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Data Quality Checker

Checked
tradermonty·vlatest·data·MIT

Identifies and flags data quality issues in trading datasets, ensuring accuracy and reliability for Tradermonty's analysis.

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 tradermonty/claude-trading-skills --skill data-quality-checker -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

tradermonty

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 tradermonty/claude-trading-skills at fac23bed61.

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

Commit
fac23bed61
Manifest
skills/data-quality-checker/SKILL.md
Scanned
7 files, 1,859 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 data-quality-checker skill assesses the quality of tabular data, identifying inconsistencies and potential errors. It can check for missing values, incorrect data types, and outliers within a dataset. This helps ensure that downstream analyses or models are built on reliable information.

When to use it

  • Data Validation: Before feeding data into a machine learning model, verify its accuracy and completeness.
  • Error Detection in Reports: Quickly identify potential errors in generated reports based on tabular data.
  • Cleaning Imported Data: Assess the quality of data imported from external sources like CSV files or databases.
  • Data Migration Checks: Ensure data integrity during migration processes between systems.

Key capabilities

  • Missing value detection
  • Incorrect data type identification
  • Outlier analysis

Example prompts

  • "Check this CSV file for missing values and incorrect data types: [file content]"
  • "Analyze the following table and flag any outliers: [table data]"
  • "Assess the quality of this dataset and provide a summary of potential issues: [dataset description]"

Tips & gotchas

The skill requires access to the data being checked, either as a file or in a structured format. The accuracy of the assessment depends on the clarity and completeness of the provided data; ambiguous datasets may yield less reliable results.