fastxyz/skill-optimizer

Skill-optimizer addresses a critical bottleneck in agentic workflows: the quality and token-efficiency of tool definitions. While the problem is real, the project currently lacks a defensible moat. With only 12 stars and minimal community engagement (1 fork) over 39 days, it is a nascent utility rather than a platform. Technically, it functions as a specialized prompt-optimizer or a 'DSPy for tools,' which is a pattern easily replicated by more established observability frameworks like LangSmith or Arize Phoenix. Furthermore, Anthropic (the creator of MCP) and OpenAI have a direct incentive to build these optimization loops natively into their developer consoles to reduce friction for their respective agent ecosystems. The project's relevance is highly tied to the 'Model Context Protocol' (MCP) trend, but it is likely to be superseded by official tooling or broader agent-evaluation suites within 6 months.