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Pandas Data Analysis

Checked
pluginagentmarketplace·vlatest·data

Helps with data, analysis as part of data analysis and analytics workflows workflows.

Repository

Install on your platform

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

Installs under a different name. Its SKILL.md names it Pandas Data Analysis, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add pluginagentmarketplace/custom-plugin-python --skill "Pandas Data Analysis" -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

pluginagentmarketplace

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 pluginagentmarketplace/custom-plugin-python at 4415517654.

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

Commit
4415517654
Manifest
skills/pandas-data-analysis/SKILL.md
Scanned
4 files, 280 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 enables AI agents to perform advanced data manipulation and analysis using the Python pandas library. It allows for efficient handling of structured datasets, including reading various file formats, cleaning messy data, and executing complex statistical operations directly within an agent's workflow.

When to use it

  • Processing large CSV or Excel files containing sales records, customer logs, or survey results without writing manual scripts.
  • Cleaning inconsistent data by removing duplicates, handling missing values, and correcting data types automatically.
  • Generating summary statistics and aggregating data to identify trends or patterns in business metrics.
  • Preparing raw datasets for visualization or machine learning models by filtering and transforming columns.

Key capabilities

  • Load and export data from diverse sources like CSV, Excel, SQL databases, and JSON files.
  • Perform robust data cleaning tasks including dropping rows, imputing missing values, and renaming columns.
  • Execute groupby operations to aggregate data by specific categories or time periods.
  • Apply custom functions and vectorized operations for mathematical transformations across entire datasets.

Example prompts

  • "Load the attached sales.csv file, drop any rows with missing revenue figures, and calculate the total monthly sales per region."
  • "Read this Excel sheet containing customer feedback, filter out entries with negative sentiment scores, and count the occurrences of each keyword in the comments column."
  • "Import the provided JSON data, convert the 'date' column to datetime format, and generate a summary table showing the average price by product category."

Tips & gotchas

Ensure your AI agent has access to the necessary file paths or internet permissions to download datasets before attempting to load them. While pandas is powerful for tabular data, it may struggle with unstructured text analysis; consider combining this skill with NLP tools for complex linguistic tasks.