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Found 25 Skills
Expert guidance for OpenAI API development including GPT models, Assistants API, function calling, embeddings, and best practices for production applications.
Claude AI cookbooks - code examples, tutorials, and best practices for using Claude API. Use when learning Claude API integration, building Claude-powered applications, or exploring Claude capabilities.
LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.
Z.ai API integration for building applications with GLM models. Use when working with Z.ai/ZhipuAI APIs for: (1) Chat completions with GLM-4.7/4.6/4.5 models, (2) Vision/multimodal tasks with GLM-4.6V, (3) Image generation with GLM-Image or CogView-4, (4) Video generation with CogVideoX-3 or Vidu models, (5) Audio transcription with GLM-ASR-2512, (6) Function calling and tool use, (7) Web search integration, (8) Translation, slide/poster generation agents. Triggers: Z.ai, ZhipuAI, GLM, BigModel, Zhipu, CogVideoX, CogView, Vidu.
Provides tool and function calling patterns with LangChain4j. Handles defining tools, function calls, and LLM agent integration. Use when building agentic applications that interact with tools.
Use when designing agent tools, creating tool descriptions, implementing MCP tools, or asking about "tool design", "agent tools", "tool descriptions", "MCP", "function calling", "tool consolidation"
AI integration with Vercel AI SDK - Build AI-powered applications with streaming, function calling, and tool use. Trigger: When implementing AI features, when using useChat or useCompletion, when building chatbots, when integrating LLMs, when implementing function calling.
A skill that equips you with real-time, source-grounded web search and content retrieval using the Exa API—optimized for balanced relevance and speed (type="auto") and full-text extraction for downstream reasoning, RAG, and code assistance. Powering agents with fast, high-quality web search by Exa.AI.
Chat with LLM models using ModelsLab's OpenAI-compatible Chat Completions API. Supports 60+ models including DeepSeek R1, Meta Llama, Google Gemini, Qwen, and Mistral with streaming, function calling, and structured outputs.
Use this skill when building production LLM applications, implementing guardrails, evaluating model outputs, or deciding between prompting and fine-tuning. Triggers on LLM app architecture, AI guardrails, output evaluation, model selection, embedding pipelines, vector databases, fine-tuning, function calling, tool use, and any task requiring production AI application design.
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
Advanced Gemini 3 Pro features including function calling, built-in tools (Google Search, Code Execution, File Search, URL Context), structured outputs, thought signatures, context caching, batch processing, and framework integration. Use when implementing tools, function calling, structured JSON output, context caching, batch API, LangChain, Vercel AI, or production features.