Decoupled AI Engineering Workflow with pdlc-skills

说明与用途

pdlc-skills 框架通过磁盘上的共享状态文件解耦了图、提示和循环层，而不是直接调用。这使得可以在单独的 git worktree 中并行处理独立功能，并由人类监督最终发布决策。

问题

Traditional AI engineering pipelines often suffer from tight coupling between logic layers, making them fragile and difficult to parallelize. There is a need for a system where the graph (stage order), prompt (model interaction), and loop (iteration control) layers are decoupled to allow for robust, parallel feature development without direct inter-layer calls.

结果

The pdlc-skills framework successfully decouples the three engineering layers (Graph, Prompt, Loop) by using a shared state file on disk rather than direct function calls. This architecture enables parallel processing of independent features in separate git worktrees, with clear separation between automated iteration and human shipping decisions.

工作流

• Lead agent or human splits overall design into N mutually independent features.
• Outer script starts an inner loop for each feature in its own git worktree.
• Graph layer checks preconditions (e.g., requirements exist) at stage boundaries.
• Prompt layer executes the specific command (e.g., write requirements, design, tests) using shared rules.
• Command writes results and metadata to a feature-specific state file on disk.
• Loop layer reads the state file to decide whether to continue iterating, stop, or block.
• Inner loops run in parallel until each reaches 'review complete' or 'blocked'.
• Human collects results from all N features and decides on shipping.

证据

The three layers never call each other, not once. They hand off through a single file on disk.

来源

The loop's stop decision reads the first box [command results] and nothing else. Which means the model's self-assessment is excluded from the stop path.

来源

Each feature's inner loop runs in its own git worktree, out of everyone's way, with artifacts going through separate PRs.

来源