I Built a Claude Code Skill That Runs a Full-Stack Security Audit in One Command

A developer released an open-source Claude Code skill, toshipon/claude-code-security-audit-skill, that runs an 8-phase, evidence-based full-stack security audit from a single command, covering web, infrastructure, backend, mobile, compliance, and LLM/AI layers. Each check ships with the concrete command, grep pattern, or SQL query that produces evidence, and findings map to established frameworks including OWASP Top 10:2025, MASVS v2, CIS Benchmarks, and the OWASP Top 10 for LLM Applications. Reference files load only for the target being audited, keeping SKILL.md concise.

TL;DR: Security audits usually start from a blank page, every single time. I packaged my audit process into an open-source Claude Code skill: run /security-audit all and you get an 8-phase, evidence-based assessment across web, infrastructure, backend, mobile, compliance, and LLM/AI — including the dashboard-only settings that no CLI can reach. It's on GitHub: toshipon/claude-code-security-audit-skill https://github.com/toshipon/claude-code-security-audit-skill AI coding agents now write a large share of our code. Review and security checks have not kept up. Even before AI, my audits had a recurring problem: every engagement started from scratch. The stack spans web, mobile, infrastructure, and backend, and each layer has its own checklist. OWASP's guidance is comprehensive, but it's a mountain of material to re-read each time. And some of the most important settings — Vercel Deployment Protection, Supabase Auth policies — don't exist in any CLI or API. They live behind dashboards. So I stopped treating the audit as a document I rewrite each time, and turned it into a reusable Claude Code skill. Install it from the plugin marketplace, inside Claude Code: /plugin marketplace add toshipon/claude-code-security-audit-skill /plugin install security-audit@toshipon-security-audit Then: /security-audit all full-stack audit recommended /security-audit nextjs just the Next.js app /security-audit supabase just the Supabase backend /security-audit llm just LLM/AI security /security-audit terraform just the IaC The audit runs in 8 phases: | Phase | Scope | How it's checked | |---|---|---| | 1. Reconnaissance | Project structure, trust boundaries, data flow | Glob / Read | | 2. Web application | Next.js Server Actions, Middleware, CSP, known CVEs | Grep / Bash | | 3. Infrastructure | Vercel, Terraform, AWS, GCP, Azure | CLI + Chrome MCP | | 4. Backend | Supabase RLS, Python, Go, Rails, Rust | SQL / Grep | | 5. Mobile | iOS, Android, Flutter, React Native | Grep / Bash | | 6. Compliance | PCI-DSS, HIPAA, SOX, GDPR, CCPA, SOC 2, ISO 27001 | Analysis | | 7. Advanced detection | Supply chain, containers/K8s, CI/CD, secrets, LLM/AI | Grep / Bash | | 8. Cross-layer analysis | Auth flow consistency, token lifecycle, data protection continuity | Analysis | Nothing here invents its own categories. Web checks map to OWASP Top 10:2025 and WSTG, mobile to MASVS v2 / MASTG, infrastructure to CIS Benchmarks, and LLM checks to the OWASP Top 10 for LLM Applications. That matters when you hand the report to someone else: it's written in a language every security reviewer already speaks. The detailed check patterns — more than two dozen reference files covering everything from Next.js to Rust to PCI-DSS — live in a references/ directory and are loaded only when that target is audited. SKILL.md itself stays a concise overview. This was the single most important lesson from Anthropic's own skill best practices and from studying Trail of Bits' skills : a skill that dumps everything into context stops being a tool and becomes noise. Run /security-audit android , and only the Android reference loads. An LLM doing security review has a failure mode: plausible-sounding findings with no proof. So every check in this skill ships with the concrete command, grep pattern, or SQL query that produces the evidence. For example, finding Supabase tables with Row Level Security disabled is not an opinion — it's this query: SELECT n.nspname AS schema, c.relname AS table name FROM pg class c JOIN pg namespace n ON n.oid = c.relnamespace WHERE c.relkind = 'r' AND n.nspname = 'public' AND c.relrowsecurity = false; Every finding in the report carries a file path, a line number, and a concrete remediation. The skill also hard-codes the excuses it must reject — the ones I kept hearing and making in real audits: This is the part most audit automation skips. Some settings simply cannot be checked from a terminal: | Setting | CLI/SQL | Needs dashboard inspection | |---|---|---| | Vercel environment variables | ✅ vercel env ls | — | | Vercel Deployment Protection | — | Toggle state and protection scope | | Supabase RLS status | ✅ SQL query | — | | Supabase Auth settings MFA, email confirmation, rate limits | — | Dashboard only | | Supabase Security Advisor | — | Full lint findings | The skill drives Chrome MCP to open those dashboard pages, extract the setting states, and capture screenshots as evidence. And because a dashboard finding can't be fixed with a code change, the report format says so explicitly and includes the manual remediation steps: HIGH-003 Vercel Deployment Protection Disabled - Current Value: Deployment Protection = OFF - Recommended Value: Deployment Protection = Vercel Authentication - Manual Remediation Steps: 1. Vercel Dashboard → Project Settings → Deployment Protection 2. Select "Standard Protection" 3. Enable "Vercel Authentication" 4. Verify the protection level for Preview Deployments 5. Click "Save" - Impact: Public access to Preview URLs will be restricted. The skill has an explicit When NOT to Use section, because an audit tool that overclaims is worse than one with clear limits: Since the first release, the roadmap has been moving from "one-shot audit" toward a workflow: CVSS v4.0 scoring for findings as an evidence-based assessment workflow, with unscored findings reported explicitly rather than guessed , custom user-defined detection rules, and local continuous monitoring. Auto-fix PRs and external scanner integration Semgrep / CodeQL are in progress. The skill is MIT-licensed and built for the stacks I work on every day as an SRE — which means it is opinionated, and I'd genuinely like to know where those opinions are wrong for your stack. I'm toshipon — an SRE and security-focused indie hacker in Japan, and founder of BUILD GEEKS. I run fixed-scope security audits for teams shipping AI-built software. If your team builds with Claude Code or other AI agents and wants an evidence-based audit — not a generic checklist — DM me on X.