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cognitive-biases skill

/skills/cognitive-biases

This skill helps you apply cognitive bias insights to product design and decision-making to ethically optimize onboarding, pricing, and conversions.

npx playbooks add skill flpbalada/my-opencode-config --skill cognitive-biases

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---
name: cognitive-biases
description:
  Apply cognitive bias knowledge to product design and decision-making. Use when
  designing user experiences, analyzing user behavior, improving conversions, or
  ensuring ethical design practices.
---

# Cognitive Biases - Psychology for Product Design

Understanding psychological patterns that influence human decision-making, first
systematically studied by Kahneman and Tversky. Essential for creating user
experiences that work with human psychology.

## When to Use This Skill

- Designing user onboarding flows
- Improving conversion rates ethically
- Analyzing why users behave unexpectedly
- Reviewing designs for dark patterns
- Planning pricing and positioning strategies
- Understanding decision-making in user research

## Foundation: Dual-Process Theory

```
┌─────────────────────────────────────────────────────────────────┐
│                     HUMAN DECISION-MAKING                        │
├────────────────────────────┬────────────────────────────────────┤
│       SYSTEM 1 (95%)       │          SYSTEM 2 (5%)             │
├────────────────────────────┼────────────────────────────────────┤
│ Fast                       │ Slow                               │
│ Automatic                  │ Deliberate                         │
│ Intuitive                  │ Analytical                         │
│ Unconscious                │ Conscious                          │
│ Associative                │ Logical                            │
│ Low effort                 │ High effort                        │
│ Emotional                  │ Rational                           │
├────────────────────────────┼────────────────────────────────────┤
│ "Feels right"              │ "Let me think about this"          │
└────────────────────────────┴────────────────────────────────────┘

Most user interactions happen through System 1.
Design for intuition, not just logic.
```

## Core Cognitive Biases

### 1. Anchoring Bias

**What it is:** The brain latches onto the first piece of information as a
reference point for all subsequent decisions.

```
Pricing Example:

❌ Without anchor:
   "Pro plan: $49/month"
   User thinks: "Is that expensive?"

✅ With anchor:
   "Enterprise: $199/month" (shown first)
   "Pro plan: $49/month"
   User thinks: "That's a great deal!"
```

**Product applications:**

- Show premium/enterprise tier first in pricing tables
- Display original price crossed out before sale price
- Set high initial expectations, then exceed them

### 2. Loss Aversion

**What it is:** Humans feel losses 2x more intensely than equivalent gains.

```
Framing comparison:

Gain frame (weaker):    "Save $100 with annual billing"
Loss frame (stronger):  "You're losing $100 by paying monthly"

Progress frame:
Weaker:  "Complete setup to unlock features"
Stronger: "Don't lose your progress - 80% complete"
```

**Product applications:**

- Free trials that create ownership feeling
- Progress indicators showing what users might lose
- "Save" vs "Spend" framing in messaging

### 3. Availability Bias

**What it is:** We overestimate the likelihood of events we can easily recall.

```
Making success feel common:

"Join 50,000+ developers"        → Success is common
"Featured in TechCrunch"         → Credibility by association
"Sarah from NYC just signed up"  → Real-time social proof
"5 people viewing this now"      → Popularity signal
```

**Product applications:**

- Social proof and testimonials prominently displayed
- Recent activity feeds that influence behavior
- Success stories that make outcomes feel achievable

### 4. Confirmation Bias

**What it is:** We seek information confirming existing beliefs and ignore
contradictory evidence.

```
Personalization flow:

User selects: "I'm a developer"
    ↓
Show: Developer-focused features
Hide: Marketing automation features
    ↓
User thinks: "This product gets me"
```

**Product applications:**

- Personalized onboarding based on user type
- Customizable dashboards reflecting preferences
- Content recommendations aligned with interests

### 5. Planning Fallacy

**What it is:** We consistently underestimate how long tasks will take.

```
Setting realistic expectations:

❌ "Quick setup"           → User expects 1 min, takes 10
✅ "10-minute setup"       → User expects 10, finishes in 8

Progress that manages expectations:
┌────────────────────────────────────┐
│ Step 2 of 5 · About 4 minutes left │
│ ████████░░░░░░░░░░░░░░ 40%         │
└────────────────────────────────────┘
```

**Product applications:**

- Realistic time estimates for user tasks
- Progress indicators with time remaining
- Break complex tasks into visible steps

### 6. Framing Effect

**What it is:** How information is presented changes decisions, even when
underlying data is identical.

```
Same data, different perception:

Negative frame: "10% of projects fail"
Positive frame: "90% success rate"

Feature absence: "No hidden fees"
Feature presence: "Transparent pricing"

Risk frame: "You might lose data"
Safety frame: "Your data is protected"
```

**Product applications:**

- Positive framing in UI copy and messaging
- Feature benefits vs feature absence language
- Success-oriented progress messaging

