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Found 334 Skills
Generate video summary reports using the VSS video_search_frag extension with Long Video Summarization (LVS), Enterprise RAG knowledge retrieval, and human-in-the-loop parameter collection. Use when: user wants to generate a video summary, report, or analysis using the frag pipeline.
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).
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.
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.
Build AI agents with Cloudflare Agents SDK on Workers + Durable Objects. Provides WebSockets, state persistence, scheduling, and multi-agent coordination. Prevents 23 documented errors. Use when: building WebSocket agents, RAG with Vectorize, MCP servers, or troubleshooting "Agent class must extend", "new_sqlite_classes", binding errors, WebSocket payload limits.
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.
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.
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).
AWS-curated copy-paste prompts for AI coding agents (MVP scaffolding, RAG chatbot with Claude on Bedrock, security baseline evaluation, cost anomaly detection, GPU quota requests, EKS deployment, Well-Architected review, etc.) plus downloadable installable agents (Multi-Account Transition Advisor, Bill Shock Preventer, Service Quota Agent, Bedrock Model Availability Agent, AWS DB Advisor). Use when the user asks for a prompt to do X on AWS, wants an installable agent for multi-account / cost monitoring / quota management / Bedrock model availability / database selection, or asks how to use AWS prompts. For migration intent (GCP to AWS, OpenAI/Gemini to Bedrock), route to the migration-to-aws skill. Do not use for: factual AWS Activate / programs / credits questions, learn articles, sample architectures, or for prompts that are not in the bundled `references/prompt-library/` tree.
Crawl an entire website or section and extract content from every page. Use when you need content from many pages under a common URL prefix: "crawl the whole site", "get all docs pages", "scrape every blog post", "download the full docs for RAG", "extract all pages under /api". Async BFS — starts a job and polls for results. Step 4 of the crw workflow ladder.
Reference skill for building production-ready crw integrations. Covers verb selection, call surfaces (CLI/MCP/REST), post-filtering strategies, context-window hygiene, Hybrid RAG patterns, common pitfalls, and crw-specific operational considerations (search backend limits, renderer pool, proxy rotation). Load this when writing application code that embeds crw, designing a multi-step agent workflow, or debugging an integration that isn't behaving as expected.
Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval