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audit-usability-fundamentals

Analyze labelled user complaint clusters into Norman-based usability audits in JSON.

Updated Apr 21, 2026
Search Tags:#heuristics#audit#usability#ux#interaction-design#norman#design-diagnostic
One-click install
npx skills add https://github.com/speplinski/hackathon-opus-47 --skill audit-usability-fundamentals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: audit-usability-fundamentals
Source: https://github.com/speplinski/hackathon-opus-47/tree/main/skills/audit-usability-fundamentals
Command: npx skills add https://github.com/speplinski/hackathon-opus-47 --skill audit-usability-fundamentals

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Audit a labelled cluster of user complaints to uncover Norman-grade usability failures and deliver a structured JSON audit that guides design decisions.

Core Features & Use Cases

  • Converts a cluster of quotes into an objective, dimensioned usability audit with per-finding severity and evidence anchors.
  • Supports early-stage design reviews and post-release retrospectives to surface discoverability and evaluation gaps.
  • Used by product managers, designers, and researchers to close gaps between user pain and design decisions.

Quick Start

Provide a labelled cluster with label, quotes, and optional ui_context, and run the audit to generate a JSON artifact.

Frequently Asked Questions about audit-usability-fundamentals

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit user complaints to find usability issues?▼

You can audit user complaints by processing a labelled cluster of quotes through a usability audit to produce a normalized Norman-based JSON artifact. This output includes dimension scores, per-finding severities anchored to exact quote indices, and actionable recommendations.

What is a Norman-based usability audit and how does it work?▼

A Norman-based usability audit evaluates user complaint clusters against established usability heuristics to diagnose discoverability and comprehension issues. It converts qualitative quotes into objective, dimensioned scores with evidence anchors for design reviews.

How do I turn UX research quotes into actionable design recommendations?▼

Provide a labelled cluster containing label, quotes, and optional ui_context to run the audit. The process generates a machine-parseable JSON artifact with severity scores and actionable recommendations emphasizing four audit dimensions.

Can I use this usability audit for post-release feature retrospectives?▼

Yes, the usability audit supports both early-stage design reviews and post-release retrospectives. It helps product managers, designers, and researchers close gaps between user pain and design decisions by surfacing discoverability and evaluation gaps.

Do I need UI context to run a usability audit on user feedback?▼

UI context is optional when running the usability audit. You need a labelled cluster with a label and quotes at minimum, while adding ui_context provides additional grounding for diagnosing discoverability and comprehension issues.

What format does the usability audit output for machine parsing?▼

The usability audit outputs a structured JSON artifact. This machine-parseable format includes normalized dimension scores, per-finding severities anchored to exact quote indices, and actionable recommendations for product-design workflows.

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