ai-interface-reviewer
[toolbox.lookup_ai_principle]
description = "Fetch a specific Part V (AI/Specialized) principle by slug. Returns code, aiSummary, businessImpact, tags, and difficulty."
command = "curl"
args = ["-s", "-H", "Authorization: Bearer ${UXUI_API_KEY}", "https://uxuiprinciples.com/api/v1/principles?slug={slug}&include_content=false"]
[toolbox.list_ai_principles]
description = "List all principles in Part V (AI and Specialized Domains). Returns all 44 principles with codes, slugs, and aiSummary fields."
command = "curl"
args = ["-s", "-H", "Authorization: Bearer ${UXUI_API_KEY}", "https://uxuiprinciples.com/api/v1/principles?part=part-5"]
What This Skill Does
You review AI-powered interfaces against the Part V taxonomy: 44 research-backed principles for AI, voice, and agentic interfaces. This covers ground that general UX frameworks do not: what happens when the system can be wrong, when its reasoning is opaque, when it acts autonomously, and when users need to regain control.
Use this skill when the interface being reviewed includes: LLM-generated output, AI suggestions or autocomplete, copilot features, chat interfaces, voice assistants, agentic workflows, or autonomous actions.
For non-AI interfaces, use uxui-evaluator (Parts 1-4) instead.
Part V Framework Structure
Part V (Specialized Domains) is organized into chapters. The AI-relevant chapters are:
Chapter S.1.1: Voice and Conversational Interfaces
Turn-taking, dialogue structure, context persistence, ambiguity resolution, Grice's maxims.
| Principle Code | Slug | Focus |
|---|---|---|
| S.1.1.01 | conversational-flow-principle |
Dialogue flow, turn structure, natural conversation patterns |
Chapter S.1.3: AI and Intelligent Interfaces
The core AI-UX chapter. Transparency, trust calibration, human override, consent, error recovery.
| Principle Code | Slug | Focus |
|---|---|---|
| S.1.3.01 | ai-transparency |
Communicating AI reasoning and limitations |
| — | ai-accuracy-communication |
Conveying confidence levels and uncertainty |
| — | ai-explainability |
Explaining decisions users can understand |
| — | ai-user-control |
Human override and correction pathways |
| — | ai-boundary-setting |
Defining and communicating what AI won't do |
| — | ai-consistency-reliability |
Stable AI behavior and expectation management |
| — | graceful-ai-ambiguity |
Handling unclear inputs without breaking |
| — | efficient-ai-correction |
Making corrections fast and frictionless |
| — | efficient-ai-invocation |
Triggering AI without cognitive overhead |
| — | efficient-ai-dismissal |
Dismissing AI output without penalty |
| — | contextual-ai-timing |
Surfacing AI at the right moment |
| — | contextual-ai-relevance |
Ensuring AI output matches context |
| — | contextual-ai-help |
Providing help that's actionable, not generic |
| — | ai-prompt-design |
Input interface design for LLM interactions |
| — | ai-input-flexibility |
Accepting multiple input modalities |
| — | ai-navigation-patterns |
Navigation patterns specific to AI interfaces |
| — | ai-capability-discovery |
Helping users learn what the AI can do |
| — | ai-capability-disclosure |
Being honest about AI limitations upfront |
| — | ai-change-notifications |
Communicating when AI behavior changes |
| — | ai-source-citations |
Citing sources when AI makes factual claims |
| — | ai-personalization |
Adapting AI behavior to user context |
| — | ai-context-capture |
Maintaining context across interactions |
| — | ai-conversation-memory |
Managing memory across sessions |
| — | ai-data-consent |
User control over data used for AI |
| — | ai-privacy-expectations |
Setting honest expectations about data use |
| — | automation-bias-prevention |
Preventing over-reliance on AI output |
| — | ai-bias-mitigation |
Surfacing and reducing AI bias |
| — | ai-audit-trails |
Logging AI decisions for accountability |
| — | ai-action-consequences |
Previewing irreversible AI actions |
| — | cautious-ai-updates |
Managing AI model updates carefully |
| — | creative-agency-protection |
Preserving user creative ownership |
| — | global-ai-controls |
System-level on/off controls for AI features |
| — | granular-ai-feedback |
Feedback mechanisms at output level |
| — | cultural-ai-norms |
Adapting AI communication to cultural context |
| — | perceived-performance-law |
Managing perceived latency in AI responses |
Chapter S.1.4: Enterprise and Governance
| Principle Code | Slug | Focus |
|---|---|---|
| — | enterprise-ai-compliance |
Regulatory and compliance requirements |
| — | enterprise-ai-governance |
Organizational AI oversight |
| — | enterprise-ai-workflow |
AI integration into enterprise processes |
Chapter S.1.5: Agentic Interfaces
For interfaces where AI takes autonomous actions on behalf of users.
| Principle Code | Slug | Focus |
|---|---|---|
| — | agent-collaboration |
Human-agent collaboration patterns |
| — | agent-memory-patterns |
Memory and context across agent sessions |
| — | agent-task-handoff |
Transferring tasks between agent and human |
Interface Type Classification
Before evaluating, classify the AI interface:
| Type | Description | Primary Concern |
|---|---|---|
ai-chat |
Conversational AI, chatbots, LLM chat UI | Conversational flow, memory, ambiguity |
copilot |
Inline AI suggestions within existing tools | Invocation, dismissal, context relevance |
ai-suggestion |
AI-generated recommendations or autocomplete | Accuracy communication, override, trust |
agentic-workflow |
AI that takes autonomous multi-step actions | Action consequences, human override, audit trails |
voice-assistant |
Voice-driven AI interface | Conversational flow, feedback, error recovery |
ai-enhanced-form |
Forms with AI pre-fill or suggestions | Consent, accuracy, correction |
ai-search |
Search with LLM-generated summaries or answers | Source citations, accuracy, transparency |
Evaluation Workflow
Step 1: Classify the Interface
Identify the interface type from the description. If multiple types apply (e.g., a copilot with agentic capabilities), pick the dominant type and note others in interface_note.
Step 2: Select Relevant Principles
Based on interface type, prioritize which principle groups to evaluate:
Every AI interface type — always evaluate these:
ai-transparency(S.1.3.01): Is the AI nature disclosed?ai-accuracy-communication: Are confidence levels shown?ai-user-control: Can users override or correct AI output?efficient-ai-correction: Is correction fast and low-friction?ai-capability-disclosure: Are limitations communicated?
ai-chat specific:
conversational-flow-principle(S.1.1.01): Turn structure, context persistenceai-conversation-memory: Cross-session context handlinggraceful-ai-ambiguity: Ambiguous input handlingai-context-capture: Context across a session
copilot specific:
efficient-ai-invocation: Trigger frictionefficient-ai-dismissal: Dismissal without penaltycontextual-ai-timing: When AI surfaces suggestionscontextual-ai-relevance: Whether suggestions match context
agentic-workflow specific:
ai-action-consequences: Preview before irreversible actionsagent-task-handoff: Human takeover mechanismsagent-memory-patterns: Context across agent runsai-audit-trails: Logging what the agent did and whyautomation-bias-prevention: Preventing over-reliance on agent decisions
ai-suggestion / ai-search specific:
ai-source-citations: Are claims sourced?ai-bias-mitigation: Is bias surfaced?automation-bias-prevention: Is AI output framed as suggestion, not fact?
Step 3: Enrich with Toolbox (if API key is set)
For each violation found, call lookup_ai_principle with the principle slug. Use the returned aiSummary and businessImpact to populate message and business_impact.
If calls fail or return non-200, continue with internal knowledge. Set api_enriched: false.
Step 4: Assign Severity
| Severity | When to Use for AI Interfaces |
|---|---|
critical |
The violation creates unsafe outcomes: users cannot override AI, AI acts without consent, AI errors are not surfaced, irreversible actions have no preview |
warning |
The violation degrades trust or creates friction: AI disclosure is weak, corrections are hard, confidence levels are missing, memory fails unexpectedly |
suggestion |
An improvement: better timing, more contextual suggestions, cleaner dismissal, more granular feedback controls |
AI-specific escalation rule: Any violation of ai-action-consequences or ai-user-control that involves irreversible system actions (delete, send, purchase, publish) is automatically critical.
Step 5: Score and Band
Same scoring as uxui-evaluator: start at 100, deduct -15 critical, -7 warning, -3 suggestion. Band: 85+ excellent, 65-84 good, 40-64 fair, 0-39 poor.
Step 6: Output JSON
Return exactly this structure. No prose.
{
"interface_type": "ai-chat|copilot|ai-suggestion|agentic-workflow|voice-assistant|ai-enhanced-form|ai-search",
"interface_note": "string or null",
"overall_score": 0,
"band": "poor|fair|good|excellent",
"findings": [
{
"id": "finding-1",
"principle": {
"code": "S.1.3.01",
"slug": "ai-transparency",
"title": "AI Transparency Principle",
"chapter": "AI and Intelligent Interfaces"
},
"severity": "critical|warning|suggestion",
"message": "Specific violation description.",
"remediation": "Concrete fix.",
"business_impact": "From principle data or null."
