Use when a phase has unresolved decisions before planning and no CONTEXT.md exists. Asks adaptive questions on the phase's gray areas and writes {phase_num}-CONTEXT.md with locked decisions; --auto, --chain, --power change the questioning.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
How it works:
Output: {phase_num}-CONTEXT.md — decisions clear enough that downstream agents can act without asking the user again
</objective>
<execution_context> @$HOME/.claude/get-shit-done/workflows/discuss-phase.md @$HOME/.claude/get-shit-done/workflows/discuss-phase-assumptions.md @$HOME/.claude/get-shit-done/workflows/discuss-phase-power.md @$HOME/.claude/get-shit-done/templates/context.md </execution_context>
<runtime_note>
Copilot (VS Code): Use vscode_askquestions wherever this workflow calls AskUserQuestion. They are equivalent — vscode_askquestions is the VS Code Copilot implementation of the same interactive question API.
</runtime_note>
Context files are resolved in-workflow using init phase-op and roadmap/state tool calls.
</context>
If DISCUSS_MODE is "assumptions": Read and execute @$HOME/.claude/get-shit-done/workflows/discuss-phase-assumptions.md end-to-end.
If DISCUSS_MODE is "discuss" (or unset, or any other value): Read and execute @$HOME/.claude/get-shit-done/workflows/discuss-phase.md end-to-end.
MANDATORY: The execution_context files listed above ARE the instructions. Read the workflow file BEFORE taking any action. The objective and success_criteria sections in this command file are summaries — the workflow file contains the complete step-by-step process with all required behaviors, config checks, and interaction patterns. Do not improvise from the summary. </process>
<success_criteria>
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Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents with tracing, configuring LightningStore, implementing reward functions, or optimizing prompts with RL/APO algorithms.
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Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output quality.
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Create AI marketing videos for ads, promos, product launches, and brand content. Models: Veo, Seedance, Wan, FLUX for visuals, Kokoro for voiceover. Types: product demos, testimonials, explainers, social ads, brand videos. Use for: Facebook ads, YouTube ads, product launches, brand awareness. Triggers: marketing video, ad video, promo video, commercial, brand video, product video, explainer video, ad creative, video ad, facebook ad video, youtube ad, instagram ad, tiktok ad, promotional video, launch video
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Use when building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.
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Your AI research and engineering brain trust. 59 named personas across 8 cells covering frontier labs, applied product, model architecture, reasoning/RL/agents, alignment and interpretability, theory and science of DL, multimodal and…
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Use when designing a new REST or GraphQL API, reviewing an API spec before implementation, setting team API standards, or migrating REST to GraphQL. Covers resources, HTTP semantics, pagination, error handling, and pitfalls.
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