My-opencode-config cognitive-biases

Cognitive Biases - Psychology for Product Design

install
source · Clone the upstream repo
git clone https://github.com/flpbalada/my-opencode-config
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/flpbalada/my-opencode-config "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/cognitive-biases" ~/.claude/skills/flpbalada-my-opencode-config-cognitive-biases && rm -rf "$T"
manifest: skills/cognitive-biases/SKILL.md
source content

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

Core Biases at a Glance

BiasWhat It IsApplication
AnchoringFirst info becomes reference pointShow premium prices first
Loss AversionLosses feel 2x stronger than gainsFrame as "losing" vs "missing"
AvailabilityOverestimate what's easy to recallShow success stories, social proof
ConfirmationSeek info confirming beliefsPersonalize onboarding
Planning FallacyUnderestimate task durationGive realistic time estimates
Framing EffectPresentation changes perceptionUse positive framing
Sunk CostInvest based on past costsHighlight accumulated value
Social ProofLook to others for guidanceShow testimonials, usage stats
ScarcityValue rare things moreUse genuine limited offers

Progressive Disclosure

TopicFileWhen to Use
All 9 core biasescontext/core-biases.mdDeep dive into each bias with examples
Analysis frameworkcontext/analysis-framework.mdSystematic analysis of user decisions
Ethics & examplescontext/examples-ethics.mdReal-world examples and dark patterns

Ethical Framework

Before applying a bias, ask:

1. Is this helping the user? → STOP if NO
2. Would I be comfortable if exposed? → STOP if NO
3. Does this create long-term value? → STOP if NO
4. Would this work on an informed user?
   → YES (persuasion) / NO (manipulation)

Quick Reference

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

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

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

References