Total 50,657 skills, AI & Machine Learning has 8491 skills
Showing 12 of 8491 skills
Rent cars, manage Gold Plus Rewards, and access Hertz premium services
Enables Claude to browse, organize, and manage photos and videos in Google Photos via Playwright MCP
This skill enriches vague prompts with targeted research and clarification before execution. Should be used when a prompt is determined to be vague and requires systematic research, question generation, and execution guidance.
Methodology for effective AI-assisted software development. Use when helping users build software with AI coding assistants, debugging AI-generated code, planning features for AI implementation, managing version control in AI workflows, or when users mention "vibe coding," Cursor, Windsurf, or similar AI coding tools. Provides strategies for planning, testing, debugging, and iterating on code written with LLM assistance.
Scaffold development rules for AI coding agents. Auto-invoked when user asks about setting up rules, coding conventions, or configuring their AI agent environment.
Meta-skill for analyzing PRs, issues, and user interactions to improve Cursor rules and skills automatically
Implement and debug SSE (Server-Sent Events) streaming for the Perplexity AI API, including parsing, reconnection, and retry logic.
Turn an idea into a functional, demo-ready prototype using AI-assisted “vibe coding” (timeboxed build loop, prompt pack, build plan, demo script, and safety checks). Use for rapid prototyping and proving concepts in AI & Technology.
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). Use for building with LLMs, GPT/Claude apps, prompt engineering, RAG, and tool-using agents.
Test, validate, and improve agent instructions (CLAUDE.md, system prompts) using sub-agents as experiment subjects. Measures instruction compliance, context decay, and constraint strength. Use for "test prompt", "validate instructions", "prompt effectiveness", "instruction decay", or when designing robust agent behaviors.
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
Multi-directory context patterns for monorepos. Use when working with --add-dir, per-service CLAUDE.md, or separating root vs service context