rory-data/analyzing-data
skills/astronomer/analyzing-data/SKILL.md
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.
data-ai
Updated Apr 26, 2026
$ install --global
skillsauthnpx skillsauth add rory-data/copilot analyzing-dataInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 24, 2026, 9:13 AM52.9s25 files scanned
SKILL.md
- name:
- analyzing-data
- description:
- 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.
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:
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
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