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Found 1,431 Skills
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
This skill provides comprehensive knowledge for working with the Anthropic Messages API (Claude API). It should be used when integrating Claude models into applications, implementing streaming responses, enabling prompt caching for cost savings, adding tool use (function calling), processing images with vision capabilities, or using extended thinking mode. Use when building chatbots, AI assistants, content generation tools, or any application requiring Claude's language understanding. Covers both server-side implementations (Node.js, Cloudflare Workers, Next.js) and direct API access. Keywords: claude api, anthropic api, messages api, @anthropic-ai/sdk, claude streaming, prompt caching, tool use, vision, extended thinking, claude 3.5 sonnet, claude 3.7 sonnet, claude sonnet 4, function calling, SSE, rate limits, 429 errors
Complete knowledge domain for Cloudflare Workers AI - Run AI models on serverless GPUs across Cloudflare's global network. Use when: implementing AI inference on Workers, running LLM models, generating text/images with AI, configuring Workers AI bindings, implementing AI streaming, using AI Gateway, integrating with embeddings/RAG systems, or encountering "AI_ERROR", rate limit errors, model not found, token limit exceeded, or neurons exceeded errors. Keywords: workers ai, cloudflare ai, ai bindings, llm workers, @cf/meta/llama, workers ai models, ai inference, cloudflare llm, ai streaming, text generation ai, ai embeddings, image generation ai, workers ai rag, ai gateway, llama workers, flux image generation, stable diffusion workers, vision models ai, ai chat completion, AI_ERROR, rate limit ai, model not found, token limit exceeded, neurons exceeded, ai quota exceeded, streaming failed, model unavailable, workers ai hono, ai gateway workers, vercel ai sdk workers, openai compatible workers, workers ai vectorize
Complete guide for OpenAI's traditional/stateless APIs: Chat Completions (GPT-5, GPT-4o), Embeddings, Images (DALL-E 3), Audio (Whisper + TTS), and Moderation. Includes both Node.js SDK and fetch-based approaches for maximum compatibility. Use when: integrating OpenAI APIs, implementing chat completions with GPT-5/GPT-4o, generating text with streaming, using function calling/tools, creating structured outputs with JSON schemas, implementing embeddings for RAG, generating images with DALL-E 3, transcribing audio with Whisper, synthesizing speech with TTS, moderating content, deploying to Cloudflare Workers, or encountering errors like rate limits (429), invalid API keys (401), function calling failures, streaming parse errors, embeddings dimension mismatches, or token limit exceeded. Keywords: openai api, chat completions, gpt-5, gpt-5-mini, gpt-5-nano, gpt-4o, gpt-4-turbo, openai sdk, openai streaming, function calling, structured output, json schema, openai embeddings, text-embedding-3, dall-e-3, image generation, whisper api, openai tts, text-to-speech, moderation api, openai fetch, cloudflare workers openai, openai rate limit, openai 429, reasoning_effort, verbosity
Quick-start guide and API overview for the OpenServ Ideaboard - a platform where AI agents can submit ideas, pick up work, collaborate with multiple agents, and deliver x402 payable services. Use when interacting with the Ideaboard or building agents that find and ship ideas. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.
Resend Box is a local email sandbox that mocks the Resend API and captures emails for inspection. Use this skill when working with the Resend SDK or SMTP to send emails during development. It captures all emails sent via the Resend SDK (when RESEND_BASE_URL points to the sandbox) or via SMTP (port 1025). Use to verify emails are sent correctly, inspect email content, or test email templates without sending real emails.
REST API for cross-chain and same-chain token swaps, bridging, and DeFi operations. USE THIS SKILL WHEN USER WANTS TO: - Swap tokens between different blockchains (e.g., "swap USDC on Ethereum to ETH on Arbitrum") - Bridge tokens to another chain (e.g., "move my ETH from mainnet to Optimism") - Swap tokens on the same chain with best rates (e.g., "swap ETH to USDC on Polygon") - Find the best route or quote for a token swap across chains - Execute DeFi operations across chains (zap, bridge+swap+deposit, yield farming entry) - Build multi-chain payment flows (accept any token, settle in specific token) - Check supported chains, tokens, or bridges for cross-chain transfers - Track status of a cross-chain transaction - Build backend services (Python, Go, Rust, etc.) that need cross-chain swaps - Integrate cross-chain functionality via HTTP/REST (not JavaScript SDK)
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
Migrate a .NET 9 project or solution to .NET 10 and resolve all breaking changes. USE FOR: upgrading TargetFramework from net9.0 to net10.0, fixing build errors after updating the .NET 10 SDK, resolving source and behavioral changes in .NET 10 / C# 14 / ASP.NET Core 10 / EF Core 10, updating Dockerfiles for Debian-to-Ubuntu base images, resolving obsoletion warnings (SYSLIB0058-SYSLIB0062), adapting to SDK/NuGet changes (NU1510, PrunePackageReference), migrating System.Linq.Async to built-in AsyncEnumerable, fixing OpenApi v2 API changes, cryptography renames, and C# 14 compiler changes (field keyword, extension keyword, span overloads). DO NOT USE FOR: .NET Framework migrations, upgrading from .NET 8 or earlier (use migrate-dotnet8-to-dotnet9 first), greenfield .NET 10 projects, or cosmetic modernization. LOADS REFERENCES: csharp-compiler, core-libraries, sdk-msbuild (always); aspnet-core, efcore, cryptography, extensions-hosting, serialization-networking, winforms-wpf, containers-interop (selective).
Develop high-performance C/C++ plugins for Stata using the stplugin.h SDK. Use when the user asks to create a Stata plugin, write C/C++ code for Stata, accelerate a Stata command with C, build cross-platform Stata plugins, or translate/port a Python or R package into Stata. Covers the full lifecycle: SDK setup, data flow, memory safety, .ado wrappers with preserve/merge, cross-platform compilation, performance optimization (pthreads, pre-sorted indices, XorShift RNG), debugging, and distribution via net install. Also includes a translation workflow for porting Python/R packages to Stata — wrapping existing C++ backends when available, or writing C from scratch when not.
Build cross-platform VoIP calling apps with Flutter using Telnyx WebRTC SDK. Covers authentication, making/receiving calls, push notifications (FCM + APNS), call quality metrics, and AI Agent integration. Works on Android, iOS, and Web.