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.