supervisor-readiness

Aggregate runtime health signals and watch items into readiness labels.

Updated Apr 22, 2026
One-click install
npx skills add https://github.com/EnesMeyzin98/Meridian --skill supervisor-readiness
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: supervisor-readiness
Source: https://github.com/EnesMeyzin98/Meridian/tree/main/agents/skills/supervisor-readiness
Command: npx skills add https://github.com/EnesMeyzin98/Meridian --skill supervisor-readiness

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Maintains and extends the advisory supervisor-readiness layer that summarizes safe-operational health, watch items, and readiness state for long-running paper or testnet observation without changing execution behavior.

Core Features & Use Cases

  • Normalize labels such as healthy, caution, degraded, or blocked to ensure consistent operator interpretation.
  • Surface watch items and health summaries in operator dashboards and experiment reviews.
  • Preserve advisory posture: no changes to execution, risk gates, or hard kill switches.
  • Derive readiness from journaled runs to support trend analysis over time.

Quick Start

Inspect the latest supervisor-readiness payload and summarize its current health and watch items for operator review.

Frequently Asked Questions about supervisor-readiness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I summarize supervisor health signals for an operator dashboard?

To summarize supervisor health signals for an operator dashboard, aggregate runtime health data and watch items into concise readiness labels like healthy, caution, degraded, or blocked. This advisory reporting ensures operators can monitor states without altering execution behavior.

What are supervisor readiness labels and how do they work?

Supervisor readiness labels are normalized states—healthy, caution, degraded, or blocked—derived by aggregating runtime health signals and watch items. They provide a non-invasive advisory layer for operator dashboards and experiment reviews without modifying risk gates or execution.

Can I track supervisor health trends over time using journaled runs?

Yes, you can derive supervisor health trends over time by analyzing journaled runtime runs. Aggregating these historical health signals and watch items allows you to observe readiness state changes and support long-running testnet observation effectively.

Does advisory supervisor readiness reporting modify execution or risk gates?

Advisory supervisor readiness reporting does not modify execution, risk gates, or hard kill switches. It strictly preserves a non-invasive posture by only normalizing health summaries into labels like healthy or blocked for operator review.

When do I need explicit readiness labeling for long-running testnet observation?

You need explicit readiness labeling for long-running testnet observation when operators require clear, summarized health states. Translating raw health signals and watch items into labels like caution or degraded supports safe-operational decision-making without invasive system changes.