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analyzing-data▌

astronomer/agents · updated Apr 8, 2026

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

Query your data warehouse to answer business questions with cached patterns and concept mappings.

  • ›Supports pattern lookup and caching for repeated question types, with outcome recording to improve future queries
  • ›Includes concept-to-table mapping cache and table schema discovery via INFORMATION_SCHEMA or codebase grep
  • ›Provides run_sql() and run_sql_pandas() kernel functions returning Polars or Pandas DataFrames for analysis
  • ›CLI commands for managing concept, pattern, and table cach
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

  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

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

Discussion

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general reviews

Ratings

4.5★★★★★72 reviews
  • ★★★★★Dhruvi Jain· Dec 24, 2024

    Solid pick for teams standardizing on skills: analyzing-data is focused, and the summary matches what you get after install.

  • ★★★★★Harper White· Dec 24, 2024

    Solid pick for teams standardizing on skills: analyzing-data is focused, and the summary matches what you get after install.

  • ★★★★★Luis Malhotra· Dec 16, 2024

    I recommend analyzing-data for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Chen Anderson· Dec 12, 2024

    Registry listing for analyzing-data matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Carlos Yang· Dec 8, 2024

    Solid pick for teams standardizing on skills: analyzing-data is focused, and the summary matches what you get after install.

  • ★★★★★Luis Abbas· Dec 4, 2024

    analyzing-data has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Luis Khanna· Nov 27, 2024

    We added analyzing-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Li Perez· Nov 23, 2024

    analyzing-data fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Oshnikdeep· Nov 15, 2024

    We added analyzing-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Noah Rao· Nov 15, 2024

    We added analyzing-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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