This coding-agent skill automates multi-phase security audits by orchestrating isolated sub-agents through a structured workflow that Cloudflare used to seed its vulnerability discovery harness. It executes six phases - reconnaissance, coverage-led hunting, candidate validation, structured output, independent record verification, and target-neutral reporting - producing machine-readable artifacts such as architecture.md, coverage-ledger.json, findings.json (validated against report-schema.json), and human-facing reports like REPORT.md and FINDINGS-DETAIL.md. Validators are provided as zero-dependency Node.js scripts (validate-coverage-ledger.cjs and validate-findings.cjs) and the system classifies results as confirmed, needs_validation, or rejected. The skill runs incrementally and additively, reuses prior ledgers and findings to focus on gaps and revalidate changed sources, and activates automatically for natural audit triggers or via a Skills CLI installation.
The design enforces adversarial validation (a different agent verifies each finding), confirms only source-grounded boundary failures, and ties severity to impact rather than checklist deviations; defense-in-depth absences are treated as hardening notes. Requirements include a coding model that supports tool use and parallel agents, Node.js for validators, and a strict OS-enforced sandbox for executing target-controlled artifacts; without sandboxing unresolved leads remain as needs_validation. Documentation covers hunting classes across web, cloud, supply chain, native, and LLM targets, and the project is MIT-licensed with contact information for security AI research.
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