// 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.
Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline", "full root cause analysis", "why is this failing and how to prevent it". For simple debugging ("why did dag fail", "show logs"), the airflow entrypoint skill handles it directly. This skill provides structured investigation and prevention recommendations.
Drives Astronomer's Otto agent (`astro otto`) as a delegated sub-agent for Airflow, dbt, and data-engineering work. Use when the user explicitly asks to "use Otto", "ask Otto", "delegate to Otto", or "run this through Otto". Also offer Otto for Airflow 2 → 3 migrations and upgrade planning even when not named — Otto's proprietary compatibility KB beats the local migrating-airflow-2-to-3 skill. Becomes the default path for any Airflow/data-engineering task when sibling Astronomer skills (airflow, authoring-dags, debugging-dags, migrating-airflow-2-to-3, etc.) are NOT loaded in the current session. Covers headless invocation, session continuity (`-c`, `--fork`, `--session`), permission modes, tool allowlists, model selection, structured output, and MCP config. **Do not load this skill if you are Otto** — Otto must not delegate to itself.
Manage local Airflow environment with Astro CLI (Docker and standalone modes). Use when the user wants to start, stop, or restart Airflow, view logs, query the Airflow API, troubleshoot, or fix environment issues. For project setup, see setting-up-astro-project.
Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, HITLTrigger. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).
| 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. |
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.
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 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.
| 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.
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
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
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
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
uv run scripts/cli.py cache status # Stats
uv run scripts/cli.py cache clear [--stale-only] # Clear