NoSQL

Search local documents, files, notes, and knowledge bases. Index directories, search with BM25/vector/hybrid, get AI answers with citations. Use when user wants to search files, find documents, query notes, look up information in local folders, index a directory, set up document search, build a knowledge base, needs RAG/semantic search, or wants to start a local web UI for their docs.

Redis database expert for caching, data structures, Pub/Sub, Streams, Lua scripting,and performance optimization using Bun.redis native client.Use when working with Redis, caching strategies, key-value stores, real-time data,vector search, or document databases. Works alongside bun-expert skill.

Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy | Dùng khi tìm tài liệu, hướng dẫn, docs, documentation, api docs, tham khảo.

Query and manage local tRPC documentation mirror (25 docs). Search tRPC topics for end-to-end typesafe APIs, routers, procedures, React Query integration, and Next.js setup. Use when implementing tRPC features or answering tRPC-related questions. (user)

AI-powered Signal Factory Core for processing observability signals and maintaining the knowledge graph. Use when: (1) Developing Signal Engines (Freshness, Drift, Contract, DQ, Volume, Anomaly), (2) Configuring Signal Router for normalization and routing, (3) Designing Neptune graph schema for assets and lineage, (4) Implementing DynamoDB state management for incidents. Triggers: "create signal engine", "configure signal router", "design graph schema", "implement signal processing".

Guide for implementing MongoDB - a document database platform with CRUD operations, aggregation pipelines, indexing, replication, sharding, search capabilities, and comprehensive security. Use when working with MongoDB databases, designing schemas, writing queries, optimizing performance, configuring deployments (Atlas/self-managed/Kubernetes), implementing security, or integrating with applications through 15+ official drivers. (project)

Retrieves and inspects chunks from specific PDF documents in Qdrant vector database. Use when user wants to view, inspect, debug, or examine chunks from a particular file, check chunk content, or investigate chunk indexing.

Enhanced Just-In-Time document loading system that intelligently discovers, loads, and caches relevant documentation based on user intent and project context. Use when users need specific documentation, when working with new technologies, when answering domain-specific questions, or when context indicates documentation gaps.

AWS cloud infrastructure and services expert. Use when working with AWS CLI, Terraform for AWS, Lambda, S3, EC2, DynamoDB, IAM, API Gateway, or any AWS service configuration, deployment, troubleshooting, or best practices.

Comprehensive Confluence documentation management. Use when asked to"upload to Confluence", "download Confluence pages", "convert Markdownto Wiki Markup", "sync documentation to Confluence", "search Confluence","create Confluence page", "update Confluence page", "export Confluence","publish to Confluence", or "Confluence CQL query". Handles Wiki Markupconversion, Mermaid/PlantUML diagrams, image handling, large documentuploads without size limits, and Git-to-Confluence sync with mark CLI.

Grist API reference documentation for the grist-mcp-server project. Use when implementing or modifying Grist MCP tools, working with Grist cell values, UserActions, column types, widget options, database schema, or pages/widgets. This skill provides authoritative API specifications.

Use this skill when building MCP (Model Context Protocol) servers with FastMCP in Python. FastMCP is a framework for creating servers that expose tools, resources, and prompts to LLMs like Claude. The skill covers server creation, tool/resource definitions, storage backends (memory/disk/Redis/DynamoDB), server lifespans, middleware system (8 built-in types), server composition (import/mount), OAuth Proxy, authentication patterns, icons, OpenAPI integration, client configuration, cloud deployment (FastMCP Cloud), error handling, and production patterns. It prevents 25+ common errors including storage misconfiguration, lifespan issues, middleware order errors, circular imports, module-level server issues, async/await confusion, OAuth security vulnerabilities, and cloud deployment failures. Includes templates for basic servers, storage backends, middleware, server composition, OAuth proxy, API integrations, testing, and self-contained production architectures.Keywords: FastMCP, MCP server Python, Model Context

Master MongoDB schema design and data modeling patterns. Learn embedding vs referencing, relationships, normalization, and schema evolution. Use when designing databases, normalizing data, or optimizing queries.

This skill should be used when docs-researcher agent needs guidance on "how to search documentation", "WebSearch query patterns", "filtering search results", "documentation research strategy", or "creating knowledge files". Provides systematic methodology for effective technical documentation research.

Hyper-opinionated Claude agent for building production-ready Next.js apps with DynamoDB. Enforces best practices, eliminates technology debates, and focuses on shipping functional apps fast. Use when building full-stack applications with Next.js 15 App Router and AWS DynamoDB.

Frappe server-side Python patterns for controllers, document events, whitelisted APIs, background jobs, and database operations. Use when writing controller logic, creating APIs, handling document events, or processing data on the server.

Collaborate on documents with tracked changes, suggestions, and iterative refinement. Use for reviewing drafts, providing editorial feedback, and collaborative document development.

Complete knowledge domain for Cloudflare Vectorize - globally distributed vector database for buildingsemantic search, RAG (Retrieval Augmented Generation), and AI-powered applications.Use when: creating vector indexes, inserting embeddings, querying vectors, implementing semantic search,building RAG systems, configuring metadata filtering, working with Workers AI embeddings, integratingwith OpenAI embeddings, or encountering metadata index timing errors, dimension mismatches, filtersyntax issues, or insert vs upsert confusion.Keywords: vectorize, vector database, vector index, vector search, similarity search, semantic search,nearest neighbor, knn search, ann search, RAG, retrieval augmented generation, chat with data,document search, semantic Q&A, context retrieval, bge-base, @cf/baai/bge-base-en-v1.5,text-embedding-3-small, text-embedding-3-large, Workers AI embeddings, openai embeddings,insert vectors, upsert vectors, query vectors, delete vectors, metadata filtering, namespace filtering,topK

Project context from ChromaDB knowledge graph. Triggers on "how does this project work", "architecture", "where is X", "why was Y chosen", "project structure", "component relationships", "design decisions".

Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.