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-security-review)<a href="https://agentmods.dev/rules/bambooshadow-studio/mcp-power-pack/ai-security-review"><img src="https://agentmods.dev/badge/rules/bambooshadow-studio/mcp-power-pack/ai-security-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-security-review"><img src="https://agentmods.dev/badge/rules/bambooshadow-studio/mcp-power-pack/ai-security-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 | $0.00332 | $0.00332 |
| Opus 5 | $0.00166 | $0.00166 |
| Sonnet 5 | $0.00066 | $0.00066 |
| Haiku 4.5 | $0.00033 | $0.00033 |
Grade A, and why
ai-security-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 8d 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-security-review Description: Perform security review for any code that handles user input, authentication, database queries, or external API calls. Filters:
- file: "*.py"
- file: "*.js"
- file: "*.ts"
- file: "*.rs"
- file: "*.go"
// SECURITY REVIEW GUIDE
Critical Checks
- SQL/NoSQL injection in all database queries
- Cross-site scripting (XSS) in rendered output
- Cross-site request forgery (CSRF) in state-changing endpoints
- Server-side request forgery (SSRF) in URL fetch operations
- Path traversal in file operations
- Insecure direct object references (IDOR)
Authentication & Authorization
- Verify all authenticated endpoints have proper auth checks
- Check role-based access control (RBAC) is enforced server-side
- Ensure session tokens are properly validated
- JWT: verify signature, expiration, and claims
Secrets Management
- No hardcoded API keys, passwords, or tokens
- No secrets in client-side code
- Use environment variables or secret managers
- Check .gitignore for secrets files
Data Protection
- Encrypt sensitive data in transit (HTTPS/TLS)
- Hash passwords with bcrypt/argon2 (not plain SHA)
- Validate and sanitize all user input
- Implement rate limiting on auth endpoints
Output Format
## Security Findings
### CRITICAL: [finding]
**File:** [path:line]
**Risk:** [what attacker can do]
**Fix:** [specific remediation steps]
### HIGH: [finding]
...
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.
- 8d ago First seen · 50 lines · 332 tokens per session scan A 410be0e64668
ai-security-review is a cursor rule published in the GitHub repository bambooshadow-studio/mcp-power-pack (1 stars, last pushed 2mo ago), licensed MIT. It adds 332 tokens to every session, about $0.0017 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.
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pr-code-review
Code review rule for PR analysis. Challenges architectural decisions, examines the diff with the default branch, and provides comprehensive feedback on code quality, security, performance, and maintainability before publishing.
git-authoring
Conventional Commit, pull-request, release-note, and PR-review conventions for this repository.
core
The engineering contract for this repo lives in AGENTS.md (auto-loaded). Follow it. This rule only adds Cursor-specific behavior so the contract is not duplicated token-for-token on every request.
frontend
UI/component conventions. Apply when building or changing user-facing frontend code.
debugging
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