# Cognitive Biases

> Identify cognitive biases shaping product decisions and user behavior. Use when auditing decision points, conversion flows, pricing, framing, defaults, or research interpretation; avoid when a narrower bias skill such as loss aversion, status quo bias, halo effect, social proof, or Hick's Law directly fits.

- Skill: `flpbalada/cognitive-biases` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add flpbalada/cognitive-biases`
- Raw SKILL.md: https://api.skillmd.com/api/skills/flpbalada/cognitive-biases/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search, Coding & Dev Tools
- Author: flpbalada (https://skillmd.com/u/flpbalada)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/flpbalada/cognitive-biases

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# Cognitive Biases

Use psychology to explain user behavior and improve decisions.
Use ethically.

## Goal

Identify relevant biases.
Use them to reduce confusion and support user goals.
Avoid manipulation.

## Common Biases

- Anchoring: first number or option becomes reference point.
- Loss aversion: losses feel stronger than gains.
- Availability: memorable examples feel more common.
- Confirmation bias: people prefer evidence matching beliefs.
- Planning fallacy: people underestimate time and effort.
- Framing effect: wording changes perception.
- Sunk cost: past investment affects future choices.
- Social proof: people look to others for cues.
- Scarcity: rare options feel more valuable.

## Ethics Rules

- Help users make better decisions.
- Do not hide costs, constraints, or alternatives.
- Do not create false urgency or fake scarcity.
- Do not exploit fear, shame, or vulnerable states.
- Make the design acceptable to an informed user.

## Flow

1. Define user goal and business goal.
2. Map the decision point.
3. Identify likely biases.
4. Separate persuasion from manipulation.
5. Propose design changes.
6. Add measurement and guardrails.

## Questions

- What decision is the user making?
- What information is missing or overemphasized?
- Which bias may explain current behavior?
- Does the intervention help the user?
- Would the user accept it if explained plainly?

## Progressive Disclosure

| Topic | File | When to Use |
|-------|------|-------------|
| All 9 core biases | [context/core-biases.md](context/core-biases.md) | Deep dive into each bias with examples |
| Analysis framework | [context/analysis-framework.md](context/analysis-framework.md) | Systematic analysis of user decisions |
| Ethics & examples | [context/examples-ethics.md](context/examples-ethics.md) | Real-world examples and dark patterns |

## References

- [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/)

## Output

```md
## Cognitive Bias Review
- Context: [flow or decision]
- User goal: [goal]
- Biases involved: [bias and effect]
- Ethical risk: [risk or none]
- Recommendation: [change]
- Measurement: [metric]
```