From evaluation
Evaluates AI interfaces with adapted Nielsen heuristics and AI-specific ones like calibrated trust, graceful degradation, and transparency of limitations. For UX reviews of AI products.
npx claudepluginhub owl-listener/ai-design-skills --plugin evaluationThis skill uses the workspace's default tool permissions.
Nielsen's 10 usability heuristics were designed for traditional software. AI products need adapted heuristics that address the unique challenges of probabilistic, generative, and conversational systems.
Evaluates digital interfaces using Nielsen's 10 usability heuristics to identify issues, rate severity (0-4), and recommend fixes.
Evaluates interfaces against Nielsen's 10 usability heuristics, produces severity-rated findings (0-4: Cosmetic to Catastrophic), and generates remediation recommendations with effort estimates.
Evaluates UX/UI of websites, apps, and digital interfaces using Jakob Nielsen's 10 usability heuristics. Identifies issues in visibility of status, system-real-world match, consistency, error prevention, flexibility, aesthetics, recognition, error recovery, and documentation.
Share bugs, ideas, or general feedback.
Nielsen's 10 usability heuristics were designed for traditional software. AI products need adapted heuristics that address the unique challenges of probabilistic, generative, and conversational systems.
1. Visibility of system status AI adaptation: The user should always know what the AI is doing, what it's working with, and how confident it is. Progress indicators for generation. Transparency about data sources. 2. Match between system and real world AI adaptation: The AI should use language and concepts the user understands. Don't expose model internals. Frame capabilities in terms of user tasks, not technical features. 3. User control and freedom AI adaptation: Users must be able to stop generation, undo AI actions, edit outputs, and override suggestions. AI autonomy should always have an exit. 4. Consistency and standards AI adaptation: The AI should behave consistently across similar requests. Same input type should produce same output format. Persona should be stable. 5. Error prevention AI adaptation: Design prompts and interfaces that guide users toward effective interactions. Suggest clarifications before producing low-quality output. 6. Recognition rather than recall AI adaptation: Show users what the AI can do rather than requiring them to discover commands. Surface relevant capabilities contextually. 7. Flexibility and efficiency of use AI adaptation: Support both novice (guided) and expert (shortcut) interaction modes. Power users should be able to customise AI behavior. 8. Aesthetic and minimalist design AI adaptation: AI outputs should be concise and well-structured. Don't pad responses with unnecessary caveats or filler. 9. Help users recognise, diagnose, and recover from errors AI adaptation: When the AI fails, explain what went wrong in user terms, not technical terms. Offer clear recovery paths. 10. Help and documentation AI adaptation: Provide contextual guidance on how to interact with the AI effectively. Teach prompting skills through the interface.
Beyond the classic 10, AI products need evaluation against: