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/engineerwithai/engineerwith-agents/code-review-excellencenpx skills add EngineerWithAI/engineerwith-agents --skill code-review-excellencegit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote 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/engineerwithai/engineerwith-agents/code-review-excellence)<a href="https://agentmods.dev/skills/engineerwithai/engineerwith-agents/code-review-excellence"><img src="https://agentmods.dev/badge/skills/engineerwithai/engineerwith-agents/code-review-excellence.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.03190 |
| Opus 5 | $0.00021 | $0.01595 |
| Sonnet 5 | $0.00008 | $0.00638 |
| Haiku 4.5 | $0.00004 | $0.00319 |
Grade A, and why
code-review-excellence 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 yesterday.
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
88% identical to code-review-excellence — 53 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 — 521 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Excellence
Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.
When to Use This Skill
- Reviewing pull requests and code changes
- Establishing code review standards for teams
- Mentoring junior developers through reviews
- Conducting architecture reviews
- Creating review checklists and guidelines
- Improving team collaboration
- Reducing code review cycle time
- Maintaining code quality standards
Core Principles
1. The Review Mindset
Goals of Code Review:
- Catch bugs and edge cases
- Ensure code maintainability
- Share knowledge across team
- Enforce coding standards
- Improve design and architecture
- Build team culture
Not the Goals:
- Show off knowledge
- Nitpick formatting (use linters)
- Block progress unnecessarily
- Rewrite to your preference
2. Effective Feedback
Good Feedback is:
- Specific and actionable
- Educational, not judgmental
- Focused on the code, not the person
- Balanced (praise good work too)
- Prioritized (critical vs nice-to-have)
❌ Bad: "This is wrong."
✅ Good: "This could cause a race condition when multiple users
access simultaneously. Consider using a mutex here."
❌ Bad: "Why didn't you use X pattern?"
✅ Good: "Have you considered the Repository pattern? It would
make this easier to test. Here's an example: [link]"
❌ Bad: "Rename this variable."
✅ Good: "[nit] Consider `userCount` instead of `uc` for
clarity. Not blocking if you prefer to keep it."
3. Review Scope
What to Review:
- Logic correctness and edge cases
- Security vulnerabilities
- Performance implications
- Test coverage and quality
- Error handling
- Documentation and comments
- API design and naming
- Architectural fit
What Not to Review Manually:
- Code formatting (use Prettier, Black, etc.)
- Import organization
- Linting violations
- Simple typos
Review Process
Phase 1: Context Gathering (2-3 minutes)
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.
- yesterday First seen · 521 lines · 42 tokens per session scan A 235ec26133c4
code-review-excellence is a skill published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 42 tokens to every session and 3,190 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to code-review-excellence, differing in 53 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…