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Found 739 Skills
Create, edit, manage, share, or embed MotherDuck Dives. Use when the work involves Dive authoring, live React + SQL components, MCP get_dive_guide, useSQLQuery, local preview, version history, Dives-as-code, required resources, team sharing, or embedded Dive sessions.
Reviews PostgreSQL code for indexing strategies, JSONB operations, connection pooling, and transaction safety. Use when reviewing SQL queries, database schemas, JSONB usage, or connection management.
Guidelines for developing with Sequelize, a promise-based Node.js ORM supporting PostgreSQL, MySQL, MariaDB, SQLite, and SQL Server
Assists with SAP HANA Developer CLI (hana-cli) for database development and administration. Use when: installing hana-cli, connecting to SAP HANA databases, inspecting database objects (tables, views, procedures, functions), managing HDI containers, executing SQL queries, converting metadata to CDS/EDMX/OpenAPI formats, managing SAP HANA Cloud instances, working with BTP CLI integration, or troubleshooting hana-cli commands. Covers: 91 commands, 17+ output formats, HDI container management, cloud operations.
Security engineering that protects applications, data, and users from real-world threatsUse when "security, authentication, authorization, encryption, OWASP, vulnerability, XSS, SQL injection, CSRF, secrets, password, JWT, OAuth, permissions, audit, compliance, security, authentication, authorization, encryption, vulnerabilities, OWASP, compliance, audit" mentioned.
Security audit worker (L3). Scans codebase for hardcoded secrets, SQL injection, XSS, insecure dependencies, missing input validation. Returns findings with severity (Critical/High/Medium/Low), location, effort, and recommendations.
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.
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.
Learn how to build a simple WebRTC video call application using PocketBase as a signaling server, enabling peer-to-peer communication with SQLite on the server and realtime updates via Server Sent Events.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.