Total 50,476 skills, AI & Machine Learning has 8471 skills
Showing 12 of 8471 skills
Hybrid memory strategy combining OpenClaw's built-in QMD vector memory with Graphiti temporal knowledge graph. Use for all memory recall requests.
Interactive guide for creating new agent skills from scratch. Use this skill when the user wants to create a skill, write a SKILL.md, package a skill folder, or convert an existing workflow into a reusable skill. Also trigger when the user mentions "create skill", "new skill", "SKILL.md", "teach the agent", or wants to standardize a repeatable process into a skill.
AI Agent native API provider — no API keys, no signups, no subscriptions. Access 337+ APIs including Finance, Social, Real Estate, and more. Just pay with USDC per request via x402.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Connect Claude to any app. Send emails, create issues, post messages, update databases - take real actions across Gmail, Slack, GitHub, Notion, and 1000+ services.
Essential development workflow agents for code review, debugging, testing, documentation, and git operations. Includes 7 specialized agents with strong auto-discovery triggers. Use when: setting up development workflows, code reviews, debugging errors, writing tests, generating documentation, creating commits, or verifying builds.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
Build with Claude Messages API using structured outputs for guaranteed JSON schema validation. Covers prompt caching (90% savings), streaming SSE, tool use, and model deprecations. Prevents 16 documented errors. Use when: building chatbots/agents, troubleshooting rate_limit_error, prompt caching issues, streaming SSE parsing errors, MCP timeout issues, or structured output hallucinations.
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
Automated factory for converting GitHub repositories into specialized AI skills. Use this skill when the user provides a GitHub URL and wants to "package", "wrap", or "create a skill" from it. It automatically fetches repository details, latest commit hashes, and generates a standardized skill structure with enhanced metadata suitable for lifecycle management.
Help users create and run AI evaluations. Use when someone is building evals for LLM products, measuring model quality, creating test cases, designing rubrics, or trying to systematically measure AI output quality.