Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Security-focused code review for hardcoded secrets, dangerous calls, and common vulnerabilities
Security-focused code review for hardcoded secrets, dangerous calls, and common vulnerabilities
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Security-focused code review of project source code. Covers OWASP-style vulnerabilities, hardcoded secrets, dangerous function calls, and patterns relevant to AI-assisted development.
Run the auditor against the target path: python3 "$SKILL_DIR/scripts/audit_code.py" "$ARGUMENTS" If $ARGUMENTS is empty, default to $PROJECT_ROOT.
Hardcoded secrets -- API keys (AWS, GitHub, Stripe, OpenAI, Slack), tokens, private keys, connection strings, passwords Dangerous function calls -- eval, exec, subprocess with shell=True, child_process.exec, pickle deserialization, system(), gets(), etc. SQL injection -- String concatenation/interpolation in SQL queries Dependency risks -- Known hallucinated package names, unverified installations Sensitive files -- .env files committed to git, credential files in repo File permissions -- Overly permissive chmod patterns Exfiltration patterns -- Base64 encode + network send, DNS exfiltration, credential file reads
Structured report with severity-ranked findings, file locations, and actionable remediation steps.
Before committing or pushing code When reviewing third-party contributions or PRs As part of a periodic security audit of the codebase After AI-assisted code generation to verify no secrets or vulnerabilities were introduced
Identity, auth, scanning, governance, audit, and operational guardrails.
Largest current source with strong distribution and engagement signals.