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Found 178 Skills
Use this when users need to collect research materials for an article or topic by gathering YouTube videos and web articles into a NotebookLM notebook, then running analysis queries and saving the results as markdown. It is ideal for requests like "collect materials", "find relevant videos and articles on this topic for me", and "organize for NotebookLM analysis". This skill combines yt-dlp YouTube search, NotebookLM `nlm` CLI research, and markdown report output.
Route generative media requests before any creative planning or provider execution. Use this when the user asks to generate, modify, dub, animate, or assemble image, video, audio, workflow, or analysis-derived media and the first decision is which generation controller should own the job.
Adaptive multi-agent framework for automated data science tasks with planning, execution, and validation
Mark a checkpoint in the current conversation — compact it into a durable handoff document so a fresh agent can resume the work without context loss. Use when the user wants to preserve session state for a later or parallel session — phrases like "hand this off", "write a handoff", "drop a wheypoint", "checkpoint this", "compact the conversation", "I'm running low on context", "save where we are for the next session", "prep a handoff for another agent", "/wheypoint". Use even when the user just says "wrap up" or "I need to clear context" mid-task. Do NOT use for per-phase pipeline handoffs — those belong to `/cook`, `/press`, `/age`, and `/cure`.
Converge a fuzzy idea or half-formed feature into an approved spec through an iterative, grounded design dialogue. Use when the user has a fuzzy idea or design direction — phrases like "let's design X", "I'm thinking about Y", "what should the API for Z look like", "shape this into a spec", "what would it take to build/set up X", "I want to add a feature that…", "/mold". Use even when the user is "just thinking out loud" if they want the dialogue to leave behind a written artifact. Do NOT use for free-form discussion with no artifact intent (`/culture`), direct implementation (`/cook`), or research-only questions (`/briesearch`).
Set up and manage local skills for automatic matching and invocation
This skill provides comprehensive guidance for using the Replicate CLI to run AI models, create predictions, manage deployments, and fine-tune models. Use this skill when the user wants to interact with Replicate's AI model platform via command line, including running image generation models, language models, or any ML model hosted on Replicate. This skill should be used when users ask about running models on Replicate, creating predictions, managing deployments, fine-tuning models, or working with the Replicate API through the CLI.
Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
Publish a Harbor task or dataset to the registry. Use when the user wants to upload, publish, or share tasks or datasets/benchmarks on the Harbor registry.
Manages the ai-context/ memory layer: initialize from scratch, update with session work, or maintain/cleanup. Trigger: /memory-init, /memory-update, /memory-maintain, initialize memory, update memory, maintain memory.
Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts
AI project intelligence system. Manages .ai/ directory for rules, behaviours, sessions, incidents, memory, snapshots, and learning loops. Use when: starting a session, switching behaviour, logging an incident, saving feedback, reviewing past sessions, checking active hotfixes, managing snapshots, creating snippets/prompts. Proactively suggest when: user corrects AI behavior ("no", "don't", "wrong", "stop", "always", "never"), session ends, a mistake pattern repeats, starting work on unfamiliar code, user says "remember this" or "learn this".