Total 55,251 skills, AI & Machine Learning has 9172 skills
Showing 12 of 9172 skills
Generate images using AI when user wants to create pictures, draw, paint, or generate artwork. Supports text-to-image and image-to-image generation.
Helps users discover, search and install agent skills from the marketplace. Use when the user wants to find a skill, discover new capabilities, find specific tools (especially sandboxes) or install a skill. This skill should be used when the user is looking for functionality that might exist as an installable skill. Also supports progressive disclosure to recommend sandbox-related skills when relevant. 用于帮助用户在市场中搜索、发现和安装 Agent 插件（Skills）。 当用户想要查找功能、安装特定工具或寻找沙箱（sandbox）时使用。 Triggers: "查找插件","搜索技能","搜索 X 技能","安装技能","安装 X 技能","如何做 X","沙箱","安全沙箱","find a skill for X", "how do i do X", "search for skill", "install skill", "I need a skill for X", "sandbox", "aio sandbox"
Generate LLM skills from documentation, codebases, and GitHub repositories
This skill should be used when the user asks to "generate video prompts", "create Seedance prompts", "write video descriptions", mentions "Seedance", "seedance", "Jimeng", "Jimeng Platform", "video prompts", "video generation", "AI video", "short drama", "advertising video", "video extension", or discusses video prompt engineering, AI video generation, or Seedance 2.0 workflows.
Use when answering complex questions about a codebase that require exploring multiple areas or understanding how components connect - coordinates parallel sub-agents to locate, analyze, and synthesize findings
Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.
Token usage tracking, cost estimation, and usage analytics
An Agent dedicated to brainstorming and finalizing specifications. Finalize a single, implementable and testable Spec.md.
Integrate installed skill usage guidance into project CLAUDE.md/AGENTS.md based on project context. Use when skills are installed but agents don't know when to use them, when setting up a new project with skills, or when updating guidance after adding skills.
Analyzes and refines agent skills by identifying quality issues, prioritizing fixes (MUST/SHOULD/NICE), gathering user feedback, and implementing improvements. Checks for common problems like time estimates, oversized SKILL.md files, poor structure, redundant content, missing examples, and unclear workflows. Use when reviewing, improving, refactoring, or auditing existing skills. Triggers include "review skill", "improve skill", "refactor skill", "skill quality", "audit skill", "fix skill", "optimize skill", "analyze skill".
Use when "LangChain", "LLM chains", "ReAct agents", "tool calling", or asking about "RAG pipelines", "conversation memory", "document QA", "agent tools", "LangSmith"
Comprehensive technical research by combining multiple intelligence sources — Grok (X/Twitter developer discussions via Playwright), DeepWiki (AI-powered GitHub repository analysis), and WebSearch. Dispatches parallel subagents for each source and synthesizes findings into a unified report. This skill should be used when evaluating technologies, comparing libraries/frameworks, researching GitHub repos, gauging developer sentiment, or investigating technical architecture decisions. Trigger phrases include "tech research", "research this technology", "技术调研", "调研一下", "compare libraries", "evaluate framework", "investigate repo".