Use when the user wants a small, ad-hoc change handled with GSD guarantees (atomic commits, STATE.md) but without the full pipeline. Tasks land in .planning/quick/; --full, --discuss, --research, --validate add the skipped agents back.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Quick mode is the same system with a shorter path:
.planning/quick/ separate from planned phasesDefault: Skips research, discussion, plan-checker, verifier. Use when you know exactly what to do.
--discuss flag: Lightweight discussion phase before planning. Surfaces assumptions, clarifies gray areas, captures decisions in CONTEXT.md. Use when the task has ambiguity worth resolving upfront.
--full flag: Enables the complete quality pipeline — discussion + research + plan-checking + verification. One flag for everything.
--validate flag: Enables plan-checking (max 2 iterations) and post-execution verification only. Use when you want quality guarantees without discussion or research.
--research flag: Spawns a focused research agent before planning. Investigates implementation approaches, library options, and pitfalls for the task. Use when you're unsure of the best approach.
Granular flags are composable: --discuss --research --validate gives the same result as --full.
</objective>
<execution_context> @$HOME/.claude/get-shit-done/workflows/quick.md </execution_context>
<context> $ARGUMENTSContext files are resolved inside the workflow (init quick) and delegated via <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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