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Provides guidelines for AI to handle errors, uncertainty, and limitations gracefully, including error types, communication patterns, anti-patterns, severity calibration, and design artifacts.
How this skill is triggered — by the user, by Claude, or both
Slash command
/system-behavior-shaping:error-personalityThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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:
npx claudepluginhub owl-listener/ai-design-skills --plugin system-behavior-shapingArchitects AI personas defining character traits, voice guidelines, behavioral rules, boundaries, anti-patterns, and documentation for consistent AI products.
Classifies AI failures into content, behavioral, technical, and safety categories with severity levels. Helps teams log, trend, prioritize, and analyze issues like hallucinations and refusals.
Use this skill when the user asks to "analyze AI errors", "error analysis for our AI feature", "open coding", "axial coding", "analyze model failures", "categorize AI mistakes", "find patterns in bad AI outputs", "what's wrong with our AI", or has a set of bad AI outputs and wants to understand what's failing and why. This is the first step in the AI eval methodology from Hamel Husain and Shreya Shankar.