What problem does it solve? Writing accurate static-analysis security rules by hand is slow and error-prone, and generic rules produce false positives because they ignore a project's actual sanitizers and coding patterns. This Skill turns a Stage 1 code recon report (CODE_RECON.md) and a known-issues register into tailored, low-false-positive Opengrep rules with test files and validation logs. ## Core Features & Use Cases - Four input modes: interactive interview, KNOWN_ISSUES.md-driven, autonomous batch from recon hot spots, and tailored single-rule mode for a specific CVE or library. - Rule quality pipeline: builds a prioritized rule queue, writes one YAML rule per file with full CWE/OWASP/CVSSv3 metadata, generates four-type test files (vulnerable and safe cases), and validates with the opengrep CLI or a static-review fallback. - Use Case: After a pentest flags SQL injection and prototype pollution findings, record them in KNOWN_ISSUES.md, then run the skill to produce validated taint-mode rules, test fixtures, and a RULES_INDEX.md coverage map ready for CI scanning. ## Quick Start Ask the AI to create opengrep rules for your project directory using the CODE_RECON.md produced by the code recon stage, choosing interview or autonomous mode.