Borrowing it
Nothing to install: this file belongs to ahonn/dotfiles. 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/ahonn/dotfiles/master/.claude/skills/self-review/SKILL.mdgit clone --depth 1 https://github.com/ahonn/dotfilesWrote 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/ahonn/dotfiles/self-review)<a href="https://agentmods.dev/skills/ahonn/dotfiles/self-review"><img src="https://agentmods.dev/badge/skills/ahonn/dotfiles/self-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/ahonn/dotfiles/self-review"><img src="https://agentmods.dev/badge/skills/ahonn/dotfiles/self-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 21 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 155 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00025 | $0.01096 |
| Opus 5.5 | $0.00010 | $0.00438 |
| Sonnet 5.5 | $0.00005 | $0.00219 |
| Haiku 4.5 | $0.00003 | $0.00110 |
Grade A, and why
self-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 20d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Review
Review the requested diff against its intended behavior and surrounding contracts. Prioritize correctness, security, and maintainability. Use code-quality for relevant standards; avoid speculative findings or changes that only satisfy a preferred style.
Follow global AGENTS.md for authorization and test mode. PR creation warrants a local review, not an automatic independent-review requirement. Existing permission to implement or fix the work carries into review.
Arguments and scope
--base <branch>: Use the specified base. Otherwise inspect the PR base or repository default branch; verify the ref exists rather than guessing from a shell fallback.--scope branch: Review committed changes relative to the merge base with the selected base.--scope staged: Reviewgit diff --cached.--scope uncommitted: Review staged and unstaged changes, including relevant untracked files.--fix: Apply technically verified fixes within the requested scope.--report-only: Report findings without editing.--peer: Request an independent review of the same scope using available reviewer tools.- Without a fix flag, preserve the session's authorization. A review-only request produces findings; an implementation or fix request permits relevant corrections.
Inspect git status, the current branch, and available refs before selecting scope. Without an explicit scope, cover the requested work's final state: include relevant branch commits and staged, unstaged, or untracked changes that belong to the task. Do not omit a newly implemented fix because earlier work is already committed. Keep unrelated user changes outside the review. An explicitly requested revision or scope limits the review to that selection.
Use the remote-tracking base when current enough for the task; fetch when freshness matters. For committed branch changes, use git diff <base>...HEAD and inspect the associated commits. When the default task scope also includes local work, inspect git diff HEAD and relevant untracked files, then read the combined final implementation. If the requested scope is empty or the base cannot be established, report that limitation rather than silently reviewing another scope.
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.
- 20d ago Changed · -178 lines · -77 tokens per session 083fa7570289
- 1mo ago First seen · 242 lines · 102 tokens per session scan A 6215848ec51f
self-review is a skill published in the GitHub repository ahonn/dotfiles (63 stars, last pushed 6d ago), licensed MIT. It adds 25 tokens to every session and 1,096 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 2026-09-03.
Other skills, from other repositories
scan-commit
Automatically scan the repository for unstaged/untracked changes, group them into logical commits using hunk-level analysis, stage and commit them following Conventional Commits.
smart-commit
Analyze staged changes and split them into multiple logical commits following Conventional Commits.
create-pr
Rebase from the latest origin/main, squash the commits from it, and then create a PR on github with intelligent commit messages based on staged changes.
pr-review
Intelligent PR code review with dynamic agent allocation based on change types. Analyzes PR changes and generates targeted review tasks.
analyze-pr
Analyze a GitHub/GitLab Pull Request by fetching its diff, reading its web description, and generating a comprehensive document. Use when the user provides a PR link and wants to understand what changes the PR introduces, or asks to review/analyze/summarize a pull request.
gen-commit-msg
Generate intelligent commit messages based on staged changes following Conventional Commits format.