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: 61 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 persistence
• ai-conversation-memory: Cross-session context handling
• graceful-ai-ambiguity: Ambiguous input handling
• ai-context-capture: Context across a session

copilot specific:

• efficient-ai-invocation: Trigger friction
• efficient-ai-dismissal: Dismissal without penalty
• contextual-ai-timing: When AI surfaces suggestions
• contextual-ai-relevance: Whether suggestions match context

agentic-workflow specific:

• ai-action-consequences: Preview before irreversible actions
• agent-task-handoff: Human takeover mechanisms
• agent-memory-patterns: Context across agent runs
• ai-audit-trails: Logging what the agent did and why
• automation-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/en/checkout'"
}

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/en/checkout"
}

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

1. interface_type is one of the seven allowed values
2. Every finding has a principle.slug from the Part V taxonomy
3. trust_assessment has all four keys filled
4. Any irreversible-action violation of ai-action-consequences is critical
5. overall_score is between 0 and 100 and band matches
6. priority_fixes lists only IDs from findings
7. api_enriched accurately reflects toolbox call outcome
8. The output is valid JSON with no prose before or after