Show HN: Evidence-to-Skill – a gate between untrusted sources and agent skills (github.com)

A new project, Evidence-to-Skill, has been introduced to enhance the reliability of AI skills derived from untrusted sources. This workflow transforms unverified material from repositories, manuals, and documentation into compact, attributed AI skills via a series of safety checks and validation steps. Unlike traditional source-to-skill tools that prioritize compression, this approach emphasizes justification and rigorous testing of claims. Only those skills that are traced to credible evidence, linked to a specific failure mode, and cleared for reuse emerge from the process, ensuring that unverified instructions do not get promoted. The significance of Evidence-to-Skill lies in its ability to create a safer AI environment, where the skills utilized by agents are substantiated and scrutinized, rather than blindly accepted. Each promoted skill maintains a clear attribution and audit trail, providing references for verification. This method not only avoids the risk of propagating unsupported claims but also adheres to strict guidelines against automatic code execution and copying of unverified texts. By incorporating evidence-led verification into AI skill development, the project represents a crucial step toward a more responsible and transparent future in AI/ML applications.