Total 56,673 skills, AI & Machine Learning has 9430 skills
Showing 12 of 9430 skills
A natural language workflow for converting literary works (novels, stories, scripts, one-sentence concepts, etc.) into film and video content, which converts novel content into complete videos by orchestrating multiple skills in sequence. This skill is used when users need to convert novels, stories or other literary works into videos.
Reroll Claude Code /buddy companion to a target species and rarity. Use when the user wants to change their buddy pet, get a legendary companion, reroll their buddy, or is unhappy with their current /buddy result. Triggers on "reroll buddy", "change buddy", "legendary buddy", "new companion", "buddy hack", "换宠物", "重新抽", "传说宠物", "バディ変更", "伝説バディ", or similar.
Guide to image generation and editing in MassGen. Use when creating images, editing existing images, iterating on image designs, or choosing between image backends (OpenAI, Google Gemini/Imagen, Grok, OpenRouter).
Analyze, reorganize, and catalog all installed skills. Merges overlapping skills, restructures the filesystem hierarchy, and generates a compact SKILL_REGISTRY.md routing guide for system prompts.
Use when Alibaba Cloud Model Studio Wan video editing models are needed for style transfer, keyframe-controlled editing, or animation remix workflows.
Use when transcribing non-realtime speech with Alibaba Cloud Model Studio Qwen ASR models (`qwen3-asr-flash`, `qwen-audio-asr`, `qwen3-asr-flash-filetrans`). Use when converting recorded audio files to text, generating transcripts with timestamps, or documenting DashScope/OpenAI-compatible ASR request and response fields.
Use when generating videos with Alibaba Cloud Model Studio PixVerse models (`pixverse/pixverse-v5.6-t2v`, `pixverse/pixverse-v5.6-it2v`, `pixverse/pixverse-v5.6-kf2v`, `pixverse/pixverse-v5.6-r2v`). Use when building non-Wan text-to-video, first-frame image-to-video, keyframe-to-video, or multi-image reference-to-video workflows on Model Studio.
Interpreted crypto wallet data for AI agents. Use when an agent needs portfolio values, token positions, DeFi positions, NFT holdings, transaction history, PnL data, token prices, charts, gas prices, swap quotes, or DApp information across 41+ chains. Zerion transforms raw blockchain data into agent-ready JSON with USD values, protocol labels, and enriched metadata. Supports x402 pay-per-request ($0.01 USDC on Base) and API key access. Triggers on mentions of portfolio, wallet analysis, positions, transactions, PnL, profit/loss, DeFi, token balances, NFTs, swap quotes, gas prices, or Zerion.
Design Pydantic models and LLM prompt templates for structured extraction pipelines. Use when creating, editing, or reviewing Pydantic models that serve as LLM output schemas, or when writing prompt templates that pair with those models. Trigger: "pydantic model", "structured output", "extraction schema", "LLM output model", "schema design".
Guide for conducting thorough, multi-source research and producing comprehensive, well-sourced reports. Powered by AnyCap -- the capability runtime that equips AI agents with web search (including AI Grounded citations), web crawl, image generation, cloud storage, and one-click web publishing through a single CLI. Use when the user asks for deep research, competitive analysis, market research, technical deep dive, literature review, technology comparison, or any task requiring multi-source information gathering and synthesis. Also use when users say "investigate", "survey the landscape", "compare X vs Y", "state of the art", "write a report on", "look into", "find out about", "analyze the market", or any inquiry that needs more than a single search. Trigger on mentions of research, analysis, investigation, comparison, report, survey, or deep dive.
Use when an approved plan exists and needs execution, or when a hotfix/one-sentence scope needs direct TDD implementation — dispatches subagents per task, validates, reports
Build explicit learn/do-not-copy contracts for image and video generation references. Use this when a prompt uses benchmark videos, contact sheets, frames, or product images and you need to state exactly what the model should learn, what identity elements must change, and which references should be excluded from the first test.