What problem does it solve? Writing Semgrep rules that accurately detect vulnerabilities without false positives or false negatives is difficult, and untested rules erode trust in static analysis results. ## Core Features & Use Cases - Test-First Rule Creation: Enforces writing annotated test files (ruleid/ok) before the rule, then iterating with semgrep --test until all tests pass. - Taint Mode Guidance: Prioritizes taint mode for data flow vulnerabilities, tracking untrusted input from sources to dangerous sinks to reduce false positives. - AST-Based Pattern Design: Uses semgrep --dump-ast to understand how Semgrep parses code, ensuring patterns match syntactic variations. - Use Case: You need to detect all cases where user-controlled request data reaches eval() in a Python codebase. The skill guides you to write a taint mode rule with sources, sinks, and sanitizers, plus tests covering vulnerable, sanitized, and hardcoded-safe cases. ## Quick Start Ask the AI to create a Semgrep rule that detects a specific vulnerability pattern in your language, including a test file with ruleid and ok annotations.