### 7. Sunk Cost Fallacy

**What it is:** We continue investing because of past investments, not future
value.

```
Leveraging investment:

"You've been with us for 2 years"
"Don't lose your 500 saved items"
"Your profile is 80% complete"
"3,000 connections would miss you"
```

**Product applications:**

- Progress saving and restoration features
- Investment tracking showing accumulated value
- Gentle reminders of past engagement

### 8. Social Proof

**What it is:** We look to others' behavior to determine correct actions.

```
Types of social proof:

Expert:     "Recommended by security researchers"
Celebrity:  "Used by Elon Musk"
User:       "500,000+ teams trust us"
Wisdom:     "Most popular plan"
Peers:      "Teams like yours use Premium"
```

**Product applications:**

- Customer logos and testimonials
- Usage statistics and popularity indicators
- "Most popular" badges on pricing plans

### 9. Scarcity

**What it is:** We value things more when they're rare or diminishing.

```
Scarcity signals:

Time:      "Sale ends in 2:34:12"
Quantity:  "Only 3 seats left"
Access:    "Invite-only beta"
Exclusivity: "Limited to 100 companies"

⚠️  Only use with REAL scarcity
```

**Product applications:**

- Limited-time offers (when genuinely limited)
- Stock/availability indicators
- Waitlist and invite-only access

## Bias Analysis Framework

### Step 1: Identify Decision Points

Map where users make decisions:

```
User Journey Decision Points:

Landing Page
├── Stay or bounce?           [Availability, Social Proof]
├── Which CTA to click?       [Framing, Anchoring]
│
Signup
├── Email or social login?    [Convenience, Trust]
├── Share optional data?      [Reciprocity]
│
Pricing
├── Which plan?               [Anchoring, Decoy]
├── Monthly or annual?        [Loss Aversion]
│
Onboarding
├── Complete or skip?         [Commitment, Sunk Cost]
├── Invite teammates?         [Social Proof]
│
Retention
├── Continue or churn?        [Sunk Cost, Loss Aversion]
└── Upgrade or stay?          [Anchoring, Social Proof]
```

### Step 2: Map Current Bias Usage

Audit existing design:

| Screen    | Decision       | Bias Used     | Ethical? | Effective? |
| --------- | -------------- | ------------- | -------- | ---------- |
| Pricing   | Plan selection | Anchoring     | ✅       | ✅         |
| Checkout  | Add extras     | Scarcity      | ⚠️ Fake  | ❌         |
| Trial end | Convert        | Loss aversion | ✅       | ✅         |

### Step 3: Design Improvements

For each decision point:

```
Decision: Plan selection

Current state:
- Plans listed low to high
- No default highlighted
- Equal visual weight

Improved design:
- Anchor with Enterprise first (Anchoring)
- "Most popular" badge on target plan (Social Proof)
- "Recommended for you" personalization (Confirmation)
- Annual savings calculated (Loss Aversion)
```

## Output Template

After completing analysis, document as:

```markdown
## Cognitive Bias Analysis

**Product/Feature:** [Name]

**Analysis Date:** [Date]

### Decision Point Audit

| Decision Point | Current Biases | Ethical Assessment | Recommendations |
| -------------- | -------------- | ------------------ | --------------- |
| [Point 1]      | [Biases used]  | [✅/⚠️/❌]         | [Changes]       |
| [Point 2]      | [Biases used]  | [✅/⚠️/❌]         | [Changes]       |

### Recommended Improvements

#### High Priority

- [Improvement 1]: Apply [bias] at [location] to [effect]
- [Improvement 2]: Remove [dark pattern] from [location]

#### Medium Priority

- [Improvement 3]
- [Improvement 4]

### Ethical Checklist

- [ ] All scarcity claims are factual
- [ ] Users can easily reverse decisions
- [ ] No exploitation of vulnerable states
- [ ] Transparent about pricing and terms
- [ ] Personalization is controllable

### Success Metrics

| Metric            | Current | Target | Measurement   |
| ----------------- | ------- | ------ | ------------- |
| Conversion rate   | X%      | Y%     | Analytics     |
| User satisfaction | X       | Y      | Survey        |
| Regret rate       | X%      | <Y%    | Cancellations |
```

## Ethical Guidelines

### ✅ Do: Enhance Experience

```
Ethical bias application:

Reducing cognitive load:
├── Smart defaults (don't make users think)
├── Progressive disclosure (show what's relevant)
└── Clear visual hierarchy (guide attention)

Building trust:
├── Real testimonials with names/photos
├── Honest scarcity (actual inventory)
└── Transparent pricing (no surprises)

Helping decisions:
├── Comparison tables (reduce effort)
├── Recommendations (based on real fit)
└── Clear CTAs (obvious next steps)
```

### ❌ Don't: Exploit Users

```
Dark patterns to avoid:

Fake urgency:
├── "Only 2 left!" (when unlimited)
├── "Sale ends soon!" (perpetual sale)
└── Countdown timers that reset

Hidden information:
├── Fees revealed at checkout
├── Auto-renewal buried in terms
└── Difficult cancellation flows

Manipulation:
├── Guilt-tripping copy
├── Confirm-shaming ("No, I don't want to save money")
└── Trick questions in opt-outs
```

