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Data Cleaning Pipeline

Checked
aj-geddes·vlatest·data·MIT

Helps with data, pipeline automation as part of automating DevOps pipelines and CI/CD 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 Data Cleaning Pipeline, 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 aj-geddes/useful-ai-prompts --skill "Data Cleaning Pipeline" -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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/data-cleaning-pipeline/SKILL.md
Scanned
3 files, 258 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 automates the preparation of raw datasets for analysis by executing a sequence of cleaning operations. It handles common data quality issues such as missing values, duplicate entries, and inconsistent formatting to ensure downstream tasks receive reliable input.

When to use it

  • Before feeding unstructured or messy CSV/JSON files into machine learning models.
  • When preparing historical logs or sensor readings for trend analysis.
  • To standardize customer records from multiple disparate sources before merging.
  • As a preprocessing step prior to generating visualizations or business reports.

Key capabilities

  • Detects and removes duplicate rows based on specified columns.
  • Fills missing values using mean, median, or mode imputation strategies.
  • Standardizes date formats and normalizes text casing.
  • Filters out rows containing excessive null entries or outliers.
  • Validates data types against expected schemas before processing.

Example prompts

  • "Run the data cleaning pipeline on my sales.csv file to remove duplicates and fill missing revenue values with the median."
  • "Execute a cleanup routine on my user_logs.json to standardize timestamp formats and filter out entries with empty IP addresses."
  • "Apply the data cleaning pipeline to preprocess my survey responses, handling missing answers by dropping those specific rows entirely."

Tips & gotchas

Ensure your source files are accessible in the agent's working directory before invoking the skill. While the pipeline handles common anomalies, complex domain-specific logic may require custom scripting outside this standard tool.