Deep-dive usability evaluation of specific user tasks. Simulates novice user cognition step-by-step to identify learnability issues, unclear actions, and points of confusion.
This skill enables AI agents to perform a task-specific usability evaluation using the Cognitive Walkthrough method, a technique that simulates how users (especially novices) think through completing specific tasks in an interface.
Unlike broad heuristic evaluations, Cognitive Walkthrough provides deep analysis of particular user journeys, identifying where users get stuck, confused, or make errors.
Use this skill when you need granular, task-focused insights into learnability and ease of first use.
Combine with "Nielsen Heuristics" for general usability, "Don Norman Principles" for intuitive design, or "WCAG Accessibility" for inclusive access.
Invoke this skill when:
When executing this walkthrough, gather:
Cognitive Walkthrough evaluates four key questions at each step:
Q1: Will users try to achieve the right effect?
Q2: Will users notice that the correct action is available?
Q3: Will users associate the correct action with the effect they're trying to achieve?
Q4: If the correct action is performed, will users see that progress is being made?
Follow these steps systematically:
Identify the task:
Define the user:
Establish starting state:
Break the task into atomic actions (smallest meaningful steps):
Example Task: "Create account and add item to wishlist"
Key principle: Each action should be a single, observable user behavior.
For each action, answer the 4 cognitive questions:
## Action [N]: [Description]
**User's Goal at this step:** [What they're trying to accomplish]
**Current State:** [What they see/where they are]
### Q1: Will users try to achieve the right effect?
- **Analysis**: [Will users know what to do next?]
- **Issues**: [Problems if any]
- **Rating**: ✅ Clear / ⚠️ Unclear / ❌ Confusing
### Q2: Will users notice the correct action is available?
- **Analysis**: [Is the control visible/findable?]
- **Issues**: [Problems if any]
- **Rating**: ✅ Visible / ⚠️ Somewhat hidden / ❌ Hidden
### Q3: Will users associate action with intended effect?
- **Analysis**: [Does the control suggest what it does?]
- **Issues**: [Problems if any]
- **Rating**: ✅ Clear / ⚠️ Ambiguous / ❌ Misleading
### Q4: Will users see progress is being made?
- **Analysis**: [Is there feedback after the action?]
- **Issues**: [Problems if any]
- **Rating**: ✅ Clear feedback / ⚠️ Delayed/weak / ❌ No feedback
### Critical Issues Found:
- [Issue 1]
- [Issue 2]
### Recommendations:
- [Specific improvement 1]
- [Specific improvement 2]
---
After walking through all actions:
Identify failure points:
Categorize issues:
Calculate success likelihood:
Prioritize improvements:
Create comprehensive walkthrough report (see format below).
# Cognitive Walkthrough Report
**Task**: [Task description]
**User Persona**: [User type and characteristics]
**Interface**: [System/app being evaluated]
**Date**: [Date]
**Evaluator**: [AI Agent]
---
## Executive Summary
### Task Success Prediction
**Estimated Success Rate (First Attempt)**: [X]% of target users
### Critical Findings
1. [Most critical issue]
2. [Second critical issue]
3. [Third critical issue]
### Overall Assessment
[2-3 sentence summary of learnability]
---
## User Context
### Target User Profile
- **Experience Level**: [Novice/Intermediate/Expert]
- **Domain Knowledge**: [Description]
- **Technical Proficiency**: [Low/Medium/High]
- **Device/Context**: [Desktop/Mobile, environment]
- **Motivation**: [Why they're doing this]
- **Prior Experience**: [What they already know]
### Task Definition
**Goal**: [What user wants to accomplish]
**Success Criteria**: [How to know they succeeded]
**Starting Point**: [Where task begins]
---
## Step-by-Step Walkthrough
### Action 1: [Navigate to homepage]
**User's Goal**: Find where to start creating an account
**Current State**: User just arrived at homepage
#### Q1: Will users try to achieve the right effect?
- **Analysis**: Users typically look for "Sign Up", "Register", or "Create Account" in header/nav
- **Issues**: None expected - standard mental model
- **Rating**: ✅ Clear
#### Q2: Will users notice the correct action is available?
- **Analysis**: "Sign Up" button is in top-right corner of header (standard location)
- **Issues**: Small text (12px), low contrast (#999 on #FFF = 2.8:1)
- **Rating**: ⚠️ Somewhat hidden
#### Q3: Will users associate action with intended effect?
- **Analysis**: "Sign Up" is standard terminology, clearly indicates account creation
- **Issues**: None
- **Rating**: ✅ Clear
#### Q4: Will users see progress is being made?
- **Analysis**: N/A - no action taken yet (just viewing)
- **Issues**: N/A
- **Rating**: N/A
#### Critical Issues:
- **Low contrast on "Sign Up" button** - WCAG fail, hard to see
- Button is small (24px height) - mobile users may struggle
#### Recommendations:
1. Increase contrast to 4.5:1 minimum (WCAG AA)
2. Increase button size to 44px (touch target guideline)
3. Consider more prominent placement or visual weight
---
[Continue for all actions...]
