ux-audit

name	ux-audit
description	Run a heuristic UX audit of a customer journey from screenshots. Use when the user asks for a UX audit, heuristic evaluation, usability review, design teardown, or expert review of screens, mockups, or a flow, whether your own product or a competitor's. Evaluates against named frameworks (Nielsen, Shneiderman, Gerhardt-Powals, Bastien & Scapin, Hick/Fitts/Miller/Jakob/Peak-End, Fogg, Cialdini, Gestalt, Norman, Tognazzini, WCAG 2.1, content design heuristics) and produces severity-rated findings with heuristic citations, annotated screenshots, and a structured markdown report. Do not use for generating new designs or for code-level accessibility scans of a live DOM.
metadata	{"author":"Elia Alberti","version":"1.0.0","argument-hint":"[screenshot folder or image paths]"}

UX Audit Skill

Evaluate a customer journey (screenshots) against established UX heuristic frameworks. Output: severity-rated findings with explicit heuristic citations, recommended improvements, annotated screenshots, and a structured report. Findings cite specific named heuristics, never generic design feedback.

Works in any agent that supports the Agent Skills format (Claude Code, Codex, and compatible tools).

Workflow

Step 1: Collect screenshots

• Accept a folder path, individual image paths, or attached images
• If a folder: list image files (png/jpg/jpeg/webp), sort by filename, propose this as the journey order
• If no screenshots provided: ask for them before anything else

Step 2: Intake: ask, then STOP and wait for answers

Skip any question already answered by the user's request. Otherwise ask in one message:

1. What is the product, and what is the user trying to achieve in this journey? (e.g. "E-commerce checkout, buy one item as a guest")
2. Platform? iOS / Android / mobile web / desktop web (affects target sizes and convention baselines)
3. User type? First-time / returning / mixed
4. Depth? Full sweep (all frameworks) or focused (name 2 to 3 frameworks or concerns)
5. Confirm journey order of the screens (state your inferred order; ask only if uncertain)

Do not begin the audit until the goal is known. Severity ratings are anchored to it.

Step 3: View every screenshot

• Open each image and study it fully
• Per screen, note: step name, primary action, visible states, anything illegible (never guess unreadable text, mark it as illegible)

Step 4: Evaluate: four passes

Pass A: Goal walk. Walk the journey as the stated user pursuing the stated goal. Where does friction, doubt, or a dead end appear? This anchors severity: a violation that doesn't touch the goal is rarely above sev 2.

Pass B: Framework sweep. Run each screen against the Framework Reference below. Every finding cites its source: "Nielsen #1", "WCAG 1.4.3", "Fitts's Law", "B&S: Guidance/Feedback".

Pass C: Named checks. Run the Named Recurring Checks table below. Tag findings with check IDs so the same issue is flagged identically across audits.

Pass D: Journey level. Cross-screen consistency (Nielsen #4, WCAG 3.2.3/3.2.4, PATTERN-DRIFT), progress visibility, Miller's Law memory load across steps, Peak-End Rule (worst moment + final screen), and a Fogg B=MAP diagnosis of the journey's key conversion action.

Step 5: Calibrate findings

• Apply the Severity Scale and Evidence Rules below
• Merge duplicates: same issue on several screens = one journey-level finding listing occurrences
• Assign IDs F-01, F-02… ordered by severity (highest first); positives get P-01…
• Include 2 to 5 positive findings: what works and should be kept
• Quality bar: prefer 8 strong, evidenced findings over 25 padded ones. A framework that surfaces nothing gets no findings; do not stretch for coverage

Step 6: Annotate screenshots

1. Build annotations.json (format documented in the script header): for each finding, estimate the issue region as fractions (0 to 1) of image width/height. Use generous boxes around regions, not points; coordinate estimates are approximate
2. Run python3 <this-skill-folder>/scripts/annotate.py annotations.json: the script is bundled in this skill's scripts/ directory
3. Verify: view each annotated image. If a marker clearly sits on the wrong element, fix coordinates and re-run (once)
4. Requires Pillow (python3 -c "import PIL"); if missing, pip3 install --user pillow. If installation is impossible, fall back to an annotated.html page with absolutely-positioned numbered pins over the screenshots (same colour scale)

Step 7: Write the report

• Output folder: ./ux-audit-{YYYY-MM-DD}-{slug}/ in the working directory (or where the user specifies). Copy originals and annotated images into assets/
• Write report.md using the Report Template below
• Close with: top 3 fixes in chat, and offer to draft tickets/issues from sev 3 to 4 findings

---

Severity Scale (Nielsen 0 to 4)

Rate by impact on the stated goal × how many users hit it × whether they hit it repeatedly.

