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Found 277 Skills
Production MLOps and ML/LLM/agent security skill for deploying and operating ML systems in production (registry + CI/CD, serving, monitoring/drift, evaluation loops, incident response/runbooks, and governance), including GenAI security (prompt injection, jailbreaks, RAG security, privacy, and supply chain).
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.
Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.
PDF data extraction tool. Use it when users mention "PDF extraction", "PDF to Markdown", "PDF parsing", "extract PDF content", "PDF to JSON", "RAG PDF". OpenDataLoader PDF is currently the top-ranked PDF parser in benchmark tests, supporting local mode (fast, deterministic) and hybrid AI mode (for complex tables, scanned documents, formulas), with output formats including Markdown, JSON (with bounding boxes), and HTML. It is suitable for scenarios where structured data needs to be extracted from PDFs for RAG/LLM pipelines, or where batch processing of PDF documents is required.
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.
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.
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.
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.
AI-first security scanning with Medusa. 3,000+ detection patterns covering AI/ML, agents, MCP, RAG, prompt injection, and traditional SAST vulnerabilities. Wraps Medusa CLI with SARIF/JSON parsing, structured finding output, OWASP mapping, and remediation guidance.
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
INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.