NoSQL

Production-ready database schema patterns for AI applications including chat/conversation schemas, RAG document storage with pgvector, multi-tenant organization models, user management, and AI usage tracking. Use when building AI applications, creating database schemas, setting up chat systems, implementing RAG, designing multi-tenant databases, or when user mentions supabase schemas, chat database, RAG storage, pgvector, embeddings, conversation history, or AI application database.

Auto-activating documentation cache with fresh API docs. Fetches and automatically consults cached documentation when user works with libraries/frameworks.

MongoDB best practices including schema design, indexing, and query optimization.

Implement Semantic Retrieval Optimization for AI search visibility. Use when performing SRO audits, building entity maps, designing SCN architecture, writing retrieval-optimized content, implementing schema markup, calibrating trust signals, analyzing query intent, assessing technical eligibility, or creating AI-ready content strategies. Triggers on semantic SEO, entity mapping, SCN, E-E-A-T, AI search optimization, passage engineering, knowledge graph, trust signals, or retrieval optimization requests.

Creates comprehensive ServiceNow change request documentation when users request CR creation, need to generate change requests, ask to prepare deployment documentation, or mention creating release documentation. Automatically analyzes git history, queries functional requirements from BigQuery, generates technical change summaries, and creates properly formatted YAML files for L'Oréal BTDP deployments.

Expert knowledge of WAI-ARIA (Accessible Rich Internet Applications). Use when users ask about ARIA roles, states, properties, accessible name computation, ARIA attributes (aria-label, aria-labelledby, aria-describedby, etc.), widget roles, landmark roles, live regions, ARIA best practices, or how to implement accessible interactive components. Also use for questions about ARIA specifications, API mappings (core-aam, html-aam), digital publishing ARIA (dpub-aria), graphics ARIA, or any ARIA implementation questions.

Apply inclusion/exclusion criteria systematically in literature reviews. Use when: (1) Screening abstracts, (2) Reviewing full texts, (3) Documenting screening decisions, (4) Ensuring PRISMA compliance.

Design and implement the Viet K-Connect document generation template engine (DB-driven wizard schema + PDF renderSpec + history + Storage upload). Start with 2 templates and scale linearly to 50 without hardcoding.

Manages client case database in mis_documentos/ directory. Use when starting new legal work, creating documents, or generating proposals to find similar cases and maintain consistent style.

Master MongoDB index creation and types. Learn single-field, compound, unique, text, geospatial, and TTL indexes. Optimize query performance dramatically with proper indexing.

Conduct comprehensive, systematic literature reviews using multiple databases (PubMed, bioRxiv, Semantic Scholar, OpenAlex). Creates documented searches, synthesizes findings thematically, verifies citations, and generates professional markdown reports with multiple citation styles (APA, Nature, Vancouver). Use when the user needs thorough literature research or types /deep_research.

Work with ArcGIS Knowledge graphs for storing and querying connected data. Use for graph databases, relationship visualization, and openCypher queries.

Documentation for Kubernetes Agent Sandbox - a CRD-based system for managing isolated AI agent execution environments. Use for queries about Sandbox CRDs (Sandbox, SandboxTemplate, SandboxClaim, SandboxWarmPool), Python SDK (SandboxClient, SandboxRouter, ComputerUseExtension), network policies, security configurations, and implementation examples. Keywords kubernetes sandbox, agent sandbox, CRD, python sdk, agentic-sandbox-client, isolated environment, gvisor, network policy.

Specialized skill for designing AWS DynamoDB single-table schemas with optimized access patterns. Use when modeling data, designing table structure, or optimizing DynamoDB queries for production applications.

RAG (Retrieval Augmented Generation) implementation patterns including document chunking, embedding generation, vector database integration, semantic search, and RAG pipelines. Use when building RAG systems, implementing semantic search, creating knowledge bases, or when user mentions RAG, embeddings, vector database, retrieval, document chunking, or knowledge retrieval.

Master NoSQL databases: MongoDB, Redis, DynamoDB, and more.

Export query results to various formats (CSV, JSON, Excel, Markdown tables) with proper formatting and headers. Use when saving analysis results to files.This skill provides data export utilities for multiple formats:- CSV: Comma-separated with headers, customizable delimiters- JSON: Structured data with pretty-print option- Excel: Multiple sheets, cell formatting, formulas- Markdown: Tables for documentationTriggers: "export data", "save results", "output CSV", "output JSON", "output Excel", "データ出力", "結果保存", "エクスポート"

Generate comprehensive daily status reports after solving AoC puzzles. Documents success/failure, challenges faced, retry attempts, execution time, and insights for workflow tuning. Use after completing each day's puzzle to provide developer feedback on automation effectiveness.

Obsidian LiveSync の CouchDbClient の構造と使用方法を説明します。CouchDbRepository トレイトの実装方法、HTTP プロキシパターン（forward_request）、longpoll リクエストの処理、メトリクス収集、ヘルスチェックの実装を理解・拡張する際に使用します。CouchDB 関連の機能追加、トラブルシューティング、パフォーマンス改善を依頼されたときに使用してください。

Production-grade agentic system development with LlamaIndex in Python. Covers semantic ingestion (SemanticSplitterNodeParser, CodeSplitter, IngestionPipeline), retrieval strategies (BM25Retriever, hybrid search, alpha weighting), PropertyGraphIndex with graph stores (Neo4j), context RAG (RouterQueryEngine, SubQuestionQueryEngine, LLMRerank), agentic orchestration (ReAct, Workflows, FunctionTool), and observability (Arize Phoenix). Use when asked to "build a LlamaIndex agent", "set up semantic chunking", "index source code", "implement hybrid search", "create a knowledge graph with LlamaIndex", "implement query routing", "debug RAG pipeline", "add Phoenix observability", or "create an event-driven workflow". Triggers on "PropertyGraphIndex", "SemanticSplitterNodeParser", "CodeSplitter", "BM25Retriever", "hybrid search", "ReAct agent", "Workflow pattern", "LLMRerank", "Text-to-Cypher".