Use after a phase's code is written, or when the user asks to review changed files for bugs, security issues, or quality. Spawns the gsd-code-reviewer agent at quick, standard, or deep depth and writes the phase REVIEW.md.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Spawns the gsd-code-reviewer agent to analyze code at the specified depth level. Produces REVIEW.md artifact in the phase directory with severity-classified findings.
Arguments:
--depth=quick|standard|deep (optional) — review depth level, overrides workflow.code_review_depth config
--files file1,file2,... (optional) — explicit comma-separated file list, skips SUMMARY/git scoping (highest precedence for scoping)Output: {padded_phase}-REVIEW.md in phase directory + inline summary of findings </objective>
<execution_context> @$HOME/.claude/get-shit-done/workflows/code-review.md </execution_context>
<context> Phase: $ARGUMENTS (first positional argument is phase number)Optional flags parsed from $ARGUMENTS:
--depth=VALUE — Depth override (quick|standard|deep). If provided, overrides workflow.code_review_depth config.--files=file1,file2,... — Explicit file list override. Has highest precedence for file scoping. When provided, workflow skips SUMMARY.md extraction and git diff fallback entirely.Context files (CLAUDE.md, SUMMARY.md, phase state) are resolved inside the workflow via gsd-tools init phase-op and delegated to agent via <files_to_read> blocks.
</context>
Execute the code-review workflow from @$HOME/.claude/get-shit-done/workflows/code-review.md end-to-end.
The workflow (not this command) enforces these gates:
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.