bias-detection-design
Designing review workflows to surface and mitigate bias in AI outputs.
How the AI communicates mistakes, uncertainty, and limitations gracefully.
$ npx -y skills add owl-listener/ai-design-skills --skill error-personality --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/error-personalityContext preview
The summary Claude sees to decide when to auto-load this skill.
How the AI communicates mistakes, uncertainty, and limitations gracefully.
name: error-personality description: How the AI communicates mistakes, uncertainty, and limitations gracefully.
Every AI makes mistakes. Error personality is how the AI handles those moments — the tone, the honesty, the recovery. It's often the most revealing aspect of an AI persona, because it's where the mask of competence slips and the user sees the character underneath.
**The Honest Acknowledgment:** "I got that wrong. Here's the corrected version." — Direct, no excessive apology, immediately fixes the problem. **The Uncertain Hedge:** "I'm not fully confident in this — you might want to verify." — Flags uncertainty before the user discovers the error. **The Redirect:** "I can't do that, but here's what I can help with." — Turns a limitation into an alternative path. **The Learning Response:** "Thanks for the correction — I'll keep that in mind." — Acknowledges the user's input and signals adaptation.
The error personality should match the overall persona:
Not all errors deserve the same response:
Part of the Designer Skills suite. Install this collection and four more with one command in Claude Code: /plugin marketplace add Owl-Listener/designer-skills This repo still works on its own. The suite just gives everyone one front door.
Designing review workflows to surface and mitigate bias in AI outputs.
Designing for informed user consent, opt-out, and human override.
When and how AI should escalate to humans, refuse, or ask for clarification.
Defining behavioral boundaries — what the AI should and shouldn't do.
Proactively identifying failure modes, misuse, and unintended consequences.
Showing users what the AI knows, doesn't know, and how confident it is.