Borrowing it
Nothing to install: this file belongs to systemowiec/ai-agents-workspace-starter. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/systemowiec/ai-agents-workspace-starter/main/.agents/skills/security-audit/SKILL.mdgit clone --depth 1 https://github.com/systemowiec/ai-agents-workspace-starterWrote 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/systemowiec/ai-agents-workspace-starter/security-audit)<a href="https://agentmods.dev/skills/systemowiec/ai-agents-workspace-starter/security-audit"><img src="https://agentmods.dev/badge/skills/systemowiec/ai-agents-workspace-starter/security-audit/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/skills/systemowiec/ai-agents-workspace-starter/security-audit"><img src="https://agentmods.dev/badge/skills/systemowiec/ai-agents-workspace-starter/security-audit.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 | $0.00041 | $0.00885 |
| Opus 5 | $0.00020 | $0.00443 |
| Sonnet 5 | $0.00008 | $0.00177 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
security-audit 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 9d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Audit
Overview
Structured checklist for identifying and fixing security vulnerabilities, prioritized by severity.
When to Use
- Auditing new or changed code for security vulnerabilities
- Reviewing authentication or authorization logic
- Checking for hardcoded secrets before commit
- Assessing API endpoints for input validation gaps
- Preparing for security review or penetration test
When NOT to use:
- Adding security theater without real benefit
- Large security architecture redesigns (that's an architect task)
- Performance optimization (use
performance-audit)
Checklist
CRITICAL (fix immediately)
- No hardcoded secrets, API keys, or passwords in code
- No SQL injection (all queries parameterized)
- No command injection (no user input in shell commands)
- No path traversal (user input not used in file paths)
- No sensitive data exposed in logs or error messages
- All sensitive endpoints require authentication
- Authorization checks prevent users accessing others' data
- No insecure deserialization of untrusted data
- No SSRF risks (validate/restrict outbound URLs)
HIGH
- No XSS vulnerabilities (output properly escaped/sanitized)
- CSRF protection on state-changing endpoints
- No insecure direct object references (use indirect refs or authz checks)
- Rate limiting on login, registration, and sensitive endpoints
- Passwords properly hashed (bcrypt/argon2, never plaintext/MD5/SHA)
- All user input validated (type, length, format)
- Session management secure (HttpOnly, Secure, SameSite cookies)
- Security headers present (CSP, X-Frame-Options, X-Content-Type-Options)
- CORS restricted to known origins (not
*)
MEDIUM
- Error responses don't expose stack traces or internal details
- Security events logged (failed logins, privilege changes)
- No outdated dependencies with known CVEs
- Strong random number generation for security tokens (
secretsmodule) - Timeout configured on external API calls
- Input length limits to prevent DoS
- File uploads validated (type, size, content)
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.
- 9d ago First seen · 114 lines · 41 tokens per session scan A 6df22a47baa0
security-audit is a skill published in the GitHub repository systemowiec/ai-agents-workspace-starter (2 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 885 once invoked, about $0.0002 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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