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Data Validation

Checked
cosmix·vlatest·data·MIT

Helps with data, validation as part of agent 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 loom-data-validation, 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 cosmix/loom --skill loom-data-validation -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

cosmix

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 cosmix/loom at 851ce24346.

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

Commit
851ce24346
Manifest
skills/loom-data-validation/SKILL.md
Scanned
1 file, 256 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

Data Validation

What it does

This skill enables AI agents to automatically verify the accuracy, consistency, and integrity of datasets before processing or analysis. It ensures that input data meets specific quality standards, preventing errors from propagating through downstream tasks.

When to use it

  • Before feeding raw user inputs into complex reasoning models to avoid hallucinations based on bad data.
  • During automated ETL pipelines to catch missing values, duplicates, or format mismatches early.
  • When integrating external APIs where response schemas might change unexpectedly.
  • Prior to training fine-tuning datasets to ensure high-quality learning material.

Key capabilities

  • Automated schema checking against expected data structures.
  • Detection of anomalies, outliers, and logical inconsistencies within records.
  • Validation of data types, ranges, and required field presence.
  • Real-time feedback on data quality issues for immediate correction.

Example prompts

  • "Validate this CSV file against our customer database schema and list any missing mandatory fields."
  • "Check the consistency of dates in this sales log; flag any entries where the order date is after the delivery date."
  • "Run a data validation check on this JSON payload to ensure all required user profile attributes are present and correctly formatted."

Tips & gotchas

Ensure you define clear validation rules (e.g., specific formats or ranges) before running checks, as generic validation may miss domain-specific constraints. This skill is most effective when used proactively in the data ingestion pipeline rather than as a post-hoc cleanup tool.