Total 50,542 skills, AI & Machine Learning has 8483 skills
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OpenClaw 官方文档知识库，一个多渠道 AI Agent 网关。当用户询问 OpenClaw 相关问题（如安装、配置、Gateway、WhatsApp/Telegram/Discord 等渠道连接、Sessions、Tools、Skills、Pi Agent、故障排查等）时使用此 skill。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, conducting literature reviews, finding related work, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, citation verification workflows, and paper discovery/evaluation criteria.
Trend intelligence and cultural signal detection for emerging news and behaviors. USE WHEN: Researching latest news (48h), identifying cultural/tech/consumer shifts before mainstream adoption, analyzing emerging trends with advanced elicitation. PRIMARY TRIGGERS: "coolhunt [topic]" = Full research workflow (5 steps) "trend analysis" = Deep analysis with elicitation methods "news scan [topic]" = Quick news gathering WORKFLOW: Request → Web Research → Elicitation Selection → Analysis → Report OUTPUT: Markdown report with headline, summary, fact-check, and behavioral analysis saved to coolhunter-output/report-{datetime}/{title}.md
Run holistic pedagogical review on lecture slides. Checks narrative arc, student prerequisites, worked examples, notation clarity, and deck pacing.
Use this skill when crafting, reviewing, or improving prompts for LLM pipelines — including task prompts, system prompts, and LLM-as-Judge prompts. Triggers include: requests to write or refine a prompt, diagnose why an LLM produces inconsistent or incorrect outputs, bridge the gap between intent and model behavior, reduce ambiguity in instructions, add few-shot examples, structure complex prompts, or improve output formatting. Also use when the user needs help distinguishing specification failures (unclear instructions) from generalization failures (model limitations), or when iterating on prompts based on observed failure modes. Do NOT use for general coding tasks, document creation, or non-LLM writing.
Extract a learned skill from the current conversation
Quick persona switching. Triggers: 'switch persona', 'switch to X', 'become X'. Lists personas, reads selected file, switches immediately.
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM w...
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
Genera documentación llms.txt optimizada para LLMs. Usa cuando el usuario diga "crear llms.txt", "documentar para AI", "crear documentación para LLMs", "generar docs para modelos", o quiera hacer el repo legible para Claude/AI.
Compare Nim and Python scripted agent implementations and align behavior. Use when asked to port or ensure parity between Nim and Python.