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Found 7,389 Skills
Research a codebase and create architectural documentation describing how features or systems work. Use when the user asks to: (1) Document how a feature works, (2) Create an architecture overview, (3) Explain code structure for onboarding or knowledge transfer, (4) Research and describe a system's design. Produces markdown documents with Mermaid diagrams and stable code references suitable for humans and AI agents.
Write technical specifications that give agents enough context to implement features while leaving room for autonomous research and decision-making. Use when planning features, documenting architecture decisions, or creating implementation guides.
Guide for implementing HolmesGPT - an AI agent for troubleshooting cloud-native environments. Use when investigating Kubernetes issues, analyzing alerts from Prometheus/AlertManager/PagerDuty, performing root cause analysis, configuring HolmesGPT installations (CLI/Helm/Docker), setting up AI providers (OpenAI/Anthropic/Azure), creating custom toolsets, or integrating with observability platforms (Grafana, Loki, Tempo, DataDog).
OpenClaw usage expert: Helps you install, deploy, configure, and use OpenClaw personal AI assistant. Can diagnose issues, create bots, execute automated tasks, etc. Use when users need to install OpenClaw, configure Gateway, set up Channels, create Agents, troubleshoot issues, or perform OpenClaw-related operations.
Connect the complete AI development workflow through documents. It covers domain modeling and code organization (DDD), behavior verification and automated testing (BDD), as well as AI development specification setting (Agent specifications). Use when (1) the project has .feature files, (2) the user asks to organize code by business features or define naming conventions, (3) creating or updating AGENTS.md / project rule files, (4) writing or implementing Gherkin scenarios, (5) starting a new project from scratch, or (6) the agent needs the full development lifecycle.
Inter-instance communication for OpenClaw. Use this skill when you need to send messages, synchronize data, or execute commands remotely between multiple OpenClaw instances. It covers multiple methods such as agent-to-agent messaging, nodes.run remote execution, and file-level communication.
Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5] [--notify-channel -1002381931352]
Project scaffolding CLI with 30+ integrations, custom templates, and MCP server for AI agents.
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.
Automatic risk assessment before every critical action in agentic workflows. Detects irreversible operations (file deletion, database writes, deployments, payments), classifies risk level, and requires confirmation before proceeding. Triggers on destructive keywords like deploy, delete, send, publish, update database, process payment.
Maintain a structured ledger of decisions, discovered bugs and fixes, user preferences, constraints, current status, and failed approaches throughout multi-step agentic tasks. Auto-update after every significant step. Triggers on "where were we", "continue", "summarize status", "remember", or when a new agent instance takes over a task.
Evaluate every produced output (code, report, plan, data, API response) against type-specific quality criteria, score 1-10, make accept/reject decisions, and provide actionable improvement suggestions. Triggers on "evaluate", "check", "review", "quality control", "is this good enough", "score it", or before passing output to the next step in an agentic workflow.