实验假设Hypothesis
AI 开发要进入真实交付，关键不是更多代码生成，而是可观察的流程、人工审批和质量门。For AI development to reach real delivery, the key is not more code generation but an observable process, human approval and quality gates.
方法Method
- 把需求到部署拆成明确阶段Split the path from requirement to deployment into explicit stages
- 集中展示运行、阻塞、审批和成本状态Show runs, blockers, approvals and cost in one place
- 通过 Skills 与 MCP 装配可复用能力Assemble reusable capabilities through Skills and MCP
当前观察Current observation
公开仓库已经实现本地控制台、项目工作区、运行监控和生产健康检查等核心界面与流程。The public repository already implements the core screens and flows: a local console, project workspaces, run monitoring and production health checks.
限制与下一步Limits and next steps
- 目前主要面向本地单人工作流It currently targets a local, single-person workflow
- 仍需要更多真实项目验证长期记忆与恢复机制Long-term memory and recovery still need validation on more real projects