FastMCP Development
Build and manage MCP servers using the FastMCP framework. Guide for creating tools, resources, prompts, Claude Desktop integration, and deployment with Python and TypeScript.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
603 skills found
Build and manage MCP servers using the FastMCP framework. Guide for creating tools, resources, prompts, Claude Desktop integration, and deployment with Python and TypeScript.
Train and manage neural networks in distributed E2B sandboxes using the Flow Nexus platform, supporting custom architectures like Transformers, LSTMs, and GANs.
Three.js geometry generation: built-in shapes, BufferGeometry, vertex manipulation, custom meshes, and performance-optimized instanced rendering.
Perform advanced video analysis using Google's Gemini API: summarize content, transcribe audio, extract timestamps, clip segments, and analyze YouTube URLs or local files with support for multiple models and long contexts.
A systematic, multi-angle web research agent. Use for deep investigation, complex queries, and as a mandatory pre-research step before content generation to ensure evidence-backed, high-quality results.
Foundational Python library for static, animated, and interactive data visualization. Provides fine-grained control over plot elements for scientific, publication-ready figures.
Automates the creation and maintenance of CLAUDE.md files. It monitors codebase evolution and keeps project memory in sync with file changes, structure, and build commands.
Verify Everything Search integration (CLI, HTTP, SDK) for inventory_master to ensure connectivity, service health, and provider availability.
Universal MCP client for connecting to any MCP server with progressive disclosure. Wraps MCP servers as skills to prevent context window bloat from tool definitions. Use for Zapier, GitHub, sequential thinking, and file operations.
Core component library and design system patterns for consistent UI development using design tokens.
Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.
A testing utility for the npm-agentskills framework, designed to validate Nuxt module integration and skill discovery patterns.