Borrowing it
Nothing to install: this file belongs to lmanchu/iris-system. 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/lmanchu/iris-system/main/.claude/skills/gstack/review/SKILL.mdgit clone --depth 1 https://github.com/lmanchu/iris-systemWrote 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/lmanchu/iris-system/review)<a href="https://agentmods.dev/skills/lmanchu/iris-system/review"><img src="https://agentmods.dev/badge/skills/lmanchu/iris-system/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/lmanchu/iris-system/review"><img src="https://agentmods.dev/badge/skills/lmanchu/iris-system/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 | $0.00034 | $0.00681 |
| Opus 5.5 | $0.00014 | $0.00272 |
| Sonnet 5.5 | $0.00007 | $0.00136 |
| Haiku 4.5 | $0.00003 | $0.00068 |
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 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Landing PR Review
You are running the /review workflow. Analyze the current branch's diff against main for structural issues that tests don't catch.
Step 1: Check branch
- Run
git branch --show-currentto get the current branch. - If on
main, output: "Nothing to review — you're on main or have no changes against main." and stop. - Run
git fetch origin main --quiet && git diff origin/main --statto check if there's a diff. If no diff, output the same message and stop.
Step 2: Read the checklist
Read .claude/skills/review/checklist.md.
If the file cannot be read, STOP and report the error. Do not proceed without the checklist.
Step 3: Get the diff
Fetch the latest main to avoid false positives from a stale local main:
git fetch origin main --quiet
Run git diff origin/main to get the full diff. This includes both committed and uncommitted changes against the latest main.
Step 4: Two-pass review
Apply the checklist against the diff in two passes:
- Pass 1 (CRITICAL): SQL & Data Safety, LLM Output Trust Boundary
- Pass 2 (INFORMATIONAL): Conditional Side Effects, Magic Numbers & String Coupling, Dead Code & Consistency, LLM Prompt Issues, Test Gaps, View/Frontend
Follow the output format specified in the checklist. Respect the suppressions — do NOT flag items listed in the "DO NOT flag" section.
Step 5: Output findings
Always output ALL findings — both critical and informational. The user must see every issue.
- If CRITICAL issues found: output all findings, then for EACH critical issue use a separate AskUserQuestion with the problem, your recommended fix, and options (A: Fix it now, B: Acknowledge, C: False positive — skip). After all critical questions are answered, output a summary of what the user chose for each issue. If the user chose A (fix) on any issue, apply the recommended fixes. If only B/C were chosen, no action needed.
- If only non-critical issues found: output findings. No further action needed.
- If no issues found: output
Pre-Landing Review: No issues found.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 79 lines · 34 tokens per session scan A 7388a04f4921
review is a skill published in the GitHub repository lmanchu/iris-system (5 stars, last pushed 4d ago), licensed MIT. It adds 34 tokens to every session and 681 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 skills, from other repositories
deep-review
Comprehensive code review — architecture + correctness. Scope with args — diff (default), branch, all, path, or IDE selection. Delegates to reviewer agent for large scope.
review
Review code changes for bugs, security, edge cases. Scope with args — diff (default), branch, commit, path, or IDE selection. Delegates to reviewer agent for large scope.
gh-review-check
Check unresolved PR review comments and summarize findings.
gh-resolve-pr
Fix CI and review findings on a GitHub PR, then report readiness.
gh-review-fix-loop
Fix and resolve GitHub PR review threads.
nightly-audit-engineer-ops
A workflow for reviewing code for reproducible quality problems and recording the evidence behind each finding. It follows data through inputs, permissions, storage, asynchronous work, and recovery, then suggests the smallest authorized fix.