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 kevinnft/ai-agent-skills --skill code-review-and-qualitygit clone --depth 1 https://github.com/kevinnft/ai-agent-skillsWrote 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/kevinnft/ai-agent-skills/code-review-and-quality)<a href="https://agentmods.dev/skills/kevinnft/ai-agent-skills/code-review-and-quality"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/code-review-and-quality/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/kevinnft/ai-agent-skills/code-review-and-quality"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/code-review-and-quality.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 | $0.00051 | $0.03083 |
| Opus 5 | $0.00026 | $0.01541 |
| Sonnet 5 | $0.00010 | $0.00617 |
| Haiku 4.5 | $0.00005 | $0.00308 |
Grade A, and why
code-review-and-quality 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 11d 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.
This is a copy
80% identical to code-review-and-quality — 62 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review and Quality
Overview
Multi-dimensional code review with quality gates. Every change gets reviewed before merge — no exceptions. Review covers five axes: correctness, readability, architecture, security, and performance.
The approval standard: Approve a change when it definitely improves overall code health, even if it isn't perfect. Perfect code doesn't exist — the goal is continuous improvement. Don't block a change because it isn't exactly how you would have written it. If it improves the codebase and follows the project's conventions, approve it.
When to Use
- Before merging any PR or change
- After completing a feature implementation
- When another agent or model produced code you need to evaluate
- When refactoring existing code
- After any bug fix (review both the fix and the regression test)
The Five-Axis Review
Every review evaluates code across these dimensions:
1. Correctness
Does the code do what it claims to do?
- Does it match the spec or task requirements?
- Are edge cases handled (null, empty, boundary values)?
- Are error paths handled (not just the happy path)?
- Does it pass all tests? Are the tests actually testing the right things?
- Are there off-by-one errors, race conditions, or state inconsistencies?
2. Readability & Simplicity
Can another engineer (or agent) understand this code without the author explaining it?
- Are names descriptive and consistent with project conventions? (No
temp,data,resultwithout context) - Is the control flow straightforward (avoid nested ternaries, deep callbacks)?
- Is the code organized logically (related code grouped, clear module boundaries)?
- Are there any "clever" tricks that should be simplified?
- Could this be done in fewer lines? (1000 lines where 100 suffice is a failure)
- Are abstractions earning their complexity? (Don't generalize until the third use case)
- Would comments help clarify non-obvious intent? (But don't comment obvious code.)
- Are there dead code artifacts: no-op variables (
_unused), backwards-compat shims, or// removedcomments?
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.
- 11d ago First seen · 353 lines · 51 tokens per session scan A d0c1e7224515
code-review-and-quality is a skill published in the GitHub repository kevinnft/ai-agent-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 3,083 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to code-review-and-quality, differing in 62 lines, and is treated as a copy.
Other skills, from other repositories
tactical-ddd
Design, refactor, analyze, and review code by applying the principles and patterns of tactical domain-driven design. Triggers on: domain modeling, aggregate design, 'entity', 'value object', 'repository', 'bounded context', 'domain event', 'domain service', code touching domain/ directories, rich domain model…
nasde-benchmark-calibration
Calibrate assessment rubrics by reviewing agent work in GitHub/GitLab PRs and feeding human comments back into the rubric. Use this skill when the user wants to: Calibrate, tune, or sanity-check assessment criteria / dimensions of a benchmark Review trial diffs alongside the LLM-as-a-Judge scores in a PR/MR…
frontend-quality-reviewer
Review frontend quality from evidence across code, UI, architecture, TypeScript, security, performance, verification, decomposition, UX, and anti-slop concerns. Returns pass, concerns, or fail without implementing unrequested fixes.
pattern-library-manager
Add or tighten reusable approved patterns and anti-patterns in common/ with source-backed examples, approvals, graph links, evals, and validation. Keep project-specific patterns in local-only project/.
common-law-quality-gate
Fertig erstelltes Common-Law-Arbeitsprodukt auf Qualitaet prüfen: Jurisdiktion Quellenstand False Friends UK/US-Trennung Review-Bedarf. Prüfraster Jurisdiktion-Konsistenz Normen-Aktualitaet False-Friends-Scan UK-US-Trennung. Output Qualitaets-Prüfbericht Lueckenliste. Abgrenzung zu common-law-false-friends-scanner…
plumb-line-audit
Use when auditing a diff or repository against the plumb-line principles — finds laundered uncertainty, boundary leaks, hardcoded priors, overstated maturity, outputs lacking recorded lineage, and baseline drift with no explanation. Read-only: it reports, never auto-fixes.