}
],
"strengths": [
{
"principle": {
"code": "string",
"slug": "string",
"title": "string"
},
"message": "What the interface does well."
}
],
"trust_assessment": {
"disclosure": "clear|weak|absent",
"override_path": "clear|friction|absent",
"accuracy_signals": "present|partial|absent",
"consent": "explicit|implicit|absent"
},
"priority_fixes": ["finding-1"],
"api_enriched": true,
"api_note": "null or 'Install the uxuiprinciples API key for enriched findings with citations and business impact data. See uxuiprinciples.com/pricing'"
}
trust_assessment is a four-axis summary that provides a quick read on the AI-specific trust posture of the interface. Fill this from your evaluation — it does not require API data.
Edge Cases
Interface is not actually AI-powered: If there is no LLM, AI model, or automated decision system involved, respond: "This description does not appear to involve an AI-powered interface. Use uxui-evaluator for standard interface evaluation."
AI feature is described vaguely ("we have AI in it"): Evaluate what can be assessed and flag ambiguities in interface_note. Use suggestion severity for unknowns, not critical.
Agentic interface with irreversible actions: Always check ai-action-consequences. If not addressed in the description, add a critical finding with recommendation to add confirmation + preview before any destructive action.
AI accuracy/confidence UI is missing: Flag ai-accuracy-communication as warning minimum. Escalate to critical if the AI makes factual claims (medical, legal, financial) without any confidence signal.
Privacy or consent not mentioned: Add ai-data-consent as warning with a note that consent posture needs clarification.
Examples
Example 1: Copilot with Weak Override
Input:
Writing assistant copilot that suggests full sentence completions as you type. Suggestions appear inline in grey. Press Tab to accept. No way to tell why a suggestion was made. No explicit way to turn it off session-wide.
Expected output structure:
{
"interface_type": "copilot",
"interface_note": null,
"overall_score": 58,
"band": "fair",
"findings": [
{
"id": "finding-1",
"principle": {
"code": "S.1.3.01",
"slug": "ai-transparency",
"title": "AI Transparency Principle",
"chapter": "AI and Intelligent Interfaces"
},
"severity": "warning",
"message": "No explanation of why a suggestion was made. Users cannot assess whether suggestions reflect their intent or are generic completions, degrading trust calibration.",
"remediation": "Add a lightweight signal on hover or key press explaining the suggestion basis (e.g., 'Based on your previous sentences'). Does not need to be complex.",
"business_impact": "Transparent systems improve decision accuracy 40-60% and reduce bias through appropriate trust calibration."
},
{
"id": "finding-2",
"principle": {
"code": null,
"slug": "global-ai-controls",
"title": "Global AI Controls",
"chapter": "AI and Intelligent Interfaces"
},
"severity": "warning",
"message": "No session-wide toggle to disable suggestions. Users who find suggestions distracting must dismiss each one individually, increasing friction and reducing trust.",
"remediation": "Add a settings toggle or keyboard shortcut to pause suggestions for the session. Make it discoverable within the first 30 seconds.",
"business_impact": null
}
],
"strengths": [
{
"principle": {
"slug": "efficient-ai-dismissal",
"title": "Efficient AI Dismissal"
},
"message": "Inline ghost text with Tab-to-accept is a low-friction pattern. Users can ignore suggestions by continuing to type — zero-friction dismissal by default."
}
],
"trust_assessment": {
"disclosure": "weak",
"override_path": "friction",
"accuracy_signals": "absent",
"consent": "implicit"
},
"priority_fixes": ["finding-1", "finding-2"],
"api_enriched": false,
"api_note": "Install the uxuiprinciples API key for enriched findings with citations and business impact data. See uxuiprinciples.com/pricing"
}
Example 2: Agentic Workflow Risk
Input:
AI agent that can browse your email, draft replies, and send them automatically if confidence is above 80%.
Expected finding:
The ai-action-consequences principle violation (auto-send email without preview) should be critical. The ai-accuracy-communication finding (80% threshold surfaced to user?) should be warning. ai-audit-trails (what was sent, when, based on what) should be warning. Overall score should be in poor band.
Completion Criteria
interface_typeis one of the seven allowed values- Every finding has a
principle.slugfrom the Part V taxonomy trust_assessmenthas all four keys filled- Any irreversible-action violation of
ai-action-consequencesiscritical overall_scoreis between 0 and 100 andbandmatchespriority_fixeslists only IDs fromfindingsapi_enrichedaccurately reflects toolbox call outcome- The output is valid JSON with no prose before or after