debug-mcp
Systematic debugging workflow for MCP servers and Microsoft Copilot Studio integrations, featuring common fix patterns and validation scripts.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
486 skills found
Systematic debugging workflow for MCP servers and Microsoft Copilot Studio integrations, featuring common fix patterns and validation scripts.
Advanced web search and reasoning tool for OpenClaw agents. Features citation-heavy synthesis, multi-step reasoning, and live internet access via OpenRouter.
Create professional data visualizations with Python using matplotlib, seaborn, and plotly. Includes chart selection guidance, design principles, accessibility standards, and code patterns for publication-quality figures.
Resume a paused experimental loop by restoring branch context, loading configuration, reading history, and identifying optimization patterns for continued iteration.
React component development guide for LobeHub, including styling with antd-style, layout building with @lobehub/ui, and routing management.
Method-driven planning workflow that intelligently decomposes tasks into structured plan.md files using zen-mcp tools, adapting to user clarity and automation needs.
React Native best practices for Expo and bare workflow. Supports project structure, navigation, NativeWind styling, platform-specific code, and TypeScript integration.
Access Y Combinator’s library of 443+ startup resources for expert advice on fundraising, co-founders, product development, growth, and scaling your business.
Generate publication-quality statistical plots from CSV or JSON data files using AI-driven automated visualization.
Scaffold and register new sensor, actuator, or service tools for familiar-ai, automating file creation and boilerplate integration in agent.py and config.py.
Execute implementation plans in small, verifiable batches with pause-for-feedback checkpoints to prevent drift and ensure code quality.
Epistemic safety analysis for JSON data in prompts to prevent LLM hallucinations and reasoning errors when handling incomplete or large-scale datasets.