build-your-own-openclaw-agent-tutorial
Step-by-step guide to building AI agents from simple chat loops to autonomous multi-agent systems with tools, memory, and event-driven architecture
Find an Agent Skill for your task. Read the guide and copy the install command.
Updated 10-21 00:11
24,316 results
Step-by-step guide to building AI agents from simple chat loops to autonomous multi-agent systems with tools, memory, and event-driven architecture
Authoring Pi TypeScript extensions — ExtensionAPI, custom tools, commands, UI hooks, resources_discover, `registerProvider()`, and how extendResources merges…
Based on the current codebase, organize local Comet ideas, observed regressions, or improvement proposals into a focused GitHub Issue draft. Use when the user requests converting local investigations into an Issue, follow-up Issue, Bug, Feature, or maintenance task.
Create, improve, and audit AI agent skills. 14 structural patterns, deterministic scoring. Use when building or reviewing skills. NOT for agents, MCP servers,…
Audit and refine a skill repository, a single skill, a workflow framework, or an eval set. Covers design quality, context engineering, purpose fit, evidence…
Use when on the spec branch of sdlc-dev, R1 (raw→solution) requirement clarification needs to be completed, but pressure signals appear such as urging to skip clarification/skip gates, demanding "ask everything at once", or asking you to directly give the next-step routing conclusion.
Guides when to ask clarifying questions versus proceed autonomously. Use to reduce unnecessary clarifying questions when intent is clear.
Assesses decision reversibility and risk at critical checkpoints. Use when a workflow reaches a high-stakes branch needing escalation check.
Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".
Use when explicitly invoked, when reducing agent-workflow cost, or when a concrete deliverable requires no narration, final only, code only, findings only, or…
Extract structured data from construction documents using LLMs. Process RFIs, submittals, contracts, specifications. Convert unstructured PDFs to structured…
Build RAG systems for construction knowledge bases. Create searchable AI-powered construction document systems
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an…
Capture durable session learnings, stage for human promotion to 05-knowledge/lizard, and propose skill/CLAUDE.md patches. Triggered by /harvest, SessionEnd…
Look up CE.SDK Node.js reference docs, guides, and configuration pages. Use when the user needs CE.SDK docs for Node.js — configuration, headless…
Transforms vague or unreliable prompts into structured, constraint-aware prompts with explicit roles, task decomposition, output formats, and quality checks.…
Use when: the user asks to make or render charts with flint-chart, visualize tabular data, generate a ChartAssemblyInput, validate/render through MCP, or add…
Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.
Implements retrieval-augmented generation pipelines. Use when building document retrieval systems, choosing chunking strategies, selecting embedding models,…
OpenShift platform and Kubernetes expert with deep knowledge of cluster architecture, operators, networking, storage, troubleshooting, and CI/CD pipelines. Use…
Methodology for starting a new library-reference distillation skill — one that turns an external library (nuqs, zod, framer-motion, msw, react-hook-form,…
Create or edit a document that an agent reads, and prove that it changes behavior. Put a lesson that agents keep learning again into structure. Read this skill…
Inspect an existing agent codebase, map each agent's tools and skills, then implement Ratel so every agent discovers and loads only its intended capabilities.…
Build and deploy reflective research agents with FastAPI, Postgres, and multi-step planning workflows using Tavily, arXiv, and Wikipedia tools