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AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment workflows.

This skill provides AWS cost optimization, monitoring, and operational best practices with integrated MCP servers for billing analysis, cost estimation, observability, and security assessment.

Provides comprehensive GitHub operations using gh CLI and GitHub API. Activates when working with pull requests, issues, repositories, workflows, or GitHub API operations including creating/viewing/merging PRs, managing issues, querying API endpoints, and handling GitHub workflows in enterprise or public GitHub environments.

Use this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.

Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.

Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.

Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.

Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.

PostgreSQL and TimescaleDB skill collection from Timescale's pg-aiguide. Covers table design, hypertables, pgvector semantic search, and hybrid text search. **Trigger when user asks to:** - Design PostgreSQL tables or schemas - Work with TimescaleDB hypertables - Implement semantic search with pgvector - Set up hybrid text search in PostgreSQL - Migrate tables to hypertables

Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

CRITICAL: Use for ALL Rust questions including errors, design, and coding. HIGHEST PRIORITY for: 比较, 对比, compare, vs, versus, 区别, difference, 最佳实践, best practice, tokio vs, async-std vs, 比较 tokio, 比较 async, Triggers on: Rust, cargo, rustc, crate, Cargo.toml, 意图分析, 问题分析, 语义分析, analyze intent, question analysis, compile error, borrow error, lifetime error, ownership error, type error, trait error, value moved, cannot borrow, does not live long enough, mismatched types, not satisfied, E0382, E0597, E0277, E0308, E0499, E0502, E0596, async, await, Send, Sync, tokio, concurrency, error handling, 编译错误, compile error, 所有权, ownership, 借用, borrow, 生命周期, lifetime, 类型错误, type error, 异步, async, 并发, concurrency, 错误处理, error handling, 问题, problem, question, 怎么用, how to use, 如何, how to, 为什么, why, 什么是, what is, 帮我写, help me write, 实现, implement, 解释, explain

Use when reviewing pull requests, conducting code quality audits, or identifying security vulnerabilities. Invoke for PR reviews, code quality checks, refactoring suggestions.

Complete E2E (end-to-end) and integration testing skill for TypeScript/NestJS projects using Jest, real infrastructure via Docker, and GWT pattern. ALWAYS use this skill when user needs to: **SETUP** - Initialize or configure E2E testing infrastructure: - Set up E2E testing for a new project - Configure docker-compose for testing (Kafka, PostgreSQL, MongoDB, Redis) - Create jest-e2e.config.ts or E2E Jest configuration - Set up test helpers for database, Kafka, or Redis - Configure .env.e2e environment variables - Create test/e2e directory structure **WRITE** - Create or add E2E/integration tests: - Write, create, add, or generate e2e tests or integration tests - Test API endpoints, workflows, or complete features end-to-end - Test with real databases, message brokers, or external services - Test Kafka consumers/producers, event-driven workflows - Working on any file ending in .e2e-spec.ts or in test/e2e/ directory - Use GWT (Given-When-Then) pattern for tests **REVIEW** - Audit or evaluate E2E tests: - Re

Complete unit testing skill for TypeScript/NestJS projects using Jest, @golevelup/ts-jest, and in-memory databases. ALWAYS use this skill when user needs to: **SETUP** - Initialize or configure unit testing: - Set up Jest for a new project - Configure test infrastructure (jest.config.ts) - Install testing dependencies (@nestjs/testing, @golevelup/ts-jest) - Create mock helpers or test utilities - Set up coverage configuration **WRITE** - Create or add unit tests: - Write, create, add, or generate unit tests - Test a service, usecase, controller, guard, interceptor, pipe, or filter - Add tests for new code or features - Improve test coverage or add missing tests - Mock dependencies or set up test fixtures - Working on any file ending in .spec.ts **REVIEW** - Audit or evaluate unit tests: - Review existing tests for quality - Check test coverage and gaps - Audit testing patterns and conventions - Evaluate assertion quality **RUN** - Execute or analyze test results: - Run unit tests - Analyze test results o