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Found 1,573 Skills
Expert guide for configuring, customizing, and creatively leveraging OpenClaw — the self-hosted AI gateway that connects LLMs to messaging channels (Telegram, WhatsApp, Discord, Slack, iMessage, etc.). Use when the user wants to: (1) Set up or modify their openclaw.json configuration, (2) Write or edit bootstrap files (SOUL.md, USER.md, AGENTS.md, IDENTITY.md, TOOLS.md), (3) Configure messaging channels, (4) Set up models and providers, (5) Create multi-agent routing, (6) Build skills, hooks, or cron jobs, (7) Troubleshoot OpenClaw issues, (8) Get creative ideas for leveraging OpenClaw in non-obvious ways. Triggers on: openclaw, gateway, SOUL.md, USER.md, AGENTS.md, IDENTITY.md, channels setup, agent routing, heartbeat, cron jobs, openclaw hooks, openclaw skills, openclaw config, openclaw.json, personal assistant setup.
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.
Optimizes markdown documents for token efficiency, clarity, and LLM consumption. Use when (1) a markdown file needs streamlining for use as LLM context, (2) reducing token count in documentation without losing meaning, (3) converting verbose docs into concise reference material, (4) improving structure and scannability of markdown files, or (5) preparing best-practices or knowledge docs for agent consumption.
Hub skill for requirements elicitation. Provides technique selection, orchestration guidance, LLMREI patterns, and autonomy level configuration. Use when gathering requirements from stakeholders, conducting elicitation sessions, or preparing requirements for specification.
AI/LLM: Use when crafting system prompts, optimizing LLM outputs, or improving agent instructions. NOT for general coding.
Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".
Set up Langfuse local development workflow with hot reload and debugging. Use when developing LLM applications locally, debugging traces, or setting up a fast iteration loop with Langfuse. Trigger with phrases like "langfuse local dev", "langfuse development", "debug langfuse traces", "langfuse hot reload", "langfuse dev workflow".
Provides tool and function calling patterns with LangChain4j. Handles defining tools, function calls, and LLM agent integration. Use when building agentic applications that interact with tools.
Removes AI writing artifacts from documentation and code. Use when editing LLM-generated prose, reviewing READMEs, polishing docs before publishing, or cleaning up AI-generated code. Use for emdash cleanup, formulaic phrase removal, tone calibration, over-commented code, verbose naming, and AI code smell detection.
Use when an existing agent already works without Prefactor and you need to add tracing for runs, llm calls, tool calls, and failures with minimal behavior changes.
Extract edge hints from daily market observations and news reactions, with optional LLM ideation, and output canonical hints.yaml for downstream concept synthesis and auto detection.
Interactive tutorial teaching Snowflake Cortex CLASSIFY_TEXT for categorizing unstructured text. Guide users through classifying customer reviews using Python and SQL. Use when user wants to learn text classification, Cortex LLM functions, or analyze unstructured feedback data.