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Found 148 Skills
Use when "evaluating technology", "choosing frameworks", "stack comparison", "technology decisions", or asking about "React vs Vue", "PostgreSQL vs MySQL", "AWS vs GCP", "build vs buy"
Diagnose and manage Alibaba Cloud databases through natural language. Use when users need to troubleshoot database performance issues (high CPU, slow queries, abnormal connections, lock waits), check instance status, analyze disk space, optimize SQL, run health inspections, or detect security baseline violations. Supports RDS (MySQL/PostgreSQL/SQL Server), PolarDB, MongoDB, Redis (Tair), and Lindorm. Trigger this skill even for casual descriptions like "my database is slow", "can't connect to the database", "help me check this SQL", or "database disk is almost full". Also suitable for consulting Alibaba Cloud-specific database features (e.g., PolarDB Serverless, DAS autonomy capabilities) and comparing product differences (RDS vs PolarDB). Do NOT use this skill for general SQL tutorials, non-Alibaba Cloud databases, or local database administration.
Alibaba Cloud PolarDB Database AI Assistant. For PolarDB MySQL/PostgreSQL cluster management, performance diagnostics, parameter tuning, slow SQL analysis, backup recovery, connection session analysis, primary-standby switchover diagnostics, security configuration audit, and other O&M operations. Use when user questions involve PolarDB, cluster IDs starting with pc-, kernel parameters, primary-standby switchover, IMCI columnar storage, etc.
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
Expert knowledge for Azure Backup development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when backing up Azure VMs, AKS, SQL/PostgreSQL/MySQL, SAP HANA, files/disks/blobs, or automating via CLI/PowerShell/REST, and other Azure Backup related development tasks. Not for Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), Azure Blob Storage (use azure-blob-storage), Azure Files (use azure-files).
Database performance optimization, schema design, query analysis, and connection management across PostgreSQL, MySQL, MongoDB, and SQLite with ORM integration. Use this skill for queries, indexes, connection pooling, transactions, and database architecture decisions.
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 running commands inside a Zeabur service container. Use for one-off database operations like queries, data cleanup, or migrations (e.g. mongosh, psql, mysql, redis-cli). Use when user says "exec into container", "run command in service", "query database", "delete from database", "run mongo command", "run SQL", "check files in container", "debug inside service", or "shell into service". Use for container-level debugging like checking env vars, files, processes, or connectivity. NOT for deploying databases (use zeabur-template-deploy instead).
World-class database schema design - data modeling, migrations, relationships, and the battle scars from scaling databases that store billions of rowsUse when "database schema, data model, migration, prisma schema, drizzle schema, create table, add column, foreign key, primary key, uuid, auto increment, soft delete, normalization, denormalization, one to many, many to many, junction table, polymorphic, enum type, index strategy, database, schema, migration, data-model, prisma, drizzle, typeorm, postgresql, mysql, sqlite" mentioned.
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).
Connect Spice to data sources and query across them with federated SQL. Use when connecting to databases (Postgres, MySQL, DynamoDB), data lakes (S3, Delta Lake, Iceberg), warehouses (Snowflake, Databricks), files, APIs, or catalogs; configuring datasets; creating views; writing data; or setting up cross-source queries.
Implementing Entity Framework Core repositories and migrations for PostgreSQL, MySQL, and SQLite at Bitwarden. Use when creating or modifying EF repositories, generating EF migrations, or working with non-MSSQL data access in the server repo.