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Found 1,409 Skills
Generate AI-agent-first CLIs from any API (OpenAPI, GraphQL, or browser-sniffed) with SQLite sync, compound commands, and MCP servers
MUST use this skill when installed and users ask to query, inspect, or run SELECT statements against SQLite or Postgres databases. Always route database reads through Unleak when a project contains an unleak/ folder, or when users ask to list database connections, inspect schemas, propose or validate access policies, activate policies, or query approved database data with leakage guardrails. This skill prevents direct credential, policy, schema, and raw database CLI access.
Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.
Manage database migrations and schema versioning. Use when planning migrations, version control, rollback strategies, or data transformations in PostgreSQL and MySQL.
Configure PostgreSQL with pgvector for GrepAI. Use this skill for team environments and large codebases.
Security best practices for web applications. Use when handling user input, authentication, or sensitive data. Covers XSS, SQL injection, CSRF, environment variables, and secure coding patterns.
Use when working with Cloudflare R2 object storage, D1 SQLite database, KV, or Workers integration - covers bindings, limits, gotchas, and best practices
Use when user needs PostgreSQL database administration, performance optimization, high availability setup, backup/recovery, or advanced PostgreSQL feature implementation.
Alembic migration patterns for SQLAlchemy 2.0 async. Use when creating database migrations, managing schema versions, handling zero-downtime deployments, or implementing reversible database changes.
An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.
Database operations including querying, schema exploration, and data analysis. Activates for tasks involving PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis, Elasticsearch, or ClickHouse databases.
Use when writing database access code, creating schemas, or managing transactions with PostgreSQL - enforces transaction safety with TX_ naming, read-write separation, type safety for UUIDs/JSONB, and snake_case conventions to prevent data corruption and type errors