468건 · 관련도순
Run a bounded, governed iteration — "loop", "iterate until", "keep going until", "sweep", "ratchet", "until it passes", "coverage to 100". Turns an open-ended autonomous run into a finite loop with checkpoints and explicit stopping conditions.
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Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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Phase 2 of the Forge pipeline. Turn a research document into an implementation plan that is built around Beads issues and TDD. Reads plans/research/<slug>.md, designs the build, sets up red-green-refactor test strategy using the tdd skill, materializes a Beads epic/feature/tasks with dependencies and priorities, and defines demo/test checkpoints. Use when starting /forge-plan or the plan step of /forgemaster.
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Phase 3 of the Forge pipeline. Execute the plan's Beads tasks with red-green-refactor TDD, committing per checkpoint and pausing at each natural stopping point so the user can run a live demo or test and see the work themselves. Use when starting /forge-implement or the implement step of /forgemaster.
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Creates or updates the active planning artifact under planning/active/ for any change-bearing workstream. Consumes the active spec when one exists. Defines ordered tasks, target paths, commands, and checkpoints.
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Close out a completed workstream by updating README artifacts when needed, producing a commit message, and applying changelog-ready content when the work is ready for commit. Use after review when a stable checkpoint is reached.
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Close out a completed workstream by updating README artifacts when needed, producing a commit message, and applying changelog-ready content when the work is ready for commit. Use after review when a stable checkpoint is ready.
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Design, validate, and plan a startup from scratch. Covers market research, competitive analysis, business model, brand identity, product definition, financial projections, and validation experiments. Trigger when the user has a startup idea to explore, wants to validate a business concept, needs a business plan or lean canvas, asks for market sizing or competitive landscape, wants brand positioning or go-to-market strategy, or says anything like "I have an idea for..." or "is this idea worth pursuing". Also handles resuming from a previous checkpoint.
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Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
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Use when you have a written implementation plan to execute in a separate session with review checkpoints
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Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
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Build production-grade AI agents from scratch using patterns inspired by production-grade agentic systems like Claude Code. Covers two audiences — (1) workflow builders who need planner+executor pipelines, human-in-the-loop checkpoints, and multi-agent orchestration, and (2) application embedders who need HTTP/SSE APIs, multi-user session management, and React frontend integration. Use when asked to build an AI agent, create a coding assistant, embed an agent in an app, build an API agent, clone Claude Code, build a CLI agent, design an agent architecture, scaffold an agentic system, or automate a multi-step workflow. Triggers on keywords like agent builder, agent blueprint, coding assistant, CLI agent, agentic tool, claude code clone, agent scaffold, agent architecture, workflow automation, multi-agent, planner executor, SSE streaming, agent API.
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