analyzing-data
Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.
From data-engineeringnpx claudepluginhub astronomer/agents --plugin dataThis skill uses the workspace's default tool permissions.
reference/common-patterns.mdreference/discovery-warehouse.mdscripts/cache.pyscripts/cli.pyscripts/config.pyscripts/connectors.pyscripts/kernel.pyscripts/pyproject.tomlscripts/templates.pyscripts/tests/__init__.pyscripts/tests/conftest.pyscripts/tests/integration/__init__.pyscripts/tests/integration/conftest.pyscripts/tests/integration/test_duckdb_e2e.pyscripts/tests/integration/test_postgres_e2e.pyscripts/tests/integration/test_sqlite_e2e.pyscripts/tests/test_cache.pyscripts/tests/test_config.pyscripts/tests/test_connectors.pyscripts/tests/test_utils.pyData 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:
uv run scripts/cli.py pattern lookup "<user's question>"If a pattern exists, follow its strategy. Record the outcome after executing:
uv run scripts/cli.py pattern record <name> --success # or --failure -
Concept lookup — Find known table mappings:
uv run scripts/cli.py concept lookup <concept> -
Table discovery — If cache misses, search the codebase (
Grep pattern="<concept>" glob="**/*.sql") or queryINFORMATION_SCHEMA. See reference/discovery-warehouse.md. -
Execute query:
uv run scripts/cli.py exec "df = run_sql('SELECT ...')" uv run scripts/cli.py exec "print(df)" -
Cache learnings — Always cache before presenting results:
# Cache concept → table mapping uv run scripts/cli.py concept learn <concept> <TABLE> -k <KEY_COL> # Cache query strategy (if discovery was needed) uv run scripts/cli.py pattern learn <name> -q "question" -s "step" -t "TABLE" -g "gotcha" -
Present findings to user.
Kernel Functions
| Function | Returns |
|---|---|
run_sql(query, limit=100) | Polars DataFrame |
run_sql_pandas(query, limit=100) | Pandas DataFrame |
pl (Polars) and pd (Pandas) are pre-imported.
CLI Reference
Kernel
uv run scripts/cli.py warehouse list # List warehouses
uv run scripts/cli.py start [-w name] # Start kernel (with optional warehouse)
uv run scripts/cli.py exec "..." # Execute Python code
uv run scripts/cli.py status # Kernel status
uv run scripts/cli.py restart # Restart kernel
uv run scripts/cli.py stop # Stop kernel
uv run scripts/cli.py install <pkg> # Install package
Concept Cache
uv run scripts/cli.py concept lookup <name> # Look up
uv run scripts/cli.py concept learn <name> <TABLE> -k <KEY_COL> # Learn
uv run scripts/cli.py concept list # List all
uv run scripts/cli.py concept import -p /path/to/warehouse.md # Bulk import
Pattern Cache
uv run scripts/cli.py pattern lookup "question" # Look up
uv run scripts/cli.py pattern learn <name> -q "..." -s "..." -t "TABLE" -g "gotcha" # Learn
uv run scripts/cli.py pattern record <name> --success # Record outcome
uv run scripts/cli.py pattern list # List all
uv run scripts/cli.py pattern delete <name> # Delete
Table Schema Cache
uv run scripts/cli.py table lookup <TABLE> # Look up schema
uv run scripts/cli.py table cache <TABLE> -c '[...]' # Cache schema
uv run scripts/cli.py table list # List cached
uv run scripts/cli.py table delete <TABLE> # Delete
Cache Management
uv run scripts/cli.py cache status # Stats
uv run scripts/cli.py cache clear [--stale-only] # Clear
References
- reference/discovery-warehouse.md — Large table handling, warehouse exploration, INFORMATION_SCHEMA queries
- reference/common-patterns.md — SQL templates for trends, comparisons, top-N, distributions, cohorts