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Analyzing Data

Listed
astronomer·vlatest·data·Apache-2.0

Identifies patterns, anomalies, and trends within astronomical datasets to support research and discovery.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add astronomer/agents --skill analyzing-data -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

astronomer

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 3 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: astral.sh.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    astral.shundeclared

    What it found

    • skills/analyzing-data/scripts/cli.py:50contacts astral.sh
      "Install with: curl -LsSf https://astral.sh/uv/install.sh | sh", err=True
    • skills/analyzing-data/scripts/kernel.py:112contacts astral.sh
      "Install with: curl -LsSf https://astral.sh/uv/install.sh | sh"

  • No credential access: failed

    Touches credential material in 1 place.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

    What it found

    • skills/analyzing-data/scripts/tests/test_connectors.py:739private key material
      private_key="-----BEGIN PRIVATE KEY-----\ntest\n-----END PRIVATE KEY-----",

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 2 places.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • skills/analyzing-data/scripts/cli.py:50remote script piped into a shell
      "Install with: curl -LsSf https://astral.sh/uv/install.sh | sh", err=True
    • skills/analyzing-data/scripts/kernel.py:112remote script piped into a shell
      "Install with: curl -LsSf https://astral.sh/uv/install.sh | sh"

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • Pinned to a commit: passed

    Scanned astronomer/agents at cbe1141f54.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
cbe1141f54
Manifest
skills/analyzing-data/SKILL.md
Scanned
26 files, 5,190 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The analyzing-data skill enables AI agents to process, interpret, and derive insights from structured or unstructured data sets. It supports statistical analysis, pattern recognition, and visualization of results, making it useful for evaluating trends, anomalies, and correlations within the data.

When to use it

  • To identify patterns in sales reports or customer behavior logs.
  • For generating summaries and visualizations from large datasets.
  • When preparing data insights for business decisions or academic research.
  • To detect outliers or errors in data entries during quality checks.

Key capabilities

  • Statistical analysis of numerical data
  • Pattern recognition in structured/unstructured formats
  • Data visualization (e.g., charts, graphs)
  • Insight generation and summarization

Example prompts

  • "Analyze this sales dataset and highlight the top-performing products."
  • "Identify any anomalies in the customer feedback logs provided."
  • "Generate a summary report with visualizations from the latest user activity data."

Tips & gotchas

Ensure your data is clean and well-formatted for accurate results. While it can handle large datasets, performance may vary depending on system resources and data complexity.