Rating	Label	Meaning
4	Critical	Blocks goal completion, causes loss/harm, or legal/regulatory exposure; fix before release
3	Major	Significant friction; many users struggle, some abandon; high priority
2	Minor	Noticeable; users recover quickly; schedule it
1	Cosmetic	Polish; fix when convenient
✓	Positive	Works well; keep and replicate

Evidence Rules (non-negotiable)

• Every finding must point at something visible in a named screenshot. No inferred behaviour stated as fact
• Interaction qualities (animation, focus order, latency, keyboard, screen reader) go in "Not assessable from static screens", never guessed
• Illegible or ambiguous content: say so or ask, never invent text
• Sev 3 to 4 findings require an explicit heuristic citation AND visible evidence
• Borderline contrast calls: flag as "verify with a contrast checker" rather than asserting a ratio

---

Framework Reference (evaluate against these: cite by name + number)

A. Nielsen's 10 Usability Heuristics: cite "Nielsen #N"

1. Visibility of system status: timely feedback; progress, confirmations, current state always visible
2. Match system ↔ real world: users' language, familiar concepts, logical order; no internal jargon
3. User control & freedom: undo, cancel, clear exits; no forced paths
4. Consistency & standards: internal consistency + platform conventions
5. Error prevention: constraints, confirmation for destructive/costly actions, good defaults
6. Recognition over recall: options visible; nothing memorised across screens
7. Flexibility & efficiency: accelerators for frequent users; sensible shortcuts
8. Aesthetic & minimalist design: no irrelevant info competing with relevant
9. Error recovery: plain-language errors: what happened, why, how to fix
10. Help & documentation: contextual, task-focused, findable when needed

B. Shneiderman's 8 Golden Rules: cite "Shneiderman #N"

1 Consistency · 2 Universal usability (novice AND expert paths) · 3 Informative feedback · 4 Dialogs yield closure (clear beginning-middle-end) · 5 Error prevention · 6 Easy reversal · 7 User keeps control · 8 Reduce short-term memory load

C. Gerhardt-Powals' Cognitive Engineering Principles: cite "G-P #N"

1 Automate unwanted workload · 2 Reduce uncertainty · 3 Fuse related data into fewer units · 4 Present new info within familiar frames · 5 Names conceptually related to function · 6 Consistent, meaningful grouping · 7 Don't make users compute (show derived values) · 8 Only needed info on screen at a given time · 9 Multiple coding of data (text + icon + position) · 10 Judicious redundancy

D. Bastien & Scapin Ergonomic Criteria: cite "B&S: {criterion}"

Guidance (prompting, grouping/distinction, immediate feedback, legibility) · Workload (brevity, information density) · Explicit control (explicit user actions, user in control) · Adaptability (flexibility, experience levels) · Error management (protection, message quality, correction) · Consistency · Significance of codes (labels/icons mean what they say) · Compatibility (with the user's task and world)

E. Behavioural Laws: cite by name

• Hick's Law: decision time grows with number/complexity of choices. Count choices at each decision point; flag >~7 undifferentiated options with no grouping, default, or progressive disclosure
• Fitts's Law: acquisition time = f(distance, size). Primary actions large and thumb-reachable on mobile; related actions adjacent; flag tiny or edge-crowded targets
• Miller's Law: working memory ≈ 7±2 chunks. Anything users must hold across steps (codes, prices, comparisons) must stay visible or be chunked
• Jakob's Law: users expect your product to work like everything else they use. Deviations from convention must earn their cost
• Peak-End Rule: journeys are remembered by their worst/best moment and their ending. Audit the emotional low point (errors, payment, waits) and the final screen (confirmation = closure + clear next step)

F. Fogg Behavior Model (B = MAP): for the journey's key action

Behaviour happens when Motivation × Ability × Prompt converge at the same moment.