### Ethical Decision Framework

```
Before applying a bias, ask:

1. Is this helping the user?
   YES → Continue
   NO  → Stop

2. Would I be comfortable if this was exposed?
   YES → Continue
   NO  → Stop

3. Does this create long-term value?
   YES → Continue
   NO  → Stop

4. Would this work on an informed user?
   YES → Continue (persuasion)
   NO  → Stop (manipulation)
```

## Real-World Examples

### Amazon: Ethical Anchoring

```
Product page:

List Price:    $79.99  ──→ Anchor (if real MSRP)
Price:         $49.99
You Save:      $30.00 (38%)

✅ Ethical if list price is genuine
❌ Unethical if inflated for appearance
```

### Spotify: Positive Framing

```
Subscription conversion:

"Get 3 months free"
    vs
"Pay for 9 months, get 12"

Same value, different perception.
Ethical because both options are clearly available.
```

### Duolingo: Commitment + Loss Aversion

```
Streak system:

"🔥 15 day streak!"
"Don't break your streak - practice now"

✅ Ethical: Creates positive habit
⚠️ Watch for: Anxiety-inducing pressure
```

## Integration with Other Methods

| Method              | Combined Use                            |
| ------------------- | --------------------------------------- |
| **Five Whys**       | Why do users behave unexpectedly?       |
| **Graph Thinking**  | Map bias influences across user journey |
| **Business Canvas** | Bias impact on value proposition        |
| **Jobs-to-be-Done** | Align bias use with user goals          |
| **A/B Testing**     | Validate bias effectiveness ethically   |

## Quick Reference

```
BIAS CHEAT SHEET

Acquisition:
├── Social Proof → "Join 50,000+ users"
├── Anchoring → Show premium first
└── Scarcity → "Limited beta access"

Activation:
├── Commitment → Small first steps
├── Planning Fallacy → Realistic time estimates
└── Loss Aversion → Show progress at risk

Retention:
├── Sunk Cost → "Your history, connections"
├── Confirmation → Personalized experience
└── Social Proof → "Your team uses this"

Revenue:
├── Anchoring → Price comparison
├── Framing → Annual savings highlighted
└── Loss Aversion → "You're losing $X/month"

Referral:
├── Social Proof → "X friends joined"
├── Reciprocity → Give before asking
└── Scarcity → "Exclusive invite codes"
```

## Resources

- [Thinking, Fast and Slow - Daniel Kahneman](https://www.goodreads.com/book/show/11468377-thinking-fast-and-slow)
- [Predictably Irrational - Dan Ariely](https://danariely.com/books/predictably-irrational/)
- [Hooked - Nir Eyal](https://www.nirandfar.com/hooked/)
- [Dark Patterns Hall of Shame](https://darkpatterns.org/)
- [Growth.Design Psychology Studies](https://growth.design/psychology)

Overview

This skill applies cognitive bias knowledge to product design and decision-making to create more effective, human-centered experiences. It helps teams recognize where biases influence user behavior and converts that insight into practical design changes. The goal is to improve conversions, reduce friction, and avoid manipulative patterns while protecting trust and ethics.

How this skill works

The skill inspects user journeys and decision points to identify which cognitive biases are operating (anchoring, loss aversion, social proof, scarcity, etc.). It maps current bias usage, evaluates ethical risk, and produces prioritized recommendations—UI copy, layout, timing, and measurement changes—to nudge behavior responsibly. Outputs include decision-point audits, recommended experiments, and an ethical checklist.

When to use it

  • Designing onboarding flows or progressive disclosure to reduce dropoff
  • Optimizing pricing pages and plan choice to increase conversions ethically
  • Auditing product flows for dark patterns and ethical risks
  • Interpreting unexpected user behavior from analytics or research
  • Planning messaging and positioning for better decision framing

Best practices

  • Design for System 1: reduce cognitive load with clear defaults and hierarchy
  • Use anchors, framing, and social proof only when factual and transparent
  • Prefer helping decisions over exploiting vulnerabilities; run ethical checks
  • Provide undo paths and clear information to avoid regret and churn
  • Validate bias-driven changes with A/B tests and user feedback

Example use cases

  • Revamp pricing table: add a genuine premium anchor and highlight annual savings
  • Onboarding redesign: break setup into timed steps and show realistic durations
  • Checkout audit: remove fake scarcity labels and replace with real-stock indicators
  • Retention campaign: show accumulated value (sunk cost) and safe upgrade paths
  • Landing page test: add recent-signup social proof and measure lift in signups

FAQ

Will applying biases make the product manipulative?

Not if you follow ethical guidelines: be transparent, factual, allow reversal, and prioritize user benefit. Use biases to reduce friction and help decisions, not to deceive.

How do I measure whether a bias-based change helped?

Define clear success metrics (conversion, satisfaction, regret/churn) and validate with A/B tests, qualitative feedback, and retention analysis.