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escalation-design

escalation-design

github.com/owl-listenerDesign & UI191133 installsUpdated 20w ago
Repository

About escalation-design

Escalation Design is a skill for planning how an AI should hand off requests it cannot safely or reliably handle alone. It helps teams define escalation triggers, routes, context transfer, and fallback experiences.

Key features

  1. 01Identify escalation triggers
  2. 02Define handoff routes and recipients
  3. 03Design conversation context transfer
  4. 04Plan fallback paths when support is unavailable
  5. 05Avoid common escalation failures

Use cases

  1. 01Set escalation rules for high-stakes requests
  2. 02Design human support handoffs
  3. 03Plan responses to unresolved ambiguity

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/owl-listener/ai-design-skills --skill escalation-design

After installing, type/escalation-designin Claude Code to confirm it loaded.

Details

From the project README

Escalation Design

Escalation is what happens when the AI reaches the boundary of what it should handle alone. Designing escalation well means the user gets help instead of a dead end — and the AI knows its limits.

Escalation Triggers

The AI should escalate when:

  • Confidence is low: The AI isn't sure its output is correct or helpful
  • Stakes are high: The decision has significant consequences (financial, medical, legal, safety)
  • Emotional distress: The user shows signs of crisis, distress, or vulnerability
  • Ambiguity is unresolvable: The AI can't determine intent even after clarification
  • Scope boundary: The request is outside what the AI is designed to handle
  • Policy boundary: The request approaches or crosses a guardrail
  • Conflict: The user disagrees with the AI and the disagreement can't be resolved

Escalation Types

  • To human support: Transfer to a human agent with full context
  • To the user themselves: "This decision is yours to make" — handing back agency
  • To a specialist: Routing to domain-specific help (medical, legal, technical)
  • To a supervisor/admin: Flagging for organisational review
  • Self-escalation: The AI flags its own output for review before delivering it

Designing the Escalation Experience

The user's experience of escalation matters:

  • Context transfer: When escalating to a human, pass the full conversation. Don't make the user repeat themselves.
  • Warm handoff: "I'm connecting you with someone who can help with this" — not a cold redirect.
  • Expectation setting: Tell the user what will happen next and how long it might take.
  • Graceful degradation: If no human is available, offer alternatives — not a dead end.
  • Dignity: Never make the user feel stupid for needing escalation.

Escalation Anti-Patterns

  • The infinite loop: AI keeps trying instead of escalating, frustrating the user
  • Premature escalation: AI escalates when it could easily handle the request, annoying the user
  • Context loss: User has to start over after escalation
  • Blame shifting: AI implies the user caused the problem
  • Hidden escalation: Escalation happens without the user knowing

Design Artefacts

  • Escalation trigger matrix: Trigger | Threshold | Escalation Type | User Experience
  • Escalation flow diagrams per feature
  • Context handoff specifications
  • Fallback path designs for when escalation isn't available
  • Escalation quality metrics

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