Database Claude Skills
1 639 database skills from 232 authors. They work on schemas, queries and moving data between them. Half of them fit into 2 210 tokens or less — that is what one costs your context window when the agent loads it. 286 ship runnable scripts rather than instructions alone. 7 of them cannot work without an MCP server, most often rube. We also found 387 copies of these same skills sitting in other people's repositories — counted once here, not 387 times.
1 639 unique 232 authors 5 updated this month 163 from vendors
Generates service-specific configuration files for GitLab stack projects in ./config directory, using .env as the primary configuration source. Creates nginx, PostgreSQL, Redis, and custom service configs with strict validation for secrets, paths, and Docker best practices. Use when setting up service configurations, creating config templates, or ensuring configs follow stack patterns.
Connect to and query WRDS (Wharton Research Data Services) from any research project. Use this skill whenever the user needs to download, query, or explore data from WRDS — including Compustat, CRSP, FactSet, I/B/E/S, or any other WRDS-hosted database. Also trigger when the user mentions WRDS tables, WRDS libraries, or wants to look up variable definitions or coverage in WRDS datasets. Do NOT trigger for general SQL or database questions unrelated to WRDS.
DuckDB SQL reference for MotherDuck. Use when you need exact DuckDB syntax or function behavior, friendly SQL features like QUALIFY, GROUP BY ALL, or list/struct types, MotherDuck-specific SQL such as shares, secrets, snapshots, or UNDROP, or to fix SQL errors and PostgreSQL-style SQL that fails on MotherDuck.
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.
Create, schedule, run, and debug MotherDuck Flights — Python jobs that run on MotherDuck compute. Use whenever someone wants to create a flight, schedule a Python script or recurring job on MotherDuck, set up scheduled ingestion from Postgres, dlt sources, S3, BigQuery, Snowflake, or APIs, refresh aggregates or transformations on a cron, or operate flights with get_flight_guide, create_flight, run_flight, flight logs, secrets, schedules, and versions.
Design and build database schemas and data models in MotherDuck. Produces a file-based SQL project scaffold with a model manifest. Use for any schema design or data modeling task — creating tables, choosing data types, star schemas, wide denormalized tables, raw/staging/analytics layers, dbt-style transformation projects, or restructuring data for analytics workloads.
Load and ingest data into MotherDuck from local files, object storage (S3, GCS, Azure, R2), HTTPS, dataframes, or external databases. Use for any import or bulk-load task — CSV, Parquet, JSON, Delta, Iceberg, local DuckDB database upload — and for choosing between CTAS, INSERT...SELECT, COPY, cloud-storage secrets, and Postgres-endpoint versus native DuckDB-client paths.
Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.
Plan a migration onto MotherDuck. Use when moving from Snowflake, BigQuery, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, source-vs-target validation, rollback, and native-versus-DuckLake posture.
Execute DuckDB SQL queries against MotherDuck databases. Use when running analytics, aggregations, transformations, or any SQL operation. Covers query best practices, CTEs, window functions, QUALIFY, and performance optimization.
Create and manage MotherDuck data shares for zero-copy, read-only data distribution. Use whenever someone wants to share a database with team members, another organization, or the public — covers CREATE SHARE, access/visibility/update modes, GRANT READ ON SHARE, attaching share URLs, UPDATE SHARE, and REFRESH DATABASE.
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.
Create, schedule, run, and debug MotherDuck Flights — Python jobs that run on MotherDuck compute. Use whenever someone wants to create a flight, schedule a Python script or recurring job on MotherDuck, set up scheduled ingestion from Postgres, dlt sources, S3, BigQuery, Snowflake, or APIs, refresh aggregates or transformations on a cron, or operate flights with get_flight_guide, create_flight, run_flight, flight logs, secrets, schedules, and versions.
DuckDB SQL reference for MotherDuck. Use when you need exact DuckDB syntax or function behavior, friendly SQL features like QUALIFY, GROUP BY ALL, or list/struct types, MotherDuck-specific SQL such as shares, secrets, snapshots, or UNDROP, or to fix SQL errors and PostgreSQL-style SQL that fails on MotherDuck.
Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.
Load and ingest data into MotherDuck from local files, object storage (S3, GCS, Azure, R2), HTTPS, dataframes, or external databases. Use for any import or bulk-load task — CSV, Parquet, JSON, Delta, Iceberg, local DuckDB database upload — and for choosing between CTAS, INSERT...SELECT, COPY, cloud-storage secrets, and Postgres-endpoint versus native DuckDB-client paths.
Plan a migration onto MotherDuck. Use when moving from Snowflake, BigQuery, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, source-vs-target validation, rollback, and native-versus-DuckLake posture.
