Use when every phase in a milestone is complete and the user wants to archive it, tag the release, and start the next version. Archives roadmap and requirements to .planning/milestones/, updates PROJECT.md, then tags the version.
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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。
Purpose: Create historical record of shipped version, archive milestone artifacts (roadmap + requirements), and prepare for next milestone. Output: Milestone archived (roadmap + requirements), PROJECT.md evolved, git tagged. </objective>
<execution_context> Load these files NOW (before proceeding):
User input:
Follow complete-milestone.md workflow:
Check for audit:
.planning/v$ARGUMENTS-MILESTONE-AUDIT.md/gsd-audit-milestone firstgaps_found: recommend /gsd-plan-milestone-gaps firstpassed: proceed to step 1## Pre-flight Check
{If no v$ARGUMENTS-MILESTONE-AUDIT.md:}
⚠ No milestone audit found. Run `/gsd-audit-milestone` first to verify
requirements coverage, cross-phase integration, and E2E flows.
{If audit has gaps:}
⚠ Milestone audit found gaps. Run `/gsd-plan-milestone-gaps` to create
phases that close the gaps, or proceed anyway to accept as tech debt.
{If audit passed:}
✓ Milestone audit passed. Proceeding with completion.
Verify readiness:
Gather stats:
Extract accomplishments:
Archive milestone:
.planning/milestones/v$ARGUMENTS-ROADMAP.mdArchive requirements:
.planning/milestones/v$ARGUMENTS-REQUIREMENTS.md.planning/REQUIREMENTS.md (fresh one created for next milestone)Update PROJECT.md:
<details> (if v1.1+)Commit and tag:
chore: archive v$ARGUMENTS milestonegit tag -a v$ARGUMENTS -m "[milestone summary]"Offer next steps:
/gsd-new-milestone — start next milestone (questioning → research → requirements → roadmap)<success_criteria>
.planning/milestones/v$ARGUMENTS-ROADMAP.md.planning/milestones/v$ARGUMENTS-REQUIREMENTS.md.planning/REQUIREMENTS.md deleted (fresh for next milestone)<critical_rules>
/gsd-new-milestone which includes requirements definition
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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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