Critique

## STEPS

### Step 1: Preparation

Invoke /impeccable, which contains design principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding. If no design context exists yet, you MUST run /impeccable teach first. Additionally gather: what the interface is trying to accomplish.

### Step 2: Gather Assessments

Launch two independent assessments. **Neither must see the other's output** to avoid bias.

You SHOULD delegate each assessment to a separate sub-agent for independence. Use your environment's agent spawning mechanism (e.g., Claude Code's `Agent` tool, or Codex's subagent spawning). Sub-agents should return their findings as structured text. Do NOT output findings to the user yet.

If sub-agents are not available in the current environment, complete each assessment sequentially, writing findings to internal notes before proceeding.

**Tab isolation**: When browser automation is available, each assessment MUST create its own new tab. Never reuse an existing tab, even if one is already open at the correct URL. This prevents the two assessments from interfering with each other's page state.

#### Assessment A: LLM Design Review

Read the relevant source files (HTML, CSS, JS/TS) and, if browser automation is available, visually inspect the live page. **Create a new tab** for this; do not reuse existing tabs. After navigation, label the tab by setting the document title:
```javascript
document.title = '[LLM] ' + document.title;
```
Think like a design director. Evaluate:

**AI Slop Detection (CRITICAL)**: Does this look like every other AI-generated interface? Review against ALL **DON'T** guidelines in the impeccable skill. Check for AI color palette, gradient text, dark glows, glassmorphism, hero metric layouts, identical card grids, generic fonts, and all other tells. **The test**: If someone said "AI made this," would you believe them immediately?

**Holistic Design Review**: visual hierarchy (eye flow, primary action clarity), information architecture (structure, grouping, cognitive load), emotional resonance (does it match brand and audience?), discoverability (are interactive elements obvious?), composition (balance, whitespace, rhythm), typography (hierarchy, readability, font choices), color (purposeful use, cohesion, accessibility), states & edge cases (empty, loading, error, success), microcopy (clarity, tone, helpfulness).

**Cognitive Load** (consult [cognitive-load](reference/cognitive-load.md)):
- Run the 8-item cognitive load checklist. Report failure count: 0-1 = low (good), 2-3 = moderate, 4+ = critical.
- Count visible options at each decision point. If >4, flag it.
- Check for progressive disclosure: is complexity revealed only when needed?

**Emotional Journey**:
- What emotion does this interface evoke? Is that intentional?
- **Peak-end rule**: Is the most intense moment positive? Does the experience end well?
- **Emotional valleys**: Check for anxiety spikes at high-stakes moments (payment, delete, commit). Are there design interventions (progress indicators, reassurance copy, undo options)?

**Nielsen's Heuristics** (consult [heuristics-scoring](reference/heuristics-scoring.md)):
Score each of the 10 heuristics 0-4. This scoring will be presented in the report.

Return structured findings covering: AI slop verdict, heuristic scores, cognitive load assessment, what's working (2-3 items), priority issues (3-5 with what/why/fix), minor observations, and provocative questions.

#### Assessment