Agents analyzing-data

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.

install

source · Clone the upstream repo

git clone https://github.com/astronomer/agents

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/astronomer/agents "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/analyzing-data" ~/.claude/skills/astronomer-agents-analyzing-data && rm -rf "$T"

manifest: skills/analyzing-data/SKILL.md

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

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