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The author built an autonomous AI system that runs a live product, generating work, quality-gating it, opening pull requests, and self-improving based on analytics. The main challenge was making the system trustworthy rather than making the model smarter.
Introduces SkillCenter, the largest open skill library for AI agents with 216,938 structured, source-grounded skills across 24 domains, built using an automated pipeline with an LLM-based quality gate called SkillGate. The library combines peer-reviewed and community skills, emphasizing traceability and offline searchability.
This article introduces the method of building an industrial-grade Skill from scratch, emphasizing core features such as precise triggering, permission scoping, and evaluable iteration, as well as the importance of constructing scoring criteria, test cases, and quality gate scripts, demonstrating how to implement professional and maintainable skill packages in agent environments like Codex.