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Griffin Monitors

Checked
griffin-open-source·vlatest·devops

Griffin Monitors tracks specified URLs for content changes, alerting you to updates and potential issues – a valuable tool for website monitoring.

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 griffin-open-source/skills --skill griffin-monitors -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

griffin-open-source

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 griffin-open-source/skills at cac5257582.

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

Commit
cac5257582
Manifest
griffin-monitors/SKILL.md
Scanned
2 files, 550 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 griffin-monitors skill provides real-time monitoring capabilities for AI agents. It allows users to observe agent activity, track resource utilization, and identify potential performance bottlenecks. This skill is designed to enhance transparency and control over deployed AI agents.

When to use it

  • Debugging Agent Behavior: Investigate unexpected actions or errors by observing the agent's internal state and decision-making process.
  • Performance Optimization: Identify resource constraints (CPU, memory) that are impacting agent speed and efficiency.
  • Security Auditing: Track data access patterns and API calls to ensure compliance with security policies.
  • Monitoring Long-Running Tasks: Keep tabs on the progress of complex or time-consuming operations performed by the AI agent.

Key capabilities

  • Real-time activity tracking
  • Resource utilization monitoring (CPU, memory)
  • API call logging
  • Data access pattern analysis

Example prompts

  • "Show me the last 10 API calls made by the agent."
  • "What is the current CPU usage of the agent?"
  • "Can you log all data access events for this session?"
  • “Display a graph of memory consumption over the past hour.”

Tips & gotchas

The effectiveness of griffin-monitors depends on proper configuration and integration within the AI agent's environment. Ensure sufficient logging levels are enabled to capture relevant information for analysis.