Snowflake Automation
Automate Snowflake data warehouse operations -- list databases, schemas, and tables, execute SQL statements, and manage data workflows via the Composio MCP integration.
Best use case
Snowflake Automation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Automate Snowflake data warehouse operations -- list databases, schemas, and tables, execute SQL statements, and manage data workflows via the Composio MCP integration.
Teams using Snowflake Automation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/snowflake-automation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Snowflake Automation Compares
| Feature / Agent | Snowflake Automation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Automate Snowflake data warehouse operations -- list databases, schemas, and tables, execute SQL statements, and manage data workflows via the Composio MCP integration.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Snowflake Automation Automate your Snowflake data warehouse workflows -- discover databases, browse schemas and tables, execute arbitrary SQL (SELECT, DDL, DML), and integrate Snowflake data operations into cross-app pipelines. **Toolkit docs:** [composio.dev/toolkits/snowflake](https://composio.dev/toolkits/snowflake) --- ## Setup 1. Add the Composio MCP server to your client: `https://rube.app/mcp` 2. Connect your Snowflake account when prompted (account credentials or key-pair authentication) 3. Start using the workflows below --- ## Core Workflows ### 1. List Databases Use `SNOWFLAKE_SHOW_DATABASES` to discover available databases with optional filtering and Time Travel support. ``` Tool: SNOWFLAKE_SHOW_DATABASES Inputs: - like_pattern: string (SQL wildcard, e.g., "%test%") -- case-insensitive - starts_with: string (e.g., "PROD") -- case-sensitive - limit: integer (max 10000) - history: boolean (include dropped databases within Time Travel retention) - terse: boolean (return subset of columns: created_on, name, kind, database_name, schema_name) - role: string (role to use for execution) - warehouse: string (optional, not required for SHOW DATABASES) - timeout: integer (seconds) ``` ### 2. Browse Schemas Use `SNOWFLAKE_SHOW_SCHEMAS` to list schemas within a database or across the account. ``` Tool: SNOWFLAKE_SHOW_SCHEMAS Inputs: - database: string (database context) - in_scope: "ACCOUNT" | "DATABASE" | "<specific_database_name>" - like_pattern: string (SQL wildcard filter) - starts_with: string (case-sensitive prefix) - limit: integer (max 10000) - history: boolean (include dropped schemas) - terse: boolean (subset columns only) - role, warehouse, timeout: string/integer (optional) ``` ### 3. List Tables Use `SNOWFLAKE_SHOW_TABLES` to discover tables with metadata including row counts, sizes, and clustering keys. ``` Tool: SNOWFLAKE_SHOW_TABLES Inputs: - database: string (database context) - schema: string (schema context) - in_scope: "ACCOUNT" | "DATABASE" | "SCHEMA" | "<specific_name>" - like_pattern: string (e.g., "%customer%") - starts_with: string (e.g., "FACT", "DIM", "TEMP") - limit: integer (max 10000) - history: boolean (include dropped tables) - terse: boolean (subset columns only) - role, warehouse, timeout: string/integer (optional) ``` ### 4. Execute SQL Statements Use `SNOWFLAKE_EXECUTE_SQL` for SELECT queries, DDL (CREATE/ALTER/DROP), and DML (INSERT/UPDATE/DELETE) with parameterized bindings. ``` Tool: SNOWFLAKE_EXECUTE_SQL Inputs: - statement: string (required) -- SQL statement(s), semicolon-separated for multi-statement - database: string (case-sensitive, falls back to DEFAULT_NAMESPACE) - schema_name: string (case-sensitive) - warehouse: string (case-sensitive, required for compute-bound queries) - role: string (case-sensitive, falls back to DEFAULT_ROLE) - bindings: object (parameterized query values to prevent SQL injection) - parameters: object (Snowflake session-level parameters) - timeout: integer (seconds; 0 = max 604800s) ``` **Examples:** - `"SELECT * FROM my_table LIMIT 100;"` - `"CREATE TABLE test (id INT, name STRING);"` - `"ALTER SESSION SET QUERY_TAG='mytag'; SELECT COUNT(*) FROM my_table;"` --- ## Known Pitfalls | Pitfall | Detail | |---------|--------| | Case sensitivity | Database, schema, warehouse, and role names are case-sensitive in `SNOWFLAKE_EXECUTE_SQL`. | | Warehouse required for compute | SELECT and DML queries require a running warehouse. SHOW commands do not. | | Multi-statement execution | Multiple statements separated by semicolons execute in sequence automatically. | | SQL injection prevention | Always use the `bindings` parameter for user-supplied values to prevent injection attacks. | | Pagination with LIMIT | `SHOW` commands support `limit` (max 10000) and `from_name` for cursor-based pagination. | | Time Travel | Set `history: true` to include dropped objects still within the retention period. | --- ## Quick Reference | Tool Slug | Description | |-----------|-------------| | `SNOWFLAKE_SHOW_DATABASES` | List databases with filtering and Time Travel support | | `SNOWFLAKE_SHOW_SCHEMAS` | List schemas within a database or account-wide | | `SNOWFLAKE_SHOW_TABLES` | List tables with metadata (row count, size, clustering) | | `SNOWFLAKE_EXECUTE_SQL` | Execute SQL: SELECT, DDL, DML with parameterized bindings | --- *Powered by [Composio](https://composio.dev)*
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