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Audit Skill

Checked
bhargavtz·vlatest·security·Apache-2.0

Analyzes text data to identify potential compliance issues, risks, and inconsistencies based on provided rulesets.

Repository

Install on your platform

This skill no longer installs under the name audit-skill. bhargavtz/audit-skill has no skill named "audit-skill" any more. It was renamed, moved or deleted upstream.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-09-28. We don't show a command we know will fail.

Repository install instructions

What we know about this skill

Publisher

bhargavtz

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 bhargavtz/audit-skill at 79688fe96c.

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

Commit
79688fe96c
Manifest
SKILL.md
Scanned
17 files, 1,914 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 audit-skill allows an AI agent to systematically review and analyze a given text or data set. It can identify potential biases, inaccuracies, or inconsistencies within the provided content. The skill aims to provide a structured report highlighting areas of concern and suggesting improvements for increased reliability and objectivity.

When to use it

  • Content Verification: Before publishing articles or reports, use the audit-skill to check for factual errors and ensure accuracy.
  • Bias Detection: Analyze marketing materials or internal communications to identify and mitigate potential biases in language or messaging.
  • Code Review Assistance: Have the agent review code snippets for common vulnerabilities or adherence to coding standards.
  • Data Quality Assessment: Evaluate datasets before training machine learning models, identifying outliers or inconsistencies that could impact performance.

Key capabilities

  • Bias detection
  • Inaccuracy identification
  • Consistency checking
  • Structured reporting

Example prompts

  • "Audit this article for factual inaccuracies and potential biases: [paste article text]"
  • "Review the following code snippet for security vulnerabilities: [paste code]"
  • "Can you audit this dataset and identify any inconsistencies or outliers? [provide data]"

Tips & gotchas

The quality of the audit depends heavily on the clarity and completeness of the input provided. For optimal results, provide context where relevant to help the agent understand the intended purpose and scope of the content being audited.