analyzing-data
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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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/astronomer/agents --skill analyzing-data- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads analyzing-data automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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 |
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
- 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
FAQ
What does the analyzing-data skill do?
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").
How do I install analyzing-data?
Run npx skills add https://github.com/astronomer/agents --skill analyzing-data in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/analyzing-data/ (personal) or .claude/skills/analyzing-data/ (project).
Which AI agents can use analyzing-data?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is analyzing-data safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains analyzing-data?
It is published by astronomer in astronomer/agents, and it is an official skill from the maker of the technology it covers. It has 1,418 recorded installs.
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