Install
$ agentstack add skill-owl-listener-ai-design-skills-escalation-design ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Owl-Listener
- Source: Owl-Listener/ai-design-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.