# Loss Aversion Psychology

> Use loss-aversion analysis when perceived losses outweigh equivalent gains. Use for