analyzing-data
Queries the data warehouse with SQL and answers business questions about data. Use when answering anything that needs warehouse data - counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis (for example "who uses X", "how many Y", "show me Z", "find customers", "what is the count").
Install
With your agent
Install OMGSkills skill "astronomer/agents:skills/analyzing-data" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/astronomer/agents /tmp/agents && ln -s /tmp/agents/skills/analyzing-data ~/.claude/skills/analyzing-dataFrom README
Data Analysis Answer business questions by querying the data warehouse. The kernel auto-starts on first exec call. All CLI commands below are relative to this skill's directory. Before running any scripts/cli.py command, cd to the directory containing this file. Workflow Pattern lookup — Check for a cached query strategy: If a pattern exists, follow its strategy. Record the outcome after executing: Concept lookup — Find known table mappings: Table discovery — If cache misses, search the codebase (Grep pattern=" " glob="/.sql") or query INFORMATIONSCHEMA. See reference/discovery-warehouse.md. Execute query: Cache learnings — Always cache before presenting results: Present findings to user. Kernel Functions | Function | Returns | |----------|---------| | runsql(query, limit=100) | Polars DataFrame | | runsqlpandas(query, limit=100) | Pandas DataFrame | | runsqlmany(queries, limit=100) | List of Polars DataFrames (one per query) | pl (Polars) and pd (Pandas) are pre-imported.
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