Design and build database schemas and data models in MotherDuck. Produces a file-based SQL project scaffold with a model manifest. Use for any schema design or data modeling task — creating tables, choosing data types, star schemas, wide denormalized tables, raw/staging/analytics layers, dbt-style transformation projects, or restructuring data for analytics workloads.
Execute DuckDB SQL queries against MotherDuck databases. Use when running analytics, aggregations, transformations, or any SQL operation. Covers query best practices, CTEs, window functions, QUALIFY, and performance optimization.
Create and manage MotherDuck data shares for zero-copy, read-only data distribution. Use whenever someone wants to share a database with team members, another organization, or the public — covers CREATE SHARE, access/visibility/update modes, GRANT READ ON SHARE, attaching share URLs, UPDATE SHARE, and REFRESH DATABASE.
Convert AI conversation transcripts (Claude Code, Codex CLI, ChatGPT JSONL; Cursor IDE SQLite) to readable Markdown. Use when the user wants to: (1) convert .jsonl conversation transcripts to .md, (2) strip binary/base64 data from AI conversation logs, (3) make AI conversation history human-readable, (4) process session exports from ~/.claude/ or ~/.codex/, (5) batch-convert a folder of conversation JSONL files, or (6) list, search, or export Cursor IDE chat/composer conversations. Triggers on mentions of JSONL conversations, transcript conversion, conversation export, readable conversation logs, or Cursor chat history.
PostgreSQL 数据库管理
Redis 数据库管理
MongoDB 数据库管理
MySQL 数据库管理与运维
>- Validate database integrity, test migrations forward and backward, verify schema constraints, manage seed data, detect migration drift, and identify query performance issues. Covers PostgreSQL, MySQL, MongoDB with Prisma, TypeORM, Drizzle, and SQLAlchemy, plus Testcontainers test databases. "data integrity," "SQL test," "schema validation," "seed data," "query performance," "Testcontainers." Docker/IaC test-environment provisioning — use test-environments; SQL injection — use security-testing.
| Fills gaps in existing healthcare practitioner lists — adds missing phone numbers, credentials, specialties, contact info, education, reviews, and regulatory data. to these doctors", "complete this practitioner database", "enrich CRM export", "fill gaps in my provider data", "supplement this healthcare list". Accepts CSV, Google Sheet URL, or pasted data. Searches for each provider's practice website, extracts missing fields, and enriches with reviews, clinical trials, and accreditation via WSAs. Do NOT use for extracting providers from practice URLs — use healthcare-providers-extract instead. Do NOT use for validating credentials — use healthcare-providers-verify instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.
| Extracts structured practitioner data from healthcare practice websites. Returns names, credentials, specialties, contact info, and education for every provider on a practice's site. Use when user asks to extract, pull, or list doctors, providers, or staff from", "who are the providers at", "build a provider database", "list all doctors at", "scrape the team page", "get practitioner data from". Accepts practice URLs (pasted, CSV, Google Sheet) or discovers practices via Google Maps when given specialty + location. Single sites or 100+ URLs. Do NOT use for filling data gaps — use healthcare-providers-enrich instead. Do NOT use for credential validation — use healthcare-providers-verify instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.
Use this agent when reviewing PRs that touch database migrations, data backfills, or any code that transforms production data. This agent validates ID mappings against production reality, checks for swapped values, verifies rollback safety, and ensures data integrity during schema changes. Essential for any migration that involves ID mappings, column renames, or data transformations. <example>Context: The user has a PR with database migrations that involve ID mappings. user: \"Review this PR that migrates from action_id to action_module_name\" assistant: \"I'll use the data-migration-expert agent to validate the ID mappings and migration safety\" <commentary>Since the PR involves ID mappings and data migration, use the data-migration-expert to verify the mappings match production and check for swapped values.</commentary></example> <example>Context: The user has a migration that transforms enum values. user: \"This migration converts status integers to string enums\" assistant: \"Let me have the data-migration-ex...
Use this agent when you need to review database migrations, data models, or any code that manipulates persistent data. This includes checking migration safety, validating data constraints, ensuring transaction boundaries are correct, and verifying that referential integrity and privacy requirements are maintained. <example>Context: The user has just written a database migration that adds a new column and updates existing records. user: \"I've created a migration to add a status column to the orders table\" assistant: \"I'll use the data-integrity-guardian agent to review this migration for safety and data integrity concerns\" <commentary>Since the user has created a database migration, use the data-integrity-guardian agent to ensure the migration is safe, handles existing data properly, and maintains referential integrity.</commentary></example> <example>Context: The user has implemented a service that transfers data between models. user: \"Here's my new service that moves user data from the legacy_users table...
Use when designing databases for data-heavy applications, making schema decisions for performance, choosing between normalization and denormalization, selecting storage/indexing strategies, planning for scale, or evaluating OLTP vs OLAP trade-offs. Also use when encountering N+1 queries, ORM issues, or concurrency problems.
