tm-generate-mongo
根据 MongoDB 集合信息生成 TM(表模型)文件。当用户需要为 MongoDB 集合创建数据模型、生成 mongo 类型的 .tm 文件时使用。
根据 MongoDB 集合信息生成 TM(表模型)文件。当用户需要为 MongoDB 集合创建数据模型、生成 mongo 类型的 .tm 文件时使用。
Redis patterns, caching strategies, and Laravel Horizon / Python task queues.
Use when implementing redis functionality with production-grade patterns and safeguards.
Redis data structures, caching patterns, pub/sub, Lua scripting, and cluster configuration. Trigger for Redis usage patterns, caching strategies, or Redis-specific optimization.
Patrones de FirestoreRepository: paths, subcolecciones, métodos CRUD, sanitización de timestamps. Trigger: Cuando se crea un repositorio, se trabaja con paths de Firestore, o se necesitan métodos custom.
Index a folder's contents into the context system for fast retrieval and discovery. Activate when: "index this folder", "catalog these files", folder contents indexed, build file index, scan folder Do NOT activate for: saving decisions to context (/context-save), recalling context (/context-recall), plugin setup (/setup)
Create a database cluster on KubeBlocks using the generic Cluster CR template. Supports all addons (MySQL, PostgreSQL, Redis, MongoDB, Kafka, Elasticsearch, Milvus, Qdrant, etc.) with various topologies. Use when the user wants to deploy, create, provision, or launch a new database cluster — especially for engines without a dedicated addon-* skill. For MySQL, PostgreSQL, Redis, MongoDB, or Kafka, prefer the engine-specific addon skill (kubeblocks-addon-mysql, kubeblocks-addon-postgresql, kubeblocks-addon-redis, kubeblocks-addon-mongodb, kubeblocks-addon-kafka) which provides topology guidance and tuned defaults. NOT for managing existing clusters (see Day-2 operation skills).
Deploy and manage Elasticsearch clusters on KubeBlocks for full-text search, log analytics, and observability. Use when the user mentions Elasticsearch, ELK stack, search engine, log analytics, Kibana, full-text search, or explicitly wants to create an Elasticsearch cluster. Provides single-node (dev/test) and multi-node cluster creation with connection methods. No backup/restore support in KubeBlocks currently. For generic cluster creation across all engines, see kubeblocks-create-cluster. For Day-2 operations (scaling, volume expansion, etc.), use the corresponding operation skill.
Schema Management is the discipline of defining, versioning, and evolving the structure of data. In a distributed system, a change in one service's schema can have a cascading "breaking" effect on doz
Comprehensive guide for vector search implementation covering HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), Flat Index implementations, distance metrics (cosine, Euclidean, dot
Redis performance optimization and best practices. Use this skill when working with Redis data structures, Redis Query Engine (RQE), vector search with RedisVL, semantic caching with LangCache, or optimizing Redis performance.
How the @repo/redis package works in this repo. ioredis wrapper with caching patterns. Use when: adding caching to controllers, building cache keys, invalidating cache, or debugging Redis issues.
Complete guide for Testcontainers NoSQL integration testing. Use when containerized integration testing for MongoDB or Redis. Covers MongoDB document operations, Redis five data structures, Collection Fixture pattern. Includes BSON serialization, index performance testing, data isolation strategy, and container lifecycle management. Keywords: testcontainers mongodb, testcontainers redis, mongodb integration test, redis integration test, nosql testing, MongoDbContainer, RedisContainer, IMongoDatabase, IConnectionMultiplexer, BSON serialization, BsonDocument, document model testing, cache testing, Collection Fixture
SQL for data analysis with exploratory analysis, advanced aggregations, statistical functions, outlier detection, and business insights. 50+ real-world analytics queries.
Transform and export data using DuckDB SQL. Read CSV/Parquet/JSON/Excel/databases, apply SQL transformations (joins, aggregations, PIVOT/UNPIVOT, sampling), and optionally write results to files. Use when the user wants to: (1) Clean, filter, or transform data, (2) Join multiple data sources, (3) Convert between formats (CSV→Parquet, etc.), (4) Create partitioned datasets, (5) Sample large datasets, (6) Export query results. Prefer this over in-context reasoning for datasets with thousands of rows or complex transformations.
Run ad-hoc MongoDB queries in natural language. Translates natural language queries into MongoDB queries and executes them. Use for quick data exploration, answering "how many" questions, finding specific records, or aggregating statistics.
Master SQL for data analysis with complex queries, joins, aggregations, window functions, and query optimization.