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.
git clone --depth 1 https://github.com/bambooshadow-studio/mcp-power-packWrote 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/rules/bambooshadow-studio/mcp-power-pack/ai-code-review)<a href="https://agentmods.dev/rules/bambooshadow-studio/mcp-power-pack/ai-code-review"><img src="https://agentmods.dev/badge/rules/bambooshadow-studio/mcp-power-pack/ai-code-review/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/rules/bambooshadow-studio/mcp-power-pack/ai-code-review"><img src="https://agentmods.dev/badge/rules/bambooshadow-studio/mcp-power-pack/ai-code-review.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 | $0.00333 | $0.00333 |
| Opus 5.5 | $0.00133 | $0.00133 |
| Sonnet 5.5 | $0.00067 | $0.00067 |
| Haiku 4.5 | $0.00033 | $0.00033 |
Grade A, and why
ai-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 2mo 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.
What it actually says
Rule Name: ai-code-review Description: Always review code for security, performance, correctness, and maintainability before submitting. Filters:
- file: "*.py"
- file: "*.js"
- file: "*.ts"
- file: "*.jsx"
- file: "*.tsx"
- file: "*.rs"
// CODE REVIEW CHECKLIST // Priority: Security > Performance > Correctness > Maintainability
Security
- Check for injection vulnerabilities (SQL, XSS, command injection)
- Verify authentication and authorization checks
- Look for hardcoded secrets, API keys, passwords
- Validate input sanitization
Performance
- Identify N+1 query patterns
- Check for unnecessary API calls or database queries
- Look for memory leaks in closures and event listeners
- Verify caching opportunities
Correctness
- Check error handling paths (try/catch, error boundaries)
- Verify edge cases (empty states, null/undefined, boundary values)
- Check race conditions in async code
- Validate type safety
Maintainability
- Meaningful variable and function names
- Single responsibility per function
- DRY principle violations
- Adequate documentation for complex logic
Output Format
## Security Issues (X found)
- CRITICAL: [issue] at [file:line]
## Performance Issues (X found)
- HIGH: [issue] at [file:line]
## Correctness Issues (X found)
- HIGH: [issue] at [file:line]
## Maintainability (X found)
- MEDIUM: [issue] at [file:line]
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.
- 2mo ago First seen · 52 lines · 333 tokens per session scan A 2c36af53eb22
ai-code-review is a cursor rule published in the GitHub repository bambooshadow-studio/mcp-power-pack (1 stars, last pushed 4mo ago), licensed MIT. It adds 333 tokens to every session, about $0.0013 per session on Opus 5.5. 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 cursor rules, from other repositories
refactoring
Refactoring: systematic approach, extract/inline, guard clauses, early returns.
clean-code
Clean code: naming, functions, simplicity.
code-review
Code review: reviewing approach, authoring PRs, feedback conventions.
git-workflow
Git workflow: commits, branches, PRs, history management.
ai-dev-os-check
Checks code changes against AI Dev OS guidelines and automatically fixes violations. Based on benchmark Test 011 — check+fix achieves +9.9 improvement vs report-only (+0.8). Supports git diff (default), staged changes, and branch comparison. Use --dry-run for report-only mode.
ai-dev-os-review
Performs a comprehensive self-review before creating a PR. Combines guideline compliance checking (L3) with design-level review (L2) and philosophical alignment (L1). Unlike @ai-dev-os-check which only checks rules, this also evaluates architecture decisions and code design quality.