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Found 105 Skills
PUA Loop — Autonomous Iterative Development with PUA Pressure. Runs continuously until the task is completed, no user interaction required. Combines the Ralph Loop iteration mechanism with PUA quality enforcement. Triggered by: '/pua loop', '/pua:loop', 'automatic loop', 'loop mode', 'keep running', 'automatic iteration'.
Use when working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)
Expert guidance for researching, documenting, and integrating Model Context Protocol (MCP) servers and tools. Covers MCP architecture, server/client implementation patterns, tool discovery, integration workflows, security best practices, and multi-language SDK usage (Python, TypeScript, C#, Java, Rust). Enables seamless integration of MCP tools into Claude Code and AI applications.
Generates valid n8n workflow JSON with nodes, connections, settings, credentials. Use when creating workflow automations programmatically, scaffolding AI agent workflows with LangChain nodes, or converting requirements into n8n JSON.
Expert guidance for creating Claude Code skills and slash commands. Use when working with SKILL.md files, authoring new skills, improving existing skills, creating slash commands, or understanding skill structure and best practices.
Project setup. Explore the codebase, ask about strategy and aims, write persistent context to AGENTS.md. Run when starting or when aims shift.
Post-compaction context recovery. Detects in-progress RPI and evolve sessions, loads knowledge, shows recent work and pending tasks. Triggers: "recover", "lost context", "where was I", "what was I working on".
Form a committee of two high-reasoning agents to step back, do root cause analysis, and produce a plan. Use when stuck, looping, tunnel-visioning, or facing a hard planning problem.
CrewAI task design and configuration. Use when creating, configuring, or debugging crewAI tasks — writing descriptions and expected_output, setting up task dependencies with context, configuring output formats (output_pydantic, output_json, output_file), using guardrails for validation, enabling human_input, async execution, markdown formatting, or debugging task execution issues.
Set up Jetty for the first time. Guides the user through account creation, API key configuration, and introduces runbooks — human-readable markdown files that tell an agent how to accomplish multi-step tasks with measurable outcomes. Use this skill whenever the user wants to set up, configure, or get started with Jetty — including 'set up jetty', 'configure jetty', 'jetty setup', 'get started with jetty', 'install jetty', 'connect to jetty', 'jetty onboarding', 'I am new to jetty', 'how do I start with jetty', or even just 'jetty' if they do not appear to have a token yet. Also trigger if the user mentions needing an API key for Jetty or storing their OpenAI/Gemini key in Jetty.
Runs an autonomous development loop with research and implementation modes. Use when orchestrating iterative research and implementation cycles with dots-based task tracking and git workflow automation.
Multi-agent review of implementation plans. Use after creating a plan but before implementing, especially for complex or risky changes.