Use this agent when you need to analyze code for performance issues, optimize algorithms, identify bottlenecks, or ensure scalability. This includes reviewing database queries, memory usage, caching strategies, and overall system performance. The agent should be invoked after implementing features or when performance concerns arise.\\n\\n<example>\\nContext: The user has just implemented a new feature that processes user data.\\nuser: \"I've implemented the user analytics feature. Can you check if it will scale?\"\\nassistant: \"I'll use the performance-oracle agent to analyze the scalability and performance characteristics of your implementation.\"\\n<commentary>\\nSince the user is concerned about scalability, use the Task tool to launch the performance-oracle agent to analyze the code for performance issues.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: The user is experiencing slow API responses.\\nuser: \"The API endpoint for fetching reports is taking over 2 seconds to respond\"\\nassistant: \"Let me invoke the...
A comprehensive guide for interacting with PostgreSQL 16 databases. Use this skill for constructing standard and advanced SQL queries, optimizing performance, debugging errors, managing schema objects, and introspecting database structure.
Development patterns with PostgreSQL via FireDAC — connection, PL/pgSQL, sequences, JSONB, UPSERT, full-text search, migrations
Development patterns with Firebird database via FireDAC — connection, PSQL, generators, transactions, migrations
Development patterns with MySQL/MariaDB via FireDAC — connection, stored procedures, AUTO_INCREMENT, JSON, triggers, replication, migrations
Development patterns with Firebird database via FireDAC — connection, PSQL, generators, transactions, migrations
Development patterns with MySQL/MariaDB via FireDAC — connection, stored procedures, AUTO_INCREMENT, JSON, triggers, replication, migrations
Development patterns with PostgreSQL via FireDAC — connection, PL/pgSQL, sequences, JSONB, UPSERT, full-text search, migrations
| Azure Cosmos DB performance optimization and best practices guidelines for NoSQL, partitioning, queries, and SDK usage. Use when writing, reviewing, or refactoring code that interacts with Azure Cosmos DB, designing data models, optimizing queries, or implementing high-performance database operations. cross-partition queries, SDK singleton, CosmosClient, container modeling, change feed, bulk operations, vector search, full-text search, hierarchical partition keys, global distribution, autoscale throughput, indexing policy. Azure SQL, Cosmos DB for PostgreSQL (vCore), Cosmos DB for MongoDB vCore, Azure DocumentDB, general SQL databases, Redis, Elasticsearch.
Expert Celery distributed task queue engineer specializing in async task processing, workflow orchestration, broker configuration (Redis/RabbitMQ), Celery Beat scheduling, and production monitoring. Deep expertise in task patterns (chains, groups, chords), retries, rate limiting, Flower monitoring, and security best practices. Use when designing distributed task systems, implementing background job processing, building workflow orchestration, or optimizing task queue performance.
Expert in database schema design with focus on normalization, indexing strategies, FTS optimization, and performance-oriented architecture for desktop applications
Expert in graph database design and development with deep knowledge of graph modeling, traversals, query optimization, and relationship patterns. Specializes in SurrealDB but applies generic graph database concepts. Use when designing graph schemas, optimizing graph queries, implementing complex relationships, or building graph-based applications.
Expert in SQLite embedded database development for Tauri/desktop applications with focus on SQL injection prevention, migrations, FTS search, and secure data handling
Expert in SQLCipher encrypted database development with focus on encryption key management, key rotation, secure data handling, and cryptographic best practices
Expert SurrealDB developer specializing in multi-model database design, graph relations, document storage, SurrealQL queries, row-level security, and real-time subscriptions. Use when building SurrealDB applications, designing graph schemas, implementing secure data access patterns, or optimizing query performance.
Help with modern ABAP SQL features and AMDP (ABAP Managed Database Procedures) including inline declarations, window functions, GROUP BY, HAVING, PRIVILEGED ACCESS, string functions, aggregate expressions, common table expressions (CTE), AMDP classes, AMDP procedures, AMDP table functions, CDS table functions, and AMDP scalar functions. Use when users ask about ABAP SQL, modern SQL, SELECT, window functions, CTE, common table expression, AMDP, SQLScript, AMDP table function, CDS table function, aggregate, GROUP BY, HAVING, UNION, INTERSECT, EXCEPT, PRIVILEGED ACCESS, ABAP SQL expressions, built-in SQL functions, or database procedures. Triggers include "ABAP SQL query", "window function", "CTE", "AMDP", "table function", "GROUP BY", "aggregate", "PRIVILEGED ACCESS", "inline SELECT", or "SQLScript".
Access manufacturer pump curves and specifications from Grundfos, KSB, and other databases