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Design experiences that optimize mental resources using Cognitive Load Theory. Use when designing interfaces, creating onboarding flows, planning information architecture, or improving task completion rates.
npx skill4agent add flpbalada/my-opencode-config cognitive-loadHuman Processing Capacity:
┌─────────────────────────────────────────────────────┐
│ WORKING MEMORY │
│ │
│ Capacity: 7 ± 2 items simultaneously │
│ Duration: ~20 seconds without rehearsal │
│ Processing: Serial, not parallel │
│ │
│ When exceeded → Errors, frustration, abandonment │
└─────────────────────────────────────────────────────┘
Design goal: Stay WITHIN capacity limits.Total Cognitive Load = Intrinsic + Extraneous + Germane
┌─────────────────────────────────────────────────────────────────┐
│ WORKING MEMORY CAPACITY │
├─────────────────┬─────────────────┬─────────────────────────────┤
│ INTRINSIC │ EXTRANEOUS │ GERMANE │
│ │ │ │
│ Task │ Bad design │ Learning & │
│ complexity │ noise │ schema building │
│ │ │ │
│ Can't reduce │ ELIMINATE │ MAXIMIZE │
│ without │ THIS │ THIS │
│ changing task │ │ │
└─────────────────┴─────────────────┴─────────────────────────────┘| Characteristic | Example |
|---|---|
| Determined by task complexity | Filing taxes vs. sending email |
| Varies by user expertise | Expert finds it easy, novice struggles |
| Cannot be eliminated | Only managed through design |
Common Sources of Extraneous Load:
Visual:
├── Cluttered layouts
├── Poor typography
├── Inconsistent patterns
└── Distracting animations
Interaction:
├── Unclear navigation
├── Unexpected behaviors
├── Too many options
└── Hidden functionality
Content:
├── Jargon and complexity
├── Redundant information
├── Missing context
└── Poor information hierarchy| Benefit | Example |
|---|---|
| Builds mental models | User learns system behavior |
| Enables efficiency | Experienced user works faster |
| Creates transferable knowledge | Skills apply to similar tasks |
Task Decomposition:
[Main Task]
├── Step 1: [Action] → Load type: [I/E/G]
│ └── Decisions required: [count]
├── Step 2: [Action] → Load type: [I/E/G]
│ └── Decisions required: [count]
├── Step 3: [Action] → Load type: [I/E/G]
│ └── Decisions required: [count]
└── Completion| Step | Intrinsic Load | Extraneous Load | Germane Load |
|---|---|---|---|
| 1 | [Task complexity] | [Design issues] | [Learning value] |
| 2 | [Task complexity] | [Design issues] | [Learning value] |
| 3 | [Task complexity] | [Design issues] | [Learning value] |
Intervention Strategy:
High Intrinsic Load?
├── YES → Break into smaller steps
├── YES → Add progressive disclosure
├── YES → Provide scaffolding/help
└── YES → Offer simplified path
High Extraneous Load?
├── YES → Remove unnecessary elements
├── YES → Improve visual hierarchy
├── YES → Simplify navigation
└── YES → Fix inconsistent patterns
Low Germane Load?
├── YES → Add meaningful feedback
├── YES → Create learnable patterns
├── YES → Design for skill building
└── YES → Connect to existing knowledge## Cognitive Load Analysis
**Feature/Flow:** [Name]
**Date:** [Date]
### Task Map
| Step | Description | Intrinsic | Extraneous | Germane |
| ---- | ----------- | ------------ | ------------ | ------------ |
| 1 | [Step] | High/Med/Low | High/Med/Low | High/Med/Low |
| 2 | [Step] | High/Med/Low | High/Med/Low | High/Med/Low |
### Load Issues Identified
#### Intrinsic Load Problems
- [Issue]: [Location]
- [Issue]: [Location]
#### Extraneous Load Problems
- [Issue]: [Location]
- [Issue]: [Location]
### Recommendations
| Priority | Change | Expected Impact |
| -------- | ----------------- | --------------------- |
| High | [Specific change] | [Reduces X load by Y] |
| Medium | [Specific change] | [Reduces X load by Y] |
### Success Metrics
| Metric | Current | Target |
| -------------------- | ------- | ------ |
| Task completion rate | X% | Y% |
| Time to complete | X min | Y min |
| Error rate | X% | Y% |
| User satisfaction | X | Y |Load Optimization:
Intrinsic: Minimal - just type what you want
Extraneous: Near zero - white space + search box
Germane: Focus on learning query refinement
Result: 2-3x faster searches than competitorsNew User Experience:
├── Basic: Messaging interface only
├── Intermediate: Threading, channels
├── Advanced: Integrations, workflows
└── Enterprise: Admin controls
Each level builds on previous knowledge (germane load)
without overwhelming new users (extraneous load).System-wide Patterns:
Gestures:
├── Swipe from left = back (everywhere)
├── Pull down = refresh (everywhere)
├── Long press = more options (everywhere)
└── Pinch = zoom (everywhere)
Consistency eliminates extraneous load.
Users learn once, apply everywhere.| Strategy | Implementation |
|---|---|
| Task chunking | Max 3-5 items per screen |
| Mental model alignment | Use familiar metaphors |
| Smart defaults | Pre-select common choices |
| Contextual help | Show info when needed |
| Strategy | Implementation |
|---|---|
| Visual hierarchy | Size, color, position guide attention |
| Consistent patterns | Same action = same interaction |
| Content prioritization | Most important info first |
| Distraction removal | No unnecessary animations/elements |
| Strategy | Implementation |
|---|---|
| Clear feedback | Show results of every action |
| Pattern consistency | Create learnable behaviors |
| Progressive enhancement | Reveal features as expertise grows |
| Knowledge transfer | Design transferable skills |
| Method | Combined Use |
|---|---|
| Cognitive Fluency | Fluency is one way to reduce extraneous load |
| Hick's Law | Fewer choices = lower load |
| Progressive Disclosure | Manage intrinsic load over time |
| Graph Thinking | Map load relationships across system |
| Five Whys | Why are users struggling/abandoning? |
COGNITIVE LOAD CHECKLIST
Intrinsic Load Management:
□ Tasks broken into 3-5 step chunks
□ Progressive disclosure for complexity
□ Different paths for different expertise
□ Builds on familiar concepts
Extraneous Load Elimination:
□ Clear visual hierarchy
□ Consistent interaction patterns
□ No unnecessary decorative elements
□ Logical information architecture
Germane Load Optimization:
□ Clear feedback on all actions
□ Patterns that transfer across features
□ Onboarding builds mental models
□ Advanced features reward learning
Testing:
□ Task completion measured
□ Error rates tracked
□ User confusion points identified
□ Learning curve improving over time