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Skill Factory: 7-Layer Implementation Framework

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Skill Factory: 7-Layer Implementation Framework

Key Takeaways

关键要点
  • 7-layer architecture: Infrastructure → Definition → Orchestration → Execution → Observability → Governance → Delivery
  • Progressive disclosure: ~100 tokens metadata, <5000 tokens body, on-demand references
  • 6-phase build workflow: Spec → Scaffold → Implement → Test → Document → Publish
  • SKILL.md specification: Standardized format for skill definition and documentation
  • Multi-platform compatibility: Write once, deploy to 20+ agent platforms
- **7 层架构**：Infrastructure → Definition → Orchestration → Execution → Observability → Governance → Delivery - **渐进式披露**：约 100 tokens 元数据，少于 5000 tokens 主体，按需引用 - **6 阶段构建工作流**：Spec → Scaffold → Implement → Test → Document → Publish - **SKILL.md 规范**：用于 Skill 定义和文档编写的标准化格式 - **多平台兼容性**：一次编写，即可部署到 20+ 个 Agent 平台

Summary

摘要

The Skill Factory framework provides a complete architecture for building, testing, and distributing agent skills at enterprise scale. The 7-layer model separates concerns:

Skill Factory 框架提供了一套完整的企业级架构，用于构建、测试和分发 Agent Skill。该 7 层模型实现了关注点分离：

Layer 1 - Infrastructure: Runtime environments, sandboxing, resource limits Layer 2 - Skill Definition: SKILL.md spec, metadata schema, versioning Layer 3 - Orchestration: Skill composition, dependency resolution, execution planning Layer 4 - Secure Execution: Permission enforcement, audit logging, rollback mechanisms Layer 5 - Behavioral Observability: OpenTelemetry integration, performance metrics, error tracking Layer 6 - Governance: Approval workflows, compliance checks, access control Layer 7 - Delivery: Registry APIs, package management, update distribution

**Layer 1 - Infrastructure**：运行时环境、沙箱机制、资源限制 **Layer 2 - Skill Definition**：SKILL.md 规范、元数据 schema、版本控制 **Layer 3 - Orchestration**：Skill 组合、依赖解析、执行规划 **Layer 4 - Secure Execution**：权限强制执行、审计日志、回滚机制 **Layer 5 - Behavioral Observability**：OpenTelemetry 集成、性能指标、错误追踪 **Layer 6 - Governance**：审批流程、合规检查、访问控制 **Layer 7 - Delivery**：Registry APIs、包管理、更新分发

The progressive disclosure principle ensures skills remain token-efficient: metadata fits in ~100 tokens for discovery, full body under 5000 tokens for execution, with detailed references loaded only when needed.

渐进式披露原则确保 Skill 保持 Token 高效：元数据（Metadata）占用约 100 个 Token 用于发现，完整执行体控制在 5000 个 Token 以内，而详细参考信息仅在需要时加载。

The 6-phase build workflow standardizes skill development:

  1. Spec: Define requirements and interface
  2. Scaffold: Generate boilerplate from templates
  3. Implement: Write core logic with security boundaries
  4. Test: Validate behavior in sandbox environments
  5. Document: Generate SKILL.md with examples
  6. Publish: Push to registry with semantic versioning
6 阶段构建工作流标准化了 Skill 开发流程： 1. **Spec**：定义需求与接口 2. **Scaffold**：基于模板生成脚手架代码 3. **Implement**：编写具备安全边界的核心逻辑 4. **Test**：在沙盒环境中验证行为 5. **Document**：生成包含示例的 SKILL.md 6. **Publish**：遵循语义化版本控制推送到 Registry

Relevant Concepts

相关概念

关系图谱

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