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 fco3lho/unlazier-ai --skill code-reviewgit clone --depth 1 https://github.com/fco3lho/unlazier-aiWrote 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/fco3lho/unlazier-ai/code-review)<a href="https://agentmods.dev/skills/fco3lho/unlazier-ai/code-review"><img src="https://agentmods.dev/badge/skills/fco3lho/unlazier-ai/code-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/fco3lho/unlazier-ai/code-review"><img src="https://agentmods.dev/badge/skills/fco3lho/unlazier-ai/code-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 | $0.00049 | $0.00571 |
| Opus 5 | $0.00024 | $0.00285 |
| Sonnet 5 | $0.00010 | $0.00114 |
| Haiku 4.5 | $0.00005 | $0.00057 |
Grade A, and why
code-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 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Apply this structured review to every file changed in the diff or opened for review.
1. Simplicity Check (rules/structure.md)
- Each function does one thing? (SRP)
- No unnecessary abstractions? (no Strategy pattern for one-line math)
- Fits on one screen? (< 40 lines per function)
2. Completeness Check (rules/completeness.md)
- Any
// TODO,FIXME,XXX,pass,NotImplementedError? - Any
console.log,print(),debuggerstatements? - Any unused imports or variables introduced by these changes?
- Any commented-out code or unreachable branches introduced by these changes?
- Any pre-existing dead code that should be flagged to the user?
- Every
switch/matchhas adefault/exhaustive case? - Parallel
if/elsebranches (non-guard-clauses) handle all paths?
3. Correctness Check (rules/correctness.md)
- All error paths handled? (no
except: pass, no empty catch) - Edge cases covered? (null, empty, zero, boundary values)
- Inputs validated?
- Security risks? (injection, hardcoded secrets)
4. Style Check (rules/style.md)
- Matches existing project style?
- Formatter would pass? (Prettier, ruff, gofmt, etc.)
- Imports at top?
5. Naming Check (rules/naming.md)
- Names meaningful and in English? (
calculate_totalnotcalc) - Boolean prefixes? (
is_,has_,should_) - Language-idiomatic? (snake_case for Python, camelCase for JS)
6. Performance Check (rules/performance.md)
- N+1 queries in loops?
- Unnecessary allocation inside loops?
- Eager loading when lazy would do?
7. Testability Check (rules/testability.md)
- Logic testable without excessive mocking?
- Side effects isolated?
8. Async Check (rules/async.md)
- Every promise/async handled? No fire-and-forget?
- Resources cleaned up? (connections, handles, streams)
9. Documentation Check (rules/documentation.md)
- Comments explain why, not what?
- No obvious comments for self-explanatory code?
- New modules or projects include execution instructions?
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 · 66 lines · 49 tokens per session scan A 9d726ebefa47
code-review is a skill published in the GitHub repository fco3lho/unlazier-ai (3 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 571 once invoked, about $0.0002 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
open-code-review
Performs AI-powered code review on Git changes using the ocr CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply…
plannotator
Reference for using the Plannotator CLI: plan review, code review, annotating files, URLs, folders, and running local apps, annotating the last assistant message, browsing archived plan decisions, and exporting or sharing Guided Reviews. Invoke when asked to use Plannotator for anything not covered by a more specific…
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.
tech-debt-audit
Thorough, file-cited technical debt audit across 9 dimensions using AST-grep (tree-sitter), grep, LSP, and language-native tooling. Produces TECHDEBTAUDIT.md with severity, effort estimates, and prioritized fixes. Use when asked for codebase health check, tech debt audit, architecture review, code quality assessment…
agtx-review
Self-review completed work. Check for correctness, edge cases, and code quality. Write review to .agtx/review.md and stop.
at-review
Review code changes for bugs, regressions, convention violations, and high-value cleanup opportunities. Use for diffs, commit ranges, hosted PR/MR URLs, branches, paths, staged changes, or working-tree changes.