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Found 4,140 Skills
Build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered features with minimal boilerplate code in Java applications.
Expert integration patterns for Claude API and TypeScript SDK covering Messages API, streaming responses, tool use, error handling, token optimization, and production-ready implementations for building AI-powered applications
Qdrant vector database integration patterns with LangChain4j. Store embeddings, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
Expert in Spring Data Neo4j integration patterns for graph database development. Use when working with Neo4j graph databases, node entities, relationships, Cypher queries, reactive Neo4j operations, or Spring Data Neo4j repositories. Essential for graph data modeling, relationship mapping, custom queries, and Neo4j testing strategies.
Build fast unit and integration tests with Vitest 4.x. Covers configuration for Workers/React/Node, vi.mock/vi.spyOn patterns, snapshot testing, in-source testing, workspace configuration, and browser mode. Use when: setting up tests, migrating from Jest, mocking modules, testing React components, or configuring monorepo workspaces. Keywords: vitest, test, unit test, vi.mock, vi.spyOn, snapshot, coverage, Jest migration.
Complete AI agent operating system setup with Kanban task management. Use when setting up multi-agent coordination, task tracking, or configuring an agent team. Includes theme selection (DBZ, One Piece, Marvel, etc.), workflow enforcement (all tasks through board), browser setup, GitHub integration, and memory enhancement (Supermemory, QMD).
Python skill router. Use when planning, implementing, or reviewing Python changes and you need to select focused skills for workflow, design, typing/contracts, reliability, testing, data/state, concurrency, integrations, runtime operations, or notebook async behavior.
Write tests using Vitest and React Testing Library. Use when creating unit tests, component tests, integration tests, or mocking dependencies. Activates for test file creation, mock patterns, coverage, and testing best practices.
Set up API integration with configuration and helper scripts
Terramate CLI, Cloud, and Catalyst best practices and usage guides. This skill should be used when working with Terramate stacks, orchestration, code generation, Cloud integration, or Catalyst components and bundles.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
Universal Strava API integration for fitness data management. Use when working with Strava activities, athlete profiles, segments, routes, clubs, or any fitness tracking data. Triggers on requests to get/create/update activities, analyze training stats, export routes, explore segments, or interact with Strava data programmatically.