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Python Panel Data

Checked
meleantonio·vlatest·backend

Helps with Python, data as part of developing backend services and APIs workflows.

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 meleantonio/awesome-econ-ai-stuff --skill python-panel-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

meleantonio

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

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

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

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

  • Pinned to a commit: passed

    Scanned meleantonio/awesome-econ-ai-stuff at dd890d51c7.

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

Commit
dd890d51c7
Manifest
_skills/analysis/python-panel-data/SKILL.md
Scanned
2 files, 264 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

This skill allows AI agents to perform panel data analysis using Python. It leverages libraries like pandas and potentially others for statistical modeling and econometrics on datasets where observations are tracked over time for multiple entities. The agent can handle tasks such as generating descriptive statistics, running regressions with fixed effects, and creating visualizations of trends in the data.

When to use it

  • Analyzing economic policy impacts by examining outcomes across different regions or firms over several years.
  • Modeling consumer behavior by tracking individual purchasing patterns over time.
  • Evaluating the effectiveness of a new training program by comparing performance metrics for employees before and after participation.
  • Predicting future sales based on historical data, incorporating seasonal trends and other factors.

Key capabilities

  • Panel Data Analysis
  • Statistical Modeling (likely including regressions)
  • Data Visualization
  • Python Programming
  • pandas library integration

Example prompts

  • "Analyze this panel dataset of regional GDP growth rates from 2010 to 2023 and identify any significant trends."
  • "Run a fixed effects regression on this data, with income as the dependent variable and education level and experience as independent variables."
  • “Create a visualization showing the average sales per month for each store in this panel dataset.”

Tips & gotchas

  • Requires familiarity with Python and basic statistical concepts.
  • The agent's ability to handle complex econometric models may be limited by its underlying programming capabilities.