Use to keep moving without inspecting progress, or when the user asks what is next in the GSD workflow. Reads STATE.md, ROADMAP.md and phase dirs to invoke the next step; --force skips safety and verification gates.
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See how to installReview the instructions the agent will receive before you install.
Designed for rapid multi-project workflows where remembering which phase/step you're on is overhead.
Supports --force flag to bypass safety gates (checkpoint, error state, verification failures).
</objective>
<execution_context> @$HOME/.claude/get-shit-done/workflows/next.md </execution_context>
<process> Execute the next workflow from @$HOME/.claude/get-shit-done/workflows/next.md end-to-end. </process>No reviews yet. Tell others what you thought.
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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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