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Found 355 Skills
Direct Supabase PostgreSQL database connection and SQL execution. Use when creating tables, running SQL queries, managing schemas, or setting up database migrations. Automatically reads connection info from project .env files.
Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.
Database performance optimization for MongoDB, PostgreSQL, MySQL, Redis, and ORMs like Prisma and Mongoose. Covers indexes, query patterns, pagination, caching, connection pooling, migration safety, and scaling. Use when optimizing slow queries, designing indexes, reviewing database performance, or improving database scalability.
Upgrades Helm chart dependencies (PostgreSQL, Vault) in the Chainloop project, including vendorized charts, container images, and CI/CD workflows. Use when the user mentions upgrading Helm charts, Bitnami dependencies, PostgreSQL chart, or Vault chart. CRITICAL - Major version upgrades are FORBIDDEN and must be escalated.
Query PostgreSQL with Prisma 7 efficiently, using Prisma Client for mutations and selectively using raw SQL for complex/performant reads (SELECT/COUNT) when appropriate.
Advanced database design and administration for PostgreSQL, MongoDB, and Redis. Use when designing schemas, optimizing queries, managing database performance, or implementing data patterns.
Connect to MotherDuck from any application. Use when setting up database connectivity via the Postgres endpoint (recommended), pg_duckdb, native DuckDB API, or JDBC. Covers connection strings, authentication, SSL, and environment variable configuration.
Scaffold and fully configure a new Agentic Coding Starter Kit project — a Next.js 16 + TypeScript + Better Auth + Drizzle + PostgreSQL + AI SDK boilerplate. Use this skill whenever the user asks to set up, scaffold, create, initialize, or bootstrap an "agentic coding starter kit", "agentic app", "agentic boilerplate", a "Next.js app with auth and db", or mentions `create-agentic-app` / `npx create-agentic-app`. Walks the user through folder strategy, package-manager choice, Postgres setup (Docker / Neon / Vercel / BYO), OpenRouter AI configuration, migrations, a build check, and dev-server verification — ending with a working http://localhost:3000.
Comprehensive guide for Go database access. Covers parameterized queries, struct scanning, NULLable column handling, error patterns, transactions, isolation levels, SELECT FOR UPDATE, connection pool, batch processing, context propagation, and migration tooling. Use this skill whenever writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite. Also triggers for database testing or any question about database/sql, sqlx, pgx, or SQL queries in Golang. This skill explicitly does NOT generate database schemas or migration SQL.
Design and optimize database schemas for SQL and NoSQL databases. Use when creating new databases, designing tables, defining relationships, indexing strategies, or database migrations. Handles PostgreSQL, MySQL, MongoDB, normalization, and performance optimization.
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.