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.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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…
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。