevant planning/spec docs if they exist
- Understand the domain vocabulary and key invariants
If you have blanks — areas you don't understand — say so explicitly and fill them before forming opinions. A review built on misunderstood context is worse than no review.
For deep reviews, write a context proof before proceeding:
- Subsystem purpose summary
- Key invariants (must-not-break rules)
- Architecture boundaries and constraints
- PR/range intent and expected behavior
- Unknowns / missing context
- Confidence scores (0-100) per section
Everyday review discipline
These apply to every review, not just deep reviews.
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.
- 3d ago Changed 20591dbbcb14
- 7d ago First seen · 214 lines · 54 tokens per session scan A 97da8b474c28
review-team is a skill published in the GitHub repository mvschwarz/openrig (65 stars, last pushed 2d ago), licensed Apache-2.0. It adds 54 tokens to every session and 1,964 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
cw-slice
Use before writing code for any Codewhale feature, upgrade, or refactor: find the existing owner of the behavior, bound the change to one reviewable slice, and fix the evidence bar before you start.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
simplify
Review recent code changes for reuse, code quality, and efficiency, then directly apply straightforward cleanup improvements. Use when the user wants a post-implementation cleanup pass, pre-PR polish, or asks to simplify/refine recent changes. Invoke with /simplify or /simplify .
simplify
Improve clarity and reduce needless complexity after behavior is understood; preserve behavior and keep cleanup separate from correctness fixes.
review
Read-only correctness review with actionable findings first, tight file/line evidence, severity, and a concise residual-risk summary.
find-simplifications
Use for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every…