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Found 44 Skills
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
Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.
Use this skill when designing AI agent architectures, implementing tool use, building multi-agent systems, or creating agent memory. Triggers on AI agents, tool calling, agent loops, ReAct pattern, multi-agent orchestration, agent memory, planning strategies, agent evaluation, and any task requiring autonomous AI agent design.
Expert integration patterns for Claude API and TypeScript SDK covering Messages API, streaming responses, tool use, error handling, token optimization, and production-ready implementations for building AI-powered applications
Use xAI's Grok model with agentic tool calling for X (Twitter) search, web search, code execution, and real-time data access. Invoke when user needs Twitter/X insights, current events, alternative perspectives, or complex multi-step research.
LangGraph-based agent framework for consistent tool calling with automatic tool loops. Use when you need reliable multi-step task execution with OpenAI-compatible providers (Z.AI/GLM-5, OpenRouter, Groq, DeepSeek, Ollama).
Use this skill when working with Apple's Foundation Models framework for on-device AI and LLM capabilities in iOS/macOS apps
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.
Production-grade Next.js chatbot builder. Covers tool calling with human-in-the-loop (HITL) approval, PostgreSQL session persistence, GDPR consent gating, SQL-first search, per-tool UI rendering, message feedback, and follow-up suggestions. Use when building chat apps, conversational AI interfaces, customer support bots, or any chatbot needing database-backed sessions, tool approval workflows, consent gating, or custom tool output components. Reference implementation: fair-helpdesk project.
PokeClaw (PocketClaw) — on-device Android AI phone agent using Gemma 4 via LiteRT-LM with tool calling, accessibility automation, and optional cloud models.
Quickly build Chainlit AI chat demos for product demos, proof-of-concept, and stakeholder presentations. Trigger words: chainlit, build demo, chat demo, conversation demo
Market overview. Use this skill whenever the user asks about overall market. Trigger phrases include: how is the market, market overview, what is happening in crypto. MCP tools: info_marketsnapshot_get_market_overview, info_coin_get_coin_rankings, info_platformmetrics_get_defi_overview, news_events_get_latest_events, info_macro_get_macro_summary.