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 agentmods add skills/borhen68/skillengine/code-review-and-qualitynpx skills add borhen68/SkillEngine --skill code-review-and-qualitygit clone --depth 1 https://github.com/borhen68/SkillEngineWhat 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 | $0.00063 | $0.03478 |
| Opus 5 | $0.00032 | $0.01739 |
| Sonnet 5 | $0.00013 | $0.00696 |
| Haiku 4.5 | $0.00006 | $0.00348 |
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 3d 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 — 122 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 — 383 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)
Iron Rules
These target the review failure modes specific to AI agents. Each is absolute.
- No approval without evidence of review. "LGTM, clean implementation" is a rubber stamp. An approval must state what was checked per axis and what was found — including "checked X, found nothing" — or it didn't happen.
- Review the blast radius, not just the diff. Changed lines are where bugs are born; unchanged callers are where they detonate. For every modified public function, type, or contract, trace at least its direct callers before approving.
- Every finding needs location, severity, and a concrete fix. "Consider improving error handling" is noise.
"
src/api/tasks.ts:42— Critical:deleteTaskswallows the DB error and returns 200; propagate and map to 500" is a review. - Verify claims, don't inherit them. "Tests pass" in a PR description is a claim. Check the CI run or run them yourself. Authors — human or agent — report what they believe, not what is true.
- Sycophancy is a review defect. If the approach has a problem, say so directly with reasoning, even when the author is the user. A polite review that withholds a known issue is worse than no review.
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.
- 3d ago First seen · 383 lines · 63 tokens per session scan A e699756f48b7
code-review-and-quality is a skill published in the GitHub repository borhen68/SkillEngine (17 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 3,478 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 122 lines, and is treated as a copy.
Other skills, from other repositories
doubt-driven-development
在每个非平凡决策成立前，用全新上下文进行对抗式审查。当正确性比速度更重要、处理不熟悉代码、风险较高（生产、安全敏感逻辑、不可逆操作），或任何自信输出现在验证比之后调试更便宜时使用。.
test-driven-development
用测试驱动开发。用于实现任何逻辑、修复任何 bug，或改变任何行为。用于需要证明代码能工作、收到 bug 报告，或即将修改现有功能时。.
ci-cd-and-automation
自动化 CI/CD pipeline 设置。用于设置或修改构建和部署 pipeline 时；用于需要自动化质量门禁、在 CI 中配置 test runners，或建立部署策略时。.
code-review-and-quality
执行多维度代码审查。用于合并任何变更之前；用于审查自己、其他 agent 或人类编写的代码；用于在代码进入主分支前从多个维度评估代码质量。.
code-simplification
为清晰度简化代码。用于在不改变行为的前提下重构代码以提升清晰度；用于代码能运行但比应有状态更难阅读、维护或扩展时；用于审查已累积不必要复杂度的代码时。.
context-engineering
优化 agent 上下文设置。当开始新会话、agent 输出质量下降、在任务之间切换，或需要为项目配置规则文件和上下文时使用。.