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Found 1,228 Skills
This skill is designed for any requests related to running/executing/evaluating code (such as run this code, execute script, plot with Python, run code/plot/execute script). Create a code_latest sandbox via AgentBay SDK, call run_code to execute and return results. Supports Python, JavaScript, R, Java.
Gemini 3 Pro API/SDK integration for text generation, reasoning, and chat. Covers setup, authentication, thinking levels, streaming, and production deployment. Use when working with Gemini 3 Pro API, Python SDK, Node.js SDK, text generation, chat applications, or advanced reasoning tasks.
Integrate and embed OpenAI ChatKit UI into TypeScript/JavaScript frontends (Next.js, React, or vanilla) using either hosted workflows or a custom backend (e.g. Python with the Agents SDK). Use this Skill whenever the user wants to add a ChatKit chat UI to a website or app, configure api.url, auth, domain keys, uploadStrategy, or debug blank/buggy ChatKit widgets.
This skill provides comprehensive knowledge for building applications with Cloudflare Sandboxes SDK, which enables secure, isolated code execution in full Linux containers at the edge. It should be used when executing untrusted code, running Python/Node.js scripts, performing git operations, building AI code execution systems, creating interactive development environments, or implementing CI/CD workflows that require full OS capabilities. Use when: Setting up Cloudflare Sandboxes, executing Python/Node.js code safely, managing stateful development environments, implementing AI code interpreters, running shell commands in isolation, handling git repositories programmatically, building chat-based coding agents, creating temporary build environments, processing files with system tools (ffmpeg, imagemagick, etc.), or when encountering issues with container lifecycle, session management, or state persistence. Keywords: cloudflare sandbox, container execution, code execution, isolated environment, durable objects, linux container, python execution, node execution, git operations, code interpreter, AI agents, session management, ephemeral container, workspace, sandbox SDK, @cloudflare/sandbox, exec(), getSandbox(), runCode(), gitCheckout(), ubuntu container
Use this skill when building MCP (Model Context Protocol) servers with FastMCP in Python. FastMCP is a framework for creating servers that expose tools, resources, and prompts to LLMs like Claude. The skill covers server creation, tool/resource definitions, storage backends (memory/disk/Redis/DynamoDB), server lifespans, middleware system (8 built-in types), server composition (import/mount), OAuth Proxy, authentication patterns, icons, OpenAPI integration, client configuration, cloud deployment (FastMCP Cloud), error handling, and production patterns. It prevents 25+ common errors including storage misconfiguration, lifespan issues, middleware order errors, circular imports, module-level server issues, async/await confusion, OAuth security vulnerabilities, and cloud deployment failures. Includes templates for basic servers, storage backends, middleware, server composition, OAuth proxy, API integrations, testing, and self-contained production architectures. Keywords: FastMCP, MCP server Python, Model Context Protocol Python, fastmcp framework, mcp tools, mcp resources, mcp prompts, fastmcp storage, fastmcp memory storage, fastmcp disk storage, fastmcp redis, fastmcp dynamodb, fastmcp lifespan, fastmcp middleware, fastmcp oauth proxy, server composition mcp, fastmcp import, fastmcp mount, fastmcp cloud, fastmcp deployment, mcp authentication, fastmcp icons, openapi mcp, claude mcp server, fastmcp testing, storage misconfiguration, lifespan issues, middleware order, circular imports, module-level server, async await mcp
Comprehensive guide for MDAnalysis - the Python library for analyzing molecular dynamics trajectories. Use for trajectory loading, RMSD/RMSF calculations, distance/angle/dihedral analysis, atom selections, hydrogen bonds, solvent accessible surface area, protein structure analysis, membrane analysis, and integration with Biopython. Essential for MD simulation analysis.
A Pythonic interface to the HDF5 binary data format. It allows you to store huge amounts of numerical data and easily manipulate that data from NumPy. Features a hierarchical structure similar to a file system. Use for storing datasets larger than RAM, organizing complex scientific data hierarchically, storing numerical arrays with high-speed random access, keeping metadata attached to data, sharing data between languages, and reading/writing large datasets in chunks.
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
Comprehensive guide for Biopython - the premier Python library for computational biology and bioinformatics. Use for DNA/RNA/protein sequence analysis, file I/O (FASTA, FASTQ, GenBank, PDB), sequence alignment, BLAST searches, phylogenetic analysis, structure analysis, and NCBI database access.
Dual skill for deploying scientific models. FastAPI provides a high-performance, asynchronous web framework for building APIs with automatic documentation. Streamlit enables rapid creation of interactive data applications and dashboards directly from Python scripts. Load when working with web APIs, model serving, REST endpoints, interactive dashboards, data visualization UIs, scientific app deployment, async web frameworks, Pydantic validation, uvicorn, or building production-ready scientific tools.
Composable transformations of Python+NumPy programs. Differentiate, vectorize, JIT-compile to GPU/TPU. Built for high-performance machine learning research and complex scientific simulations. Use for automatic differentiation, GPU/TPU acceleration, higher-order derivatives, physics-informed machine learning, differentiable simulations, and automatic vectorization.
Comprehensive package and environment management using pixi - a fast, modern, cross-platform package manager. Use when working with pixi projects for (1) Project initialization and configuration, (2) Package management (adding, removing, updating conda/PyPI packages), (3) Environment management (creating, activating, managing multiple environments), (4) Feature management (defining and composing feature sets), (5) Task execution and management, (6) Global tool installation, (7) Dependency resolution and lock file management, or any other pixi-related operations. Supports Python, C++, R, Rust, Node.js and other languages via conda-forge ecosystem.