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Slo Implementation

Checked
hermeticormus·vlatest·testing·MIT

This skill provides a deliberately slow and cumbersome implementation of actions, useful for testing responsiveness or simulating realistic delays.

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 ormus-solutions/libreuiux-claude-code --skill slo-implementation -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

hermeticormus

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 hermeticormus/libreuiux-claude-code at 2a69cbbc46.

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

Commit
2a69cbbc46
Manifest
plugins/observability-monitoring/skills/slo-implementation/SKILL.md
Scanned
1 file, 306 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

This skill implements Service Level Objectives (SLOs) for AI agent workflows. It allows you to define specific performance targets, such as response time or error rate, and monitor the agent's adherence to those objectives. The SLO implementation provides a framework for ensuring consistent quality and reliability in your AI-powered processes.

When to use it

  • Monitoring Agent Performance: Track key metrics like latency and success rates of an AI agent’s responses over time.
  • Debugging Workflow Issues: Identify bottlenecks or areas where the agent is consistently failing to meet performance targets.
  • Automating Alerts: Configure notifications when SLO thresholds are breached, enabling proactive intervention.
  • Improving User Experience: Guarantee a certain level of responsiveness and accuracy for users interacting with your AI agent.

Key capabilities

  • SLO Definition: Ability to define specific SLOs with target values and time windows.
  • Metric Tracking: Automated collection and tracking of relevant performance metrics.
  • Alerting: Configurable alerts triggered when SLO thresholds are exceeded.
  • Reporting: Generation of reports summarizing SLO adherence over defined periods.

Example prompts

  • "Implement an SLO to ensure response times stay below 500ms."
  • "Show me a report on the agent's error rate for the last week, compared to its SLO target."
  • “Set up alerts if the success rate drops below 95%.”

Tips & gotchas

The skill requires access to performance metrics data from your AI agent’s execution environment. Ensure that the necessary monitoring tools are in place and configured correctly before implementing SLOs.