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Moltguard

Checked
openguardrails·vlatest·other

MoltGuard proactively identifies and mitigates potential vulnerabilities in your code by analyzing it for common security flaws, enhancing application safety.

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 openafw/moltguard --skill moltguard -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

openguardrails

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 openguardrails/moltguard at 280eb5ff84.

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

Commit
280eb5ff84
Manifest
moltguard/clawhub-skill/SKILL.md
Scanned
1 file, 151 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 moltguard skill provides a mechanism to monitor and log AI agent interactions, specifically focusing on identifying potentially harmful or unsafe outputs. It allows developers to track conversations, analyze responses for policy violations, and ultimately improve the safety and reliability of their agents. This skill is designed to be an integral part of a broader guardrails system.

When to use it

  • Monitoring Agent Safety: After deploying an agent, use moltguard to observe its behavior and identify potential risks or biases in responses.
  • Debugging Policy Violations: If your agent flags a response as violating a policy, moltguard can help you understand the context and root cause of the issue.
  • Improving Guardrail Effectiveness: Analyze logged interactions to refine your guardrails and improve their accuracy in preventing harmful outputs.

Key capabilities

  • Conversation Logging: Records complete agent conversations for review.
  • Policy Violation Detection: Identifies responses that potentially violate predefined safety policies.
  • Integration with Guardrails System: Designed as a component within a larger AI safety framework.

Example prompts

  • "Log this conversation and check for policy violations."
  • "Show me the last 10 logged conversations."
  • "Analyze this interaction to see why it was flagged."

Tips & gotchas

The moltguard skill is most effective when used in conjunction with a comprehensive set of guardrails and policies. Ensure your agent's configuration properly integrates with the logging functionality for optimal monitoring.