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Found 540 Skills
Persist AI chat conversations to Neon Postgres with full support for AI SDK message parts including tools, reasoning, and streaming. Uses UUID v7 for chronologically-sortable IDs.
Complete guide for Google Gemini API using the CORRECT current SDK (@google/genai v1.27+, NOT the deprecated @google/generative-ai). Covers text generation, multimodal inputs (text + images + video + audio + PDFs), function calling, thinking mode, streaming, and system instructions with accurate 2025 model information (Gemini 2.5 Pro/Flash/Flash-Lite with 1M input tokens, NOT 2M). Use when: integrating Gemini API, implementing multimodal AI applications, using thinking mode for complex reasoning, function calling with parallel execution, streaming responses, deploying to Cloudflare Workers, building chat applications, or encountering SDK deprecation warnings, context window errors, model not found errors, function calling failures, or multimodal format errors. Keywords: gemini api, @google/genai, gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite, multimodal gemini, thinking mode, google ai, genai sdk, function calling gemini, streaming gemini, gemini vision, gemini video, gemini audio, gemini pdf, system instructions, multi-turn chat, DEPRECATED @google/generative-ai, gemini context window, gemini models 2025, gemini 1m tokens, gemini tool use, parallel function calling, compositional function calling
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
Implements media and file management components including file upload (drag-drop, multi-file, resumable), image galleries (lightbox, carousel, masonry), video players (custom controls, captions, adaptive streaming), audio players (waveform, playlists), document viewers (PDF, Office), and optimization strategies (compression, responsive images, lazy loading, CDN). Use when handling files, displaying media, or building rich content experiences.
Guides technical evaluation of code review feedback before implementation. Use when receiving PR comments, review suggestions, GitHub feedback, or when asked to address reviewer feedback. Emphasizes verification and reasoned pushback over blind agreement.
[AUTO-INVOKE] MUST be invoked when debugging failed on-chain transactions. Covers transaction receipt analysis, gas diagnosis, calldata decoding, revert reason extraction, and state verification using cast. Trigger: any task involving failed tx analysis, revert debugging, or on-chain transaction troubleshooting.
Use when defending or maintaining social order, rule of law, and peaceful institutions. Applies when countering destabilization, upholding democratic norms, or reasoning through how stable civilizations resist and resolve chaos without violence.
Convert ambiguous user requests into structured USDM requirements documents. Decomposes requirements into Requirement → Reason → Description → Specification hierarchy. Integrates with GitHub Issues, Asana, and Jira tickets as input sources. Use when: "create requirements", "write requirements document", "USDM", "decompose requirements", "requirements definition", "要件定義", "要件を整理", "要件分解".
Use this skill whenever the user asks to analyze, understand, or survey an entire project, codebase, or any collection of files. Trigger phrases include "analyze a large file", "process multiple files", "comprehend this problem", "take a look at these files", "familiarize yourself with this project", or any similar request, however phrased. Also activate when the task involves processing context that exceeds what can be reasoned about in a single pass, when encountering any input larger than ~50KB that requires detailed analysis, or when the user mentions "context comprehension" or "recursive comprehension". This skill TAKES PRIORITY over your default explore subagents for any project-wide or codebase-wide analysis task.
ATP and WTA tennis data via ESPN public endpoints — tournament scores, season calendars, player rankings, player profiles, and news. Zero config, no API keys. Use when: user asks about tennis scores, match results, tournament draws, ATP/WTA rankings, tennis player info, or tennis news. Don't use when: user asks about other sports. Don't use for live point-by-point data — scores update after each set/match.
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).
US stock value investing analysis framework. Systematically evaluates listed companies through 4 core dimensions (ROE sustainability, debt safety, free cash flow quality, economic moat assessment), outputting investment ratings and analytical reasoning. This skill should be used when users mention topics such as whether a US stock is worth holding long-term, fundamental analysis of a company, ROE analysis, debt ratio assessment, free cash flow, economic moat, Buffett-style stock picking, value investing screening, how to read a company's financial reports, whether a stock's valuation is reasonable, etc. Even if users simply ask something general like "What do you think of stock XX?" or "Help me analyze XX's fundamentals," this skill should be triggered to provide a structured value investing analysis framework.