analyzing-data

Name: analyzing-data
Author: astronomer

Query your data warehouse to answer business questions with cached patterns and concept mappings. Supports pattern lookup and caching for repeated question types, with outcome recording to improve future queries Includes concept-to-table mapping cache and table schema discovery via INFORMATION_SCHEMA or codebase grep Provides run_sql() and run_sql_pandas() kernel functions returning Polars or Pandas DataFrames for analysis CLI commands for managing concept, pattern, and table caches, plus warehouse selection and kernel lifecycle control

INSTALLATION

npx skills add https://github.com/astronomer/agents --skill analyzing-data

Run in your project or agent environment. Adjust flags if your CLI version differs.

SKILL.md

$2a

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 query INFORMATION_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