Total 54,004 skills, AI & Machine Learning has 8981 skills
Showing 12 of 8981 skills
Complete automated literature discovery pipeline: multi-source search → six-dimension scoring → fine reading → formatted delivery → archival. Combines a configurable engine with daily cron-driven application layer. Works with Feishu, Telegram, or any messaging platform.
Ultra-compressed communication mode. Cuts output tokens 65% (measured) by speaking like hui while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "hui mode", "talk like hui", "use hui", "less tokens", "be brief", or invokes /hui. Also auto-triggers when token efficiency is requested.
Turn a plain video clip into a cinematic AI-VFX shot at 1080p by default (or 4K / 720p on request). Give Claude a video and the change you want; it reads EVERY frame via local contact sheets and understands the audio, writes a Seedance-faithful prompt that locks your face, gestures, and camera move, then re-renders the same shot with the VFX baked in via Seedance reference-to-video.
Determine the stage of a research task and route it to a main workflow. Use when the user asks for "beginner's guide", "start research process", "what should I use for this research task", "help me choose a research skill", or requests the rw-research-router workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Select available tools based on research tasks, data, and operating environment; works without a preset local research-lab. Use when the user asks for "which tool to use for research tasks", "help me choose research tools", "is this repo useful", or requests the rw-research-lab-router workflow. Runs without a private local workspace or preset research-lab; uses user-provided materials and bundled public-source methods.
AI SDLC evidence-backed retrospective workflow. Use when delivery work is complete or paused and an AI assistant needs to capture observations, connect them to validation or artifact evidence, formulate reviewable process or policy improvement proposals, assign ownership, and preserve the rule that policy changes require an accepted decision. Supports `--quick-flow` for focused learning and `--full-flow` for strict evidence and decision gates.
Generates a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup/resumption, bearer token refresh, and sending/receiving `ClientMessage`/`ServerMessage` protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot `generateContent`, embeddings, image/video generation, or fine-tuning — use the `gemini-api` skill for those.
Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.
Use when someone asks to enhance an image, generate AI images, remove background, improve image quality, or create product shots. Also use when the user mentions 'AI image generation,' 'generate an image,' 'enhance my photo,' 'remove background,' 'improve image quality,' 'make this image better,' 'product shot enhancement,' 'generate background,' 'image enhancement,' 'AI photo,' or 'touch up my image.' Uses Sivi's generate API to create or enhance images using AI models. For uploading existing local files, see brand-assets. For generating designs from prompts, see generate-design.
Design NPC and enemy decision-making with finite state machines, behavior trees, steering behaviors, and A* pathfinding — engine-neutral algorithms that pair with the detected engine's navigation API. Use when building enemy AI, an FSM or behavior tree, steering/flocking, or pathfinding, or when the user mentions state machine, behavior tree, blackboard, A*, navmesh, seek, or patrol/chase.
Compatibility router for LangWatch evaluation requests. Use only when the user asks for evaluations without making it clear whether they mean pre-deployment experiments or production online evaluations. Routes the request to the focused companion skill and does not implement either workflow itself.
Create, modify, run, inspect, analyze, and report Python experiments that use liblaf.cherries. Use when Codex needs to work under exp/YYYY/mm/dd/group-name/, write or edit numbered scripts in src/, run them with CHERRIES_NAME and CHERRIES_TAGS, inspect Cherries/Comet logs and generated assets, or write Markdown reports in docs/.