escalation

Detect AI agent stalls and route failures to Zeus for resolution.

3|Updated Mar 14, 2026
One-click install
npx skills add https://github.com/GunjanGrunge/rrq --skill escalation-gunjangrunge
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: escalation
Source: https://github.com/GunjanGrunge/rrq/tree/main/skills/escalation
Command: npx skills add https://github.com/GunjanGrunge/rrq --skill escalation-gunjangrunge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill prevents agent failures from halting progress by providing a robust protocol for handling situations where an AI agent cannot converge on a solution, ensuring that no job is silently abandoned and that user intervention is managed effectively.

Core Features & Use Cases

  • Automated Stuck Detection: Identifies when an agent's retry loops are not yielding meaningful improvement.
  • Centralized Escalation: All agents route failures to this single protocol, owned by Zeus, for consistent handling.
  • Intelligent Decision Making: Zeus evaluates stuck states and can resolve issues, suggest new approaches, or escalate to a human.
  • User Notification & Action: Notifies users when human judgment is required, providing context and clear action options with timeouts and auto-decisions.
  • Use Case: When an image generation agent repeatedly fails to meet quality standards, this Skill will detect the stall, allow Zeus to attempt a fix, and if necessary, notify the user with options to approve, abort, or retry with a different strategy.

Quick Start

Use the escalation skill to handle a failed quality gate for the video titled 'Introductory Montage'.

Frequently Asked Questions about escalation

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

FAQPage Schema
How do I handle AI agent failures and prevent jobs from being abandoned?

Agent failure handling works by detecting when retry loops yield no meaningful improvement, routing the stuck state to a centralized evaluation protocol, and triggering user notifications with defined actions and timeouts to ensure no job is silently abandoned.

How do I detect when an AI agent is stuck in a retry loop?

Automated stuck detection identifies when an agent's retry loops are not yielding meaningful improvement, routing the stalled state to a centralized evaluation protocol to attempt a fix, suggest new approaches, or escalate to a human.

What is the best way to manage user notifications during an agent escalation?

User notification during an escalation is managed by sending context and clear action options with defined timeouts and auto-decisions, ensuring human judgment is requested only when necessary while preventing permanent job abandonment.

Can I use a centralized escalation protocol for different types of agent deadlocks?

Yes, a centralized escalation protocol handles diverse scenarios including quality gates, content detection, and deadlocks across multiple domains by routing all agent failures to a single owned protocol for consistent evaluation and resolution.

Why does an image generation agent repeatedly fail to meet quality standards?

An image generation agent fails to meet quality standards when it cannot converge on a solution; the escalation protocol detects the stall, allows a centralized manager to attempt a fix, and notifies the user with options to approve, abort, or retry with a different strategy.