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/mdgrey33/claude_mind/code-reviewnpx skills add MDGrey33/claude_mind --skill code-reviewgit clone --depth 1 https://github.com/MDGrey33/claude_mindWrote 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/mdgrey33/claude_mind/code-review)<a href="https://agentmods.dev/skills/mdgrey33/claude_mind/code-review"><img src="https://agentmods.dev/badge/skills/mdgrey33/claude_mind/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 | $0.00019 | $0.01990 |
| Opus 5 | $0.00010 | $0.00995 |
| Sonnet 5 | $0.00004 | $0.00398 |
| Haiku 4.5 | $0.00002 | $0.00199 |
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 — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Skill
Purpose
Perform thorough code reviews to ensure high-quality, secure, maintainable code before delivery. This skill is invoked automatically when code needs review.
When to Use This Skill
- Reviewing pull requests or code changes
- Validating implementation against requirements
- Ensuring code follows project standards
- Identifying potential bugs or issues
- Checking for security vulnerabilities
Review Process
Step 1: Understand Context
Before reviewing code:
- Read the associated technical specification
- Understand what the code is supposed to do
- Review acceptance criteria
- Check related architecture decisions (ADRs)
Step 2: Code Quality Review
Readability
- Code is well-formatted and consistent
- Variable and function names are descriptive
- Complex logic is commented
- No dead code or commented-out code
- Consistent indentation and style
Structure
- Functions are focused and do one thing
- Files are appropriately sized (<500 lines)
- Appropriate separation of concerns
- Clear module boundaries
- Proper use of abstraction
Error Handling
- All error cases handled
- Errors logged appropriately
- User-friendly error messages
- No silent failures
- Proper exception types used
Step 3: Security Review
Input Validation
- All user inputs validated
- Validation happens server-side (not just client)
- SQL injection prevention (parameterized queries)
- XSS prevention (output encoding)
- CSRF protection where needed
Authentication & Authorization
- Authentication required for protected endpoints
- Authorization checks proper permissions
- No hardcoded credentials
- Secure password handling (hashing, not plaintext)
- Session management secure
Data Protection
- Sensitive data encrypted at rest
- Sensitive data encrypted in transit (HTTPS)
- No sensitive data in logs
- Proper data access controls
- No exposure of internal details in errors
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 · 343 lines · 19 tokens per session scan A 628cf4104f3e
code-review is a skill published in the GitHub repository MDGrey33/claude_mind (2 stars, last pushed 8mo ago), licensed MIT. It adds 19 tokens to every session and 1,990 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…