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analyzing-data

Business
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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").

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How to use this skill

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Source SKILL.md: https://github.com/astronomer/agents/blob/HEAD/skills/analyzing-data/SKILL.md

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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

  1. 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

  2. Concept lookup — Find known table mappings:

    uv run scripts/cli.py concept lookup <concept>

  3. Table discovery — If cache misses, search the codebase (Grep pattern="<concept>" glob="**/*.sql") or query INFORMATION_SCHEMA. See reference/discovery-warehouse.md.

  4. Execute query:

    uv run scripts/cli.py exec "df = run_sql('SELECT ...')"
uv run scripts/cli.py exec "print(df)"

  5. 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"

  6. Present findings to user.

Kernel Functions

FunctionReturns
run_sql(query, limit=100)Polars DataFrame
run_sql_pandas(query, limit=100)Pandas DataFrame
run_sql_many(queries, limit=100)List of Polars DataFrames (one per query)

pl (Polars) and pd (Pandas) are pre-imported.

Run independent queries together with run_sql_many — they execute concurrently (Snowflake async / connection-pool fan-out) instead of one at a time:

uv run scripts/cli.py exec "dfs = run_sql_many(['SELECT ...', 'SELECT ...']); print(dfs[0])"

run_sql_many is fail-fast: if any query errors, the call raises and the results of the queries that succeeded are discarded. Use separate run_sql calls if you need partial results.

Timeouts: exec waits up to 120s by default, then interrupts the query and returns a "client stopped waiting" message (the query may still finish server-side). Raise it for known long-running queries: uv run scripts/cli.py exec "..." -t 600.

Idle kernel: the kernel self-terminates after 2h idle (preserving state until then). Override with ASTRO_KERNEL_IDLE_TIMEOUT (seconds; 0 disables).

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