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Found 108 Skills
Comprehensive Cline SDK skill for building AI agents. Covers the Agent runtime, ClineCore sessions, custom tools, plugins, events, LLM providers, scheduling, multi-agent teams, and production deployment. Use for any task involving @cline/sdk or its sub-packages.
HertzFlow on-chain trade-decision intelligence. Currently covers Binance Alpha forensic across all surf-SQL EVM chains (BSC / Ethereum / Arbitrum / Base / Polygon / Optimism) — insider distribution, 真实派发 confirmed sell-out, 筹码三分法 (operator / CEX pool / verifiable retail), anomaly waves, monitoring exports. Solana runs in HOLDER_SNAPSHOT mode. Auto-trigger whenever the user pastes a raw 0x-prefixed 40-hex EVM CA, a Solana base58 CA, mentions a Binance Alpha token by ticker, or asks about 链上 forensic / 内幕出货 / 派发 / chip structure / quiet insider / Alpha distribution / on-chain dump — even if they don't say "hertzflow" explicitly. Pipeline runs deterministically (~2-10 min per CA depending on activity + surf cache state); LLM only fills narrative slots, never picks the verdict or writes SQL. Perp metrics, bridge audits, and HertzFlow core contract analysis sub-domains are coming — when those ship, this skill will dispatch to them based on input pattern (perp symbol, bridge protocol name, etc.) using the router table below. REQUIRES a Surf account + SURF_API_KEY. New users get 2000 free credits (~6-8 reports) via the HertzFlow private invite. Full forensic costs ~$1.5-3 USD per CA in Surf credits after the free tier runs out.
Create a Mastra project using create-mastra and smoke test the studio in Chrome
Provides patterns to build Retrieval-Augmented Generation (RAG) systems for AI applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Consult external LLMs (Gemini, OpenAI/Codex, Qwen) for second opinions, alternative plans, independent reviews, or delegated tasks. Use when a user asks for another model's perspective, wants to compare answers, or requests delegating a subtask to Gemini/Codex/Qwen.
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.
Expert in the Vercel AI SDK. Covers Core API (generateText, streamText), UI hooks (useChat, useCompletion), tool calling, and streaming UI components with React and Next.js.
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
Model Context Protocol (MCP) server development and AI/ML integration patterns. Covers MCP server implementation, tool design, resource handling, and LLM integration best practices. Use when developing MCP servers, creating AI tools, integrating with LLMs, or when asking about MCP protocol, prompt engineering, or AI system architecture.
Guide pour la création de serveurs MCP (Model Context Protocol) de qualité permettant aux LLM d'interagir avec des services externes via des outils bien conçus. À utiliser pour construire des serveurs MCP intégrant des API ou services externes, en Python (FastMCP) ou Node/TypeScript (MCP 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).
Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (formerly neo4j-genai). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever), retrieval_query Cypher fragments, query_params, pipeline wiring (GraphRAG + LLM), embedder setup, index creation, and LangChain/LlamaIndex integration. Does NOT handle KG construction from documents — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.