Use when a completed phase needs unit or E2E tests, or the user asks to add tests for phase N. Classifies implementation files into TDD, E2E, or skip, gets plan approval, then generates RED-GREEN tests from SUMMARY.md and UAT criteria.
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。
Analyzes implementation files, classifies them into TDD (unit), E2E (browser), or Skip categories, presents a test plan for user approval, then generates tests following RED-GREEN conventions.
Output: Test files committed with message test(phase-{N}): add unit and E2E tests from add-tests command
</objective>
<execution_context> @$HOME/.claude/get-shit-done/workflows/add-tests.md </execution_context>
<context> Phase: $ARGUMENTS@.planning/STATE.md @.planning/ROADMAP.md </context>
<process> Execute the add-tests workflow from @$HOME/.claude/get-shit-done/workflows/add-tests.md end-to-end. Preserve all workflow gates (classification approval, test plan approval, RED-GREEN verification, gap reporting). </process>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.
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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
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.
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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.
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。