---
## Failure Points Analysis
### Critical Blockers (Users likely to fail)
**1. Action 7: Create password with complexity requirements**
- **Problem**: Password requirements not shown until after submission fails
- **Impact**: Users guess rules, get frustrated by repeated errors
- **Affected Users**: 70-80% of novices
- **Severity**: Critical
- **Fix Priority**: P0 (Must fix)
- **Recommendation**: Show requirements inline before user types
**2. Action 12: Find "Add to Wishlist" button**
- **Problem**: Icon-only button (heart icon) with no label, not obvious
- **Impact**: Users don't see it or don't understand what it does
- **Affected Users**: 50-60% of first-time users
- **Severity**: High
- **Fix Priority**: P1 (Should fix)
- **Recommendation**: Add text label "Add to Wishlist" next to icon
### Major Friction Points
[Continue...]
### Minor Issues
[Continue...]
---
## Success Probability by User Type
| User Type | Estimated Success Rate | Time to Complete | Confidence |
|-----------|------------------------|------------------|------------|
| **Novice** | 45% | 8-12 minutes | Low frustration tolerance |
| **Intermediate** | 75% | 4-6 minutes | Moderate confidence |
| **Expert** | 95% | 2-3 minutes | High efficiency |
**Target**: Novices should have ≥80% success rate with ≤5 minutes time.
**Gap**: Current design falls short for novices by 35 percentage points.
---
## Cognitive Load Assessment
### Memory Burden
- **Items to remember**: [List what users must recall]
- **Rating**: Low / Medium / High
- **Issue**: [If high, explain why]
### Decision Points
- **Choices users make**: [Number and complexity]
- **Rating**: Low / Medium / High
- **Issue**: [Unnecessary decisions increase cognitive load]
### Error Recovery
- **How easy to fix mistakes**: [Analysis]
- **Rating**: Easy / Moderate / Difficult
- **Issue**: [Problems with undo/back/reset]
---
## Prioritized Recommendations
### Phase 1: Critical Fixes (1-2 weeks)
**1. Show password requirements inline (Action 7)**
- **Why**: Eliminates #1 failure point
- **Impact**: +25% success rate for novices
- **Effort**: Low (4 hours)
**2. Add text label to wishlist button (Action 12)**
- **Why**: Makes feature discoverable
- **Impact**: +15% task completion
- **Effort**: Low (2 hours)
**3. Increase "Sign Up" button contrast (Action 1)**
- **Why**: Accessibility + discoverability
- **Impact**: +10% users find starting point
- **Effort**: Low (1 hour)
**Total Phase 1 Impact**: +50% novice success rate (45% → 67.5%)
---
### Phase 2: Major Improvements (1-2 months)
[Continue with medium priority items...]
---
### Phase 3: Polish (3+ months)
[Continue with nice-to-have improvements...]
---
## Alternative Design Suggestions
Based on walkthrough findings, consider these alternative approaches:
### Alternative 1: Progressive Disclosure for Signup
**Current**: All fields shown at once
**Proposed**: Step-by-step (email → password → confirm)
**Pros**: Reduces cognitive load, clearer feedback per step
**Cons**: More clicks, may feel slower
**Recommendation**: A/B test with target users
### Alternative 2: Social Sign-Up
**Current**: Email/password only
**Proposed**: Add "Sign up with Google/Apple"
**Pros**: Faster, no password to remember
**Cons**: Privacy concerns, dependency on third-party
**Recommendation**: Offer as option alongside email signup
[Continue with other alternatives...]
---
## Comparison to Best Practices
| Practice | Current Implementation | Recommendation |
|----------|------------------------|----------------|
| Password requirements visibility | Hidden until error | Show inline before typing |
| Button sizing (mobile) | 24px | 44px minimum |
| Color contrast | 2.8:1 (WCAG fail) | 4.5:1 (WCAG AA) |
| Error messages | Generic | Specific and actionable |
| Confirmation feedback | Weak | Clear success messages |
---
## Next Steps
1. **Prioritize fixes**: Start with Phase 1 critical items
2. **Prototype improvements**: Create clickable mockups with changes
3. **User testing**: Validate findings with 5-8 target users
4. **Iterate**: Run another cognitive walkthrough after changes
5. **Monitor metrics**: Track task completion rates, time-on-task, error rates
---
## Methodology Notes
- **Method**: Cognitive Walkthrough (Wharton et al., 1994)
- **Evaluator**: AI agent simulating UX expert
- **Perspective**: Novice user (first-time, no training)
- **Limitations**:
- Based on interface analysis, not actual user behavior
- Success rates are estimates, not measured data
- Should be validated with real user testing
---
## References
- Wharton, C., Rieman, J., Lewis, C., & Polson, P. (1994). "The Cognitive Walkthrough Method"
- Nielsen, J. (1994). "Heuristic Evaluation"
- Spencer, R. (2000). "The Streamlined Cognitive Walkthrough Method"
---
**Version**: 1.0
**Date**: [Date]
Discoverability Problems:
Unclear Affordances:
Feedback Failures:
Mental Model Mismatches:
Cognitive Load:
Measure walkthrough effectiveness:
Before Walkthrough:
After Implementing Fixes:
Use cognitive walkthrough when:
Complement with:
1.0 - Initial release
Remember: Cognitive Walkthrough is a predictive method. While it's highly effective at identifying learnability issues, always validate findings with real users through usability testing.