Skill Factory Implementation Framework
Key Takeaways
- Skill = folder with SKILL.md (frontmatter + instructions + references)
- Skills follow microservices philosophy: one thing done well, composable
- 7-layer architecture: Infrastructure, Definition, Orchestration, Secure Execution, Observability, Governance, Delivery
- Competitive gap at the “Skill Factory” layer — nobody provides industry-specific customization with enterprise governance
- Real moat: industry know-how + governance experience + hands-on services, not technology
- 技能（Skill）= 包含 SKILL.md（Frontmatter + 指令 + 引用）的文件夹 - 技能遵循微服务理念：专注做好一件事，可组合 - 7层架构：基础设施、定义、编排、安全执行、可观测性、治理、交付 - 竞争差距在于“技能工厂（Skill Factory）”层——尚无人提供具备企业级治理能力的行业特定定制 - 真正的护城河：行业诀窍 + 治理经验 + 贴身服务，而非技术本身
Summary
This research provides a comprehensive technical blueprint for a “Skill Factory” — an enterprise platform for building, testing, securing, and distributing AI agent skills. It establishes the precise SKILL.md definition (three layers: metadata frontmatter, procedural body, reference files) and distinguishes Skills from Tools (single-function APIs), MCP (communication protocol), and Harnesses (runtime constraint layers).
本研究为“技能工厂”——一个用于构建、测试、保障安全及分发 AI 智能体技能的企业平台——提供了一份详尽的技术蓝图。它确立了精确的 SKILL.md 定义（包含三个层级：元数据前置元数据、过程主体、参考文件），并明确区分了技能与工具（单一功能 API）、MCP（通信协议）以及 Harness（运行时约束层）。
The 7-layer architecture spans from Firecracker microVM sandboxing through skill definition and orchestration to governance/compliance and delivery. The real competitive moat lies not in technology but in accumulated industry know-how, enterprise governance expertise, and hands-on deployment services.
该七层架构涵盖从 Firecracker microVM 沙箱隔离，到技能定义与编排，再到治理/合规与交付。真正的竞争护城河不在于技术，而在于积累的行业诀窍、企业治理专长以及实战部署服务。