Validation

Name: Validation
Author: matteocervelli

Checked

matteocervelli·vlatest·other·MIT

Confirms data accuracy against external sources or predefined rules, flagging inconsistencies for review.

Repository

Install on your platform

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

Run this in your project

terminal

$ npx skills add matteocervelli/llms --skill validation --full-depth -a claude-code

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

matteocervelli

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 27 Sept 2026

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 matteocervelli/llms at 1e1f2089eb.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: 1e1f2089eb
Manifest: .archive/claude-v1/skills/validation/SKILL.md
Scanned: 8 files, 4,131 lines
Copies in repo: 2 — all of them scanned

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 validation skill allows an AI agent to verify data against predefined rules or formats. It can check if a string matches a specific pattern, confirm that a number falls within a certain range, or ensure a date is in the correct format. This ensures data integrity and reduces errors in subsequent processes.

When to use it

Form Processing: Validate user input from forms (e.g., email addresses, phone numbers) before submitting them.
Data Extraction: Confirm extracted information from documents adheres to expected formats (e.g., validating dates on invoices).
API Integration: Ensure data received from external APIs conforms to the required schema or constraints.
Code Generation: Validate generated code snippets against syntax rules and best practices.

Key capabilities

Pattern matching for strings
Range checking for numbers
Date format validation

Example prompts

"Validate this email address: [email protected]"
"Is the number 150 within the range of 100-200?"
"Check if '2023-12-25' is a valid date in YYYY-MM-DD format."

Tips & gotchas

The effectiveness of this skill depends on providing clear and accurate validation rules. Ensure the patterns or ranges provided are specific enough to avoid false positives or negatives.