sg-scout

To extract code audit and debugging techniques from GitHub repositories, you scan public and private repos to parse READMEs and locate SKILL.md definitions. This surfaces structured data fields and scored improvement proposals for benchmarking against peers.

Building a techniques library from public GitHub repos involves scanning repositories to extract structured data fields like name, description, and techniques. This process populates a living reference library by scoring proposals and benchmarking against similar tools.

Yes, you can use GitHub intelligence to find improvement proposals for code audit workflows by scanning repositories for evaluation and benchmarking patterns. Extracted findings are filed as structured proposals to feed self-improving workflows.

To benchmark your code audit tool against similar GitHub repositories, scan public repos for visual-regression and debugging techniques. The process scores extracted techniques and produces library reports comparing peer implementations.

During a GitHub scan, you can extract structured data fields including name, description, and techniques from SKILL.md definitions. Parsing these fields generates scored proposals and populates a living techniques library for future reference.

GitHub reconnaissance works for both public and private repositories to discover evaluation, benchmarking, and auditing techniques. Scanning private repos extracts actionable intelligence and surfaces improvement proposals for debugging workflows.