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Skills/astronomer/agents/analyzing-data
analyzing-data logo

analyzing-data

astronomer/agents
1K installs389 stars
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Installation

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

Summary

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.

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
  1. Concept lookup — Find known table mappings:
   uv run scripts/cli.py concept lookup <concept>
  1. Table discovery — If cache misses, search the codebase (Grep pattern="<concept>" glob="*/.sql") or query INFORMATION_SCHEMA. See reference/discovery-warehouse.md.
  1. Execute query:
   uv run scripts/cli.py exec "df = run_sql('SELECT ...')"
   uv run scripts/cli.py exec "print(df)"
  1. 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"
  1. Present findings to user.

Kernel Functions

FunctionReturns
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

Score

0–100
63/ 100

Grade

C

Popularity15/30

1,050 installs — growing adoption.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Analyzing Data skill score badge previewScore badge

Markdown

[![Analyzing Data skill](https://www.claudemarket.ai/skills/astronomer/agents/analyzing-data/badges/score.svg)](https://www.claudemarket.ai/skills/astronomer/agents/analyzing-data)

HTML

<a href="https://www.claudemarket.ai/skills/astronomer/agents/analyzing-data"><img src="https://www.claudemarket.ai/skills/astronomer/agents/analyzing-data/badges/score.svg" alt="Analyzing Data skill"/></a>

Analyzing Data FAQ

How do I install the Analyzing Data skill?

Run “npx skills add https://github.com/astronomer/agents --skill analyzing-data” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Analyzing Data skill do?

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. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Analyzing Data skill free?

Yes. Analyzing Data is a free, open-source skill published from astronomer/agents. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Analyzing Data work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Analyzing Data works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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