
Don Norman Principles Audit
This runs a structured UX audit using Don Norman's seven principles from The Design of Everyday Things: discoverability, affordance, signifiers, feedback, mapping, constraints, and conceptual models. Feed it interface descriptions, screenshots, or live URLs along with key user tasks, and it evaluates each principle with severity ratings and concrete redesign recommendations. The output is a prioritized report that flags classic usability problems like hidden navigation, unclear clickability, or missing feedback. It's built for catching the digital equivalent of Norman doors before they frustrate real users. Works well solo or stacked with Nielsen heuristics for deeper coverage.
- skills.sh installs
- 387
- GitHub stars
- 69
- last updated
- Aug 21, 2026
Install
$ npx -y skills add mastepanoski/claude-skills --skill don-norman-principles-audit --agent claude-codeInstalls into .claude/skills of the current project.
Files
Don Norman Principles UX Audit
This skill enables AI agents to perform a human-centered evaluation of usability and intuitiveness for apps, websites, or digital interfaces using Don Norman's 7 fundamental design principles from The Design of Everyday Things.
The principles emphasize discoverability, natural perception, and cognitive load reduction. Use this skill to detect intuitive frustrations (like digital "Norman doors"), improve user experience, and propose redesigns.
Combine with "Nielsen Heuristics UX Audit" or "UX Audit and Rethink" skills for comprehensive audits.
When to Use This Skill
Invoke this skill when:
- Evaluating the intuitiveness of an interface
- Identifying usability problems from a human-centered perspective
- Assessing how naturally users can discover and use features
- Auditing digital products for cognitive friction
- Planning UX improvements based on fundamental design principles
Inputs Required
When executing this audit, gather:
Sponsors
Details
- Repository
- github.com/mastepanoski/claude-skills
- Category
- Design & UI/UX
- First seen
- Jun 3, 2026