From cloud-sql-sqlserver
Explores Cloud SQL SQL Server schemas via table listings with columns and constraints, executes SQL queries on data, and monitors performance metrics using PromQL.
How this skill is triggered — by the user, by Claude, or both
Slash command
/cloud-sql-sqlserver:cloud-sql-sqlserver-dataThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
All scripts can be executed using Node.js. Replace `<param_name>` and `<param_value>` with actual values.
All scripts can be executed using Node.js. Replace <param_name> and <param_value> with actual values.
Bash:
node <skill_dir>/scripts/<script_name>.js '{"<param_name>": "<param_value>"}'
PowerShell:
node <skill_dir>/scripts/<script_name>.js '{\"<param_name>\": \"<param_value>\"}'
Note: The scripts automatically load the environment variables from various .env files. Do not ask the user to set vars unless skill executions fails due to env var absence.
Use this tool to execute SQL.
| Name | Type | Description | Required | Default |
|---|---|---|---|---|
| sql | string | The sql to execute. | Yes |
Lists detailed schema information (object type, columns, constraints, indexes, triggers, comment) as JSON for user-created tables (ordinary or partitioned). Filters by a comma-separated list of names. If names are omitted, lists all tables in user schemas.
| Name | Type | Description | Required | Default |
|---|---|---|---|---|
| table_names | string | Optional: A comma-separated list of table names. If empty, details for all tables will be listed. | No | `` |
| output_format | string | Optional: Use 'simple' for names only or 'detailed' for full info. | No | detailed |
npx claudepluginhub gemini-cli-extensions/cloud-sql-sqlserver --plugin cloud-sql-sqlserverSearches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.