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Found 316 Skills
Build search applications and query log analytics data with OpenSearch. Use this skill when the user mentions OpenSearch, search app, index setup, search architecture, semantic search, vector search, hybrid search, BM25, dense vector, sparse vector, agentic search, RAG, embeddings, KNN, PDF ingestion, document processing, or any related search topic. Also use for log analytics and observability — when the user wants to set up log ingestion, query logs with PPL, analyze error patterns, set up index lifecycle policies, investigate traces, or check stack health. Activate even if the user says log analysis, Fluent Bit, Fluentd, Logstash, syslog, traceId, OpenTelemetry, or log analytics without mentioning OpenSearch.
Semantic skill discovery and routing using GraphRAG, vector embeddings, and multi-tool search. Automatically matches user intent to the most relevant skills from 144+ available options using ck semantic search, LEANN RAG, and knowledge graph relationships. Triggers on /meta queries, complex multi-domain tasks, explicit skill requests, or when task complexity exceeds threshold (files>20, domains>2, complexity>=0.7).
Production-ready starter project for React + Cloudflare Workers + Hono with core services (D1, KV, R2, Workers AI) and optional advanced features (Clerk Auth, AI Chat, Queues, Vectorize). Complete with planning docs, session handoff protocol, and enable scripts for opt-in features. Use when: starting new full-stack project, creating Cloudflare app, scaffolding web app, AI-powered application, chat interface, RAG application, need complete starter, avoid setup time, production-ready template, full-stack boilerplate, React Cloudflare starter. Prevents: service configuration errors, binding setup mistakes, frontend-backend connection issues, CORS errors, auth integration problems, AI SDK setup confusion, missing planning docs, incomplete project structure, hours of initial setup. Keywords: cloudflare scaffold, full-stack starter, react cloudflare, hono template, production boilerplate, AI SDK integration, workers AI, complete starter project, D1 KV R2 setup, web app template, chat application scaffold, RAG starter, planning docs included, session handoff, tailwind v4 shadcn, typescript starter, vite cloudflare plugin, all services configured
Expert knowledge for Azure Arc development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing Arc-enabled Kubernetes, servers, SQL MI, Edge RAG, resource bridge, or SCVMM/VMware integration, and other Azure Arc related development tasks. Not for Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Virtual Machines (use azure-virtual-machines), Azure Policy (use azure-policy), Azure Monitor (use azure-monitor).
Wind MCP Data Bridge Skill (v1.1.0, 6 servers / 19 tools). Route by `server_type`: (1) `quote` for market data (A-shares/Hong Kong stocks snapshots, daily/weekly/monthly K-lines, minute-level data); (2) `fund_data` for fund-related data (profile/finances/holdings/performance/holders/management company); (3) `stock_data` for in-depth stock data (profile/financial fundamentals/equity structure/events/technical indicators/risk); (4) `financial_docs` for document RAG (announcements/financial news); (5) `economic_data` for EDB macro + industry economic indicators; (6) `analytics_data` for general NL → Wind data. WIND_API_KEY is required (obtained by logging into the Developer Center at aimarket.wind.com.cn). Trigger scenarios: A-shares/Hong Kong stock codes/K-lines/minute-level data, any dimension of funds, stock financial reports/valuation, listed company announcements/financial news, macroeconomic data, cross-comparison of targets. **Excluded**: US stocks/European stocks/Japanese stocks, exchange rates/futures quotes, cryptocurrencies, non-financial data.
Implements knowledge graphs for AI-enhanced relational knowledge. Covers ontology design, graph database selection (Neo4j, Neptune, ArangoDB, TigerGraph), entity extraction, hybrid graph-vector architecture, query patterns, and AI integration. Use when implementing knowledge graphs, designing ontologies, extracting entities and relationships, selecting a graph database, or building hybrid graph-vector search. Use for knowledge graph, ontology design, entity resolution, graph RAG, hallucination detection. For architecture selection and governance, use the knowledge-base-manager skill. For document retrieval pipelines, use the rag-implementer skill.
Automatically collect hot topics in the AI field or complete AI technical article writing in the writing style of 'Second Brother' according to specified topics. It focuses on actual tests of AI Coding tools (Claude Code, Qoder, Cursor, TRAE, etc.), engineering implementation of large models (SpringAI, LangChain, RAG, etc.), AI Agent and workflow orchestration, evaluation of domestic large models (GLM, Tongyi Qianwen, DeepSeek, MiniMax, Kimi, etc.), and evaluation of various AI tools and Agent tools. Trigger keywords: write an AI article, AI technical article, large model evaluation, AI tool actual test, GLM, Claude Code, Qoder, Cursor, TRAE, SpringAI, RAG, Agent, workflow, domestic large model, collect AI hot topics, AI topic, etc.
Comprehensive prompt and context engineering for any AI system. Four modes: (1) Craft new prompts from scratch, (2) Analyze existing prompts with diagnostic scoring and optional improvement, (3) Convert prompts between model families (Claude/GPT/Gemini/Llama), (4) Evaluate prompts with test suites and rubrics. Adapts all recommendations to model class (instruction-following vs reasoning). Validates findings against current documentation. Use for system prompts, agent prompts, RAG pipelines, tool definitions, or any LLM context design. NOT for running prompts, generating content, or building agents.
Complete Google Gemini API reference for 2026. Use whenever writing code that calls Gemini models. Covers the google-genai SDK, Gemini 3/3.1 models, thought signatures, thinking config, Interactions API, File Search (managed RAG), Computer Use, URL Context, Nano Banana image gen, Live API, ephemeral tokens, TTS, Veo video gen, Lyria music gen, and all tools. ALWAYS prefer `from google import genai` over any legacy import. Use this skill for ANY Gemini API question, even simple ones.
Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series
Use when connecting to a self-hosted memory backend, searching, storing, or managing memories, importing connection tokens, or troubleshooting retrieval issues. Use this skill whenever the user mentions memory search, RAG retrieval, embedding, memory storage, multimodal document upload, knowledge queries, or wants to connect to a memory service, even if they do not explicitly say "transcendence-memory".
Expert guidance for building conversational AI applications with Chainlit framework in Python. Use when (1) creating chat interfaces for LLM applications, (2) building apps with OpenAI, LangChain, LlamaIndex, or Mistral AI, (3) implementing streaming responses, (4) adding UI elements like images, files, charts, (5) handling user file uploads, (6) implementing authentication (OAuth, password), (7) creating multi-step workflows with visible steps, (8) building RAG applications with document upload, or (9) deploying chat apps to web, Slack, Discord, or Teams.