Use when the user wants to execute a phase's plans, resume the remaining plans, or run only one wave. Discovers plans, groups them into dependency waves, spawns a fresh subagent per plan, then verifies the phase.
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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。
Orchestrator stays lean: discover plans, analyze dependencies, group into waves, spawn subagents, collect results. Each subagent loads the full execute-plan context and handles its own plan.
Optional wave filter:
--wave N executes only Wave N for pacing, quota management, or staged rolloutFlag handling rule:
$ARGUMENTS$ARGUMENTS, treat it as inactiveContext budget: ~15% orchestrator, 100% fresh per subagent. </objective>
<execution_context> @$HOME/.claude/get-shit-done/workflows/execute-phase.md @$HOME/.claude/get-shit-done/references/ui-brand.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>
Available optional flags (documentation only — not automatically active):
--wave N — Execute only Wave N in the phase. Use when you want to pace execution or stay inside usage limits.--gaps-only — Execute only gap closure plans (plans with gap_closure: true in frontmatter). Use after verify-work creates fix plans.--interactive — Execute plans sequentially inline (no subagents) with user checkpoints between tasks. Lower token usage, pair-programming style. Best for small phases, bug fixes, and verification gaps.Active flags must be derived from $ARGUMENTS:
--wave N is active only if the literal --wave token is present in $ARGUMENTS--gaps-only is active only if the literal --gaps-only token is present in $ARGUMENTS--interactive is active only if the literal --interactive token is present in $ARGUMENTSContext files are resolved inside the workflow via gsd-tools init execute-phase and per-subagent <files_to_read> blocks.
</context>
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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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