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/jakesterns/agent-skills/code-reviewnpx skills add jakesterns/agent-skills --skill code-reviewgit clone --depth 1 https://github.com/jakesterns/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/jakesterns/agent-skills/code-review)<a href="https://agentmods.dev/skills/jakesterns/agent-skills/code-review"><img src="https://agentmods.dev/badge/skills/jakesterns/agent-skills/code-review.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 | $0.00017 | $0.00735 |
| Opus 5 | $0.00009 | $0.00367 |
| Sonnet 5 | $0.00003 | $0.00147 |
| Haiku 4.5 | $0.00002 | $0.00073 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Review code changes for quality, correctness, style, and potential issues. Provide clear, actionable feedback that helps the author improve their code.
Inputs
- Target: A diff, pull request, set of changed files, or specific code selection
- Context (optional): Project coding standards, language-specific conventions, or reference documents to apply during review
Steps
-
Identify the scope — Determine which files and changes are under review. If reviewing a PR or diff, read all changed files. If reviewing a selection, focus on the highlighted code and its immediate context.
-
Understand intent — Before critiquing, understand what the code is trying to accomplish. Read commit messages, PR descriptions, or surrounding code for context.
-
Review for correctness — Check for:
- Logic errors and off-by-one mistakes
- Unhandled edge cases and error conditions
- Race conditions or concurrency issues
- Incorrect assumptions about inputs or state
-
Review for security — Check for:
- Injection vulnerabilities (SQL, XSS, command injection)
- Improper input validation at system boundaries
- Hardcoded secrets or credentials
- Insecure defaults or configurations
-
Review for quality — Check for:
- Code clarity and readability
- Unnecessary complexity or over-engineering
- Duplicated logic that should be consolidated
- Naming that accurately describes purpose
- Consistent style with the surrounding codebase
-
Review for performance — Check for:
- Unnecessary allocations or computations in hot paths
- N+1 query patterns or unbounded data fetching
- Missing indexes or inefficient data structures
- Resource leaks (connections, file handles, memory)
-
Review for maintainability — Check for:
- Breaking changes to public APIs
- Missing or outdated tests for changed behavior
- Tight coupling that will make future changes harder
- Dead code or unused imports introduced by the change
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 · 84 lines · 17 tokens per session scan A a9dab55e88fa
code-review is a skill published in the GitHub repository jakesterns/agent-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 735 once invoked, about $0.0001 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…