e61baf7e4e
Comprehensive GSD analysis: 15 sections covering core philosophy (fresh context per agent), 5 methodologies (dream extraction, goal-backward verification, nyquist validation, wave execution, checkpoints), full command reference (37+), agent system (16 agents with model routing), config system, git integration, state management, session continuity, community best practices, pitfalls, framework comparison (GSD vs ECC vs BMAD vs SpecKit), and 4 detailed practical examples (new project, brownfield, debugging, quick tasks). Three zettelkasten notes: context rot vs window isolation tradeoffs, goal-backward vs forward verification, plans-as-prompts design pattern.
28 lines
1.1 KiB
Markdown
28 lines
1.1 KiB
Markdown
---
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created: "2026-03-20 10:03"
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type: zettel
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tags: [prompt-engineering, ai-architecture, gsd]
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source: "https://github.com/gsd-build/get-shit-done"
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---
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# Plans as Prompts 设计模式
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GSD 中 PLAN.md 不是"被转化为 prompt 的文档"——它**就是** prompt。XML 结构（`<task>`, `<action>`, `<verify>`, `<done>`）直接指导执行器的行为。
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这个设计消除了"文档到 prompt 的翻译损失": 传统方式需要一个中间步骤把文档理解为指令，每次翻译都引入歧义。Plans as Prompts 让计划者直接写执行指令，跳过翻译。
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关键约束使这成为可能:
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- 每个计划 ≤ 50% 上下文预算（确保执行器有足够空间思考）
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- XML 结构强制精确性（不是自然语言的模糊描述）
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- `<verify>` 块要求每个任务都有可执行的验证命令
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- `<done>` 块定义明确的完成状态
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更广泛的启示: 当 AI Agent 是执行者时，规划文档应该以 Agent 的"母语"（结构化 prompt）书写，而非以人类的可读性为优先。
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---
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## Related
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- [[GSD 方法论与最佳实践]]
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- [[目标回溯验证vs正向任务检查]]
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