Monitor Data Integrity

Name: Monitor Data Integrity
Author: pjt222

Listed

pjt222·vlatest·data·MIT

Helps with monitoring, data 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.

Run this in your project

terminal

$ npx skills add pjt222/agent-almanac --skill monitor-data-integrity -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

pjt222

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 30 Sept 2026

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

Declares what it does: failed
No SKILL.md was found in the skill's directory.
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 pjt222/development-guides at bcca822c1c.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: bcca822c1c
Manifest: skills/monitor-data-integrity/SKILL.md
Scanned: 0 files
Copies in repo: 3 — 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 monitor-data-integrity skill allows an AI agent to verify the consistency and accuracy of data within a specified dataset. It can identify missing values, detect outliers based on defined rules, and flag potential errors requiring human review. This helps ensure data quality for downstream processes like analysis or model training.

When to use it

Data Pipelines: Regularly check data flowing through ETL pipelines for unexpected changes or corruption.
Post-Ingestion Validation: After importing data from external sources, confirm its integrity before further processing.
Anomaly Detection: Identify unusual patterns in datasets that might indicate errors or malicious activity.
Model Training Data Quality: Ensure the training dataset is clean and accurate to improve model performance.

Key capabilities

Missing value detection
Outlier identification based on rules
Data consistency checks
Error flagging for review

Example prompts

"Monitor the 'sales_data' dataset for missing values in the 'customer_id' column."
"Check the 'inventory' data and flag any quantities below zero."
"Verify that all dates in the 'transaction_log' are within the last year."

Tips & gotchas

The skill requires a clear definition of rules for outlier detection. Ensure these rules accurately reflect expected data patterns to avoid false positives.