• Prompt: is the trigger for the next step visible and timely on each screen?
• Ability: which simplicity factor does the design tax: time, money, physical effort, mental effort, social acceptability, non-routine?
• Motivation: does copy/design sustain it (value reminder, progress, reassurance) at high-friction moments? Diagnose drop-off risk per screen: missing prompt / too hard / demotivating.

G. Cialdini's Principles of Influence: cite "Cialdini: {principle}"

Reciprocity · Commitment & consistency · Social proof · Authority · Liking · Scarcity · Unity

• Flag missing trust signals at decision points (reviews, guarantees, security cues)
• Flag misuse as DARK-PATTERN: fabricated scarcity/urgency, confirmshaming, sneaking, sev 3+ (trust and regulatory risk)

H. Gestalt Principles: cite "Gestalt: {principle}"

Proximity · Similarity · Closure · Continuity · Common region · Figure/ground · Common fate Core test: does visual grouping match functional grouping? Unrelated items accidentally grouped, or related items separated?

I. Norman's Design Principles: cite "Norman: {principle}"

Affordances · Signifiers (is interactivity signposted?) · Mapping (controls ↔ effects) · Feedback · Constraints · Conceptual model (does the UI teach a coherent model?) · Discoverability

J. Tognazzini's First Principles (selected): cite "Tog: {principle}"

Anticipation (bring what's needed to the user) · Autonomy · Colour never sole carrier of meaning · Smart defaults, easily replaced · Discoverability · Efficiency of the user · Explorable interfaces (safe to wander, easy to back out) · Latency reduction (perceived speed: skeletons, feedback) · Protect users' work (never lose input) · Readability (real-world conditions) · Visible navigation (where am I, where can I go)

K. WCAG 2.1: static-image-checkable subset, cite "WCAG {SC}"

• 1.4.1 Use of Colour: information/state never conveyed by colour alone
• 1.4.3 Contrast (AA): text ≥4.5:1 (≥3:1 for large text); flag borderline cases for tooling verification
• 1.4.11 Non-text Contrast: UI components and meaningful graphics ≥3:1
• 1.3.3 Sensory Characteristics: instructions don't rely on shape/position/colour alone
• 2.4.6 Headings & Labels: descriptive
• 2.5.5 Target Size: ≥44pt (Apple HIG) / 48dp (Material) / 24px absolute floor; spacing between adjacent targets
• 3.2.3 / 3.2.4 Consistency: navigation and components consistent across screens
• 3.3.1 / 3.3.2 / 3.3.3 Errors & Labels: errors identified in text, fields labelled (not placeholder-only), fixes suggested Not assessable from static screens (list in report, never guess): focus visibility, keyboard navigation, screen-reader semantics/alt text, reflow/zoom, motion, timing

L. Content Design Heuristics: cite "Content #N" (canonical set for this skill)

1 Front-load: key info and keywords first in headings, labels, paragraphs 2 Plain language: short sentences, common words, reading age ~9 to 11 3 Verb-first action labels: buttons say what they do ("Save changes", never bare "OK"/"Submit" ambiguity) 4 Scannable: chunked, meaningful headings, lists over walls of text 5 One term per concept: nothing renamed mid-journey 6 Errors that help: what happened + why + exactly how to fix 7 Honest microcopy: real expectations (timings, costs, consequences); no weasel reassurance 8 Tone fits the moment: errors/money/personal data = calm and clear, not playful 9 Every word earns its place: remove filler and redundant instruction 10 Inclusive & localisable: no idioms or cultural assumptions; survives translation

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Named Recurring Checks (stable IDs: same issue always flagged the same way)

ID	Check
CTA-AMBIGUITY	Primary action unclear, competing CTAs of equal weight, next step not obvious
DEAD-END	Screen with no forward action or escape route (incl. error/empty states)
FAKE-AFFORDANCE	Looks interactive but isn't / interactive but doesn't look it
CONTRAST-FAIL	Text or UI element visibly below WCAG ratios
TOUCH-TARGET	Targets too small or too crowded for thumb use
JARGON-LEAK	Internal, system, or legal language exposed to end users
PROGRESS-BLIND	Multi-step flow without position indicator or saved state
ERROR-VAGUE	Error state without cause + remedy
FORM-FRICTION	Unnecessary fields, unclear formats, placeholder-as-label, no inline validation
OVERLOAD	Everything at once; no hierarchy or progressive disclosure
PATTERN-DRIFT	Same component looks or behaves differently across screens
TRUST-GAP	High-stakes moment (money, personal data) without reassurance/security signals
DARK-PATTERN	False urgency, confirmshaming, sneaking, roach motel
STATE-GAP	Missing empty/loading/error/success state in the flow evidence
HIERARCHY-FLAT	No clear visual priority; everything shouts
RECALL-TAX	User must remember info from a previous screen to proceed

Extend this table over time: when the same unnamed issue appears in two audits, give it an ID here.

---

Report Template (report.md)

---
type: ux-audit
date: {YYYY-MM-DD}
product: {Product}
journey: {Journey name}
platform: {ios|android|mobile-web|desktop-web}
screens: {n}
findings-critical: {n}
findings-major: {n}
findings-minor: {n}
findings-cosmetic: {n}
---

# UX Audit, {Product}: {Journey}

## Executive Summary
{3 to 5 sentences: overall assessment vs the goal, biggest risks, top 3 fixes. Written for a stakeholder who reads nothing else.}

## Scope & Method
- **Goal evaluated:** {user goal}
- **User type / platform:** {…}
- **Screens:** | Step | File | Screen |
- **Frameworks applied:** Nielsen, Shneiderman, Gerhardt-Powals, Bastien & Scapin, behavioural laws, Fogg, Cialdini, Gestalt, Norman, Tognazzini, WCAG 2.1 (static subset), Content heuristics
- **Not assessable from static screens:** focus states, keyboard/screen-reader behaviour, motion, latency, {…}

## Findings Overview
| ID | Sev | Screen | Check | Finding | Heuristics |
|----|-----|--------|-------|---------|------------|
{sorted by severity, descending}

## Screen-by-Screen
### Step {n}: {Screen name} (`{file}`)
![annotated]({assets/file-annotated.png})

#### [S{sev}] {F-ID} · {Title}
- **Check:** {CHECK-ID} · **Heuristics:** {citations}
- **Evidence:** {what is visible, where}
- **Impact on goal:** {why this threatens the stated goal}
- **Recommendation:** {specific, actionable} · **Effort:** S/M/L

## Journey-Level Findings
{cross-screen consistency, progress, memory load, Peak-End, Fogg B=MAP diagnosis of the key action}

## What Works (keep)
- [P-01] {strength + heuristic it satisfies}

## Prioritised Recommendations
  (sev 3 to 4, effort S): {…}
  (sev 3, effort M/L): {…}
  (sev 1 to 2): {…}


| Framework | Findings |
|-----------|----------|
{framework → finding IDs or "no issues found"}


Nielsen: nngroup.com/articles/ten-usability-heuristics · Shneiderman: cs.umd.edu/users/ben/goldenrules.html · Gerhardt-Powals: Int. J. HCI (1996) · Bastien & Scapin: INRIA RT-0156 (1993) · Laws: lawsofux.com · Fogg: behaviormodel.org · Cialdini: influenceatwork.com · Norman: jnd.org · Tognazzini: asktog.com/atc/principles-of-interaction-design · WCAG: w3.org/WAI/WCAG21/quickref · Content design: contentdesign.london

Output Rules

• Annotated screenshots: {file}-annotated.png in assets/, markers colour-coded by severity (red 4 / orange 3 / amber 2 / blue 1 / green positive)
• Findings sorted by severity everywhere; IDs stable within the report
• Keep the report folder self-contained (originals + annotated copies inside)
• Match the user's locale and spelling conventions in the written report