Security audit
Skill NSBen/skillfoundry/src/skillfoundry/data/starter_skills/security-audit
🔥 SkillFoundry (forge) — open, vendor-neutral toolchain for authoring, validating, testing & publishing AI agent skills. Works across Claude Code / Cursor / Codex / OpenCode / Cline. Ships 15 starter skills.
npx -y skills add NSBen/skillfoundry --skill security-auditAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 14 days oldThe repository was created 14 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Use when reviewing code for common vulnerabilities (SQL injection, XSS, leaked secrets, unsafe eval) and proposing concrete fixes.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
1.2 KB, as published. Nobody here has run it
Security Audit
When to use
Invoke this skill whenever untrusted or security-sensitive code is added, changed, or about to be merged.
Steps
- Enumerate entry points: HTTP handlers, CLI args, deserialization,
eval/exec, file uploads. - Trace user input to dangerous sinks: SQL, HTML output, shell, file paths, regex.
- Report each issue with a CWE id, severity, and a minimal fix.
- Prefer parameterized queries, contextual output encoding, and least privilege.
- Summarize the top risks and the required follow-ups.
Examples
- "Audit this endpoint for SQL injection"
- "Find hardcoded API keys in the repo"
References
Anchor findings in the OWASP Top 10; avoid noisy false positives by confirming a real data flow from source to sink.