Analyzing Data

astronomer/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").

45k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

416

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

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

What comes with it

173 095 bytes besides the instruction

reference/common-patterns.md

reference/discovery-warehouse.md

scripts/.gitignore

scripts/cache.py

scripts/cli.py

scripts/config.py

scripts/connectors.py

scripts/kernel.py

scripts/pyproject.toml

scripts/templates.py

scripts/tests/__init__.py

scripts/tests/conftest.py

scripts/tests/integration/__init__.py

scripts/tests/integration/conftest.py

scripts/tests/integration/test_duckdb_e2e.py

scripts/tests/integration/test_kernel_interrupt.py

scripts/tests/integration/test_postgres_e2e.py

scripts/tests/integration/test_sqlite_e2e.py

scripts/tests/test_cache.py

scripts/tests/test_config.py

scripts/tests/test_connectors.py

scripts/tests/test_kernel.py

scripts/tests/test_utils.py

scripts/tests/test_warehouse.py

scripts/ty.toml

scripts/warehouse.py

The instruction itself

10 sections, as written by the author

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 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 |

| 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

How to use it

Copy the folder

Take astronomer/analyzing-data from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.