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 josherau/claude-operating-core --skill review-panelgit clone --depth 1 https://github.com/josherau/claude-operating-coreWrote 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/josherau/claude-operating-core/review-panel)<a href="https://agentmods.dev/skills/josherau/claude-operating-core/review-panel"><img src="https://agentmods.dev/badge/skills/josherau/claude-operating-core/review-panel/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/josherau/claude-operating-core/review-panel"><img src="https://agentmods.dev/badge/skills/josherau/claude-operating-core/review-panel.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 | $0.00075 | $0.00963 |
| Opus 5 | $0.00037 | $0.00481 |
| Sonnet 5 | $0.00015 | $0.00193 |
| Haiku 4.5 | $0.00007 | $0.00096 |
Grade A, and why
review-panel 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 9d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Panel
Overview
The maker never grades its own homework. The agent that produced work never decides it's ready — a panel of independent, hard-to-please reviewer subagents grades it against documented standards first. A model reviewing its own output is structurally compromised: it reuses the same reasoning that produced the work, forgives the gaps it already knows about, and mixes maker-knowledge into the verdict ("I couldn't verify X, so I left it out" is the maker lobbying, not a review). Self-reflection inside the maker plus independent graders outside it stack; neither replaces the other.
The Iron Rule
No READY / done / ship verdict on self-generated work without independent panel verdicts. Not for small artifacts, not under time pressure, not because the self-review "already found the issues."
Process
- Fix the standards first. Find the documented standards the work must meet (project docs, skill checklists, brand voice, user requirements). None written? Extract a checklist from the user's request before empaneling — reviewers grade against a checklist, not vibes.
- Empanel 2–4 reviewers with distinct lenses. Each is a fresh subagent that receives ONLY: the task brief, the artifact, the standards, and its reviewer charge. Never the maker's reasoning, self-evaluation, or "known limitations."
- Reviewer charge (include verbatim): "You are a hard-to-please reviewer. Your job is to find reasons this fails the standards, not to appreciate it. Grade each standard PASS/FAIL with evidence. Uncertain → FAIL. End with verdict: SHIP or REVISE, plus your top 3 specific objections."
- Iterate. REVISE verdicts go back to the maker with the objections; revise; re-run the failed reviewers on the new version. Max 3 rounds — then deliver to the human with unresolved objections attached. Run reviewers in the foreground and collect every verdict before proceeding — never end your turn while reviewers are still running.
- Report. Deliver the work with panel verdicts and what changed per round. Never present panel-less work as finished.
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.
- 9d ago First seen · 52 lines · 75 tokens per session scan A ff2a82812050
review-panel is a skill published in the GitHub repository josherau/claude-operating-core (3 stars, last pushed 29d ago), licensed MIT. It adds 75 tokens to every session and 963 once invoked, about $0.0004 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.
Other skills, from other repositories
review-work
Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.
one-way-door
Flags irreversible decisions before commit. Use for data models, infra, auth boundaries, API contracts, event schemas, CI/CD.
map-codebase
Deep architecture report that fans out parallel inspections across different aspects of the codebase (structure, tech stack, APIs, patterns, data flow, dependencies, testing) and synthesizes findings into a comprehensive document at .turbo/codebase-map.md and .turbo/codebase-map.html. Use when the user asks to "map…
critical-code-reviewer
Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
reply-to-pr-threads
Draft, confirm, and post replies to GitHub PR review threads. Handles per-category reply formatting, re-fetches thread resolution state so auto-resolved threads are skipped, and posts via GraphQL. Use when the user asks to "reply to PR threads", "post PR thread replies", or "draft PR reply messages".
answer-reviewer-questions
For each reviewer question on a PR, recall implementation reasoning and compose a raw answer. Use when the user asks to "answer reviewer questions", "draft answers to PR questions", or "explain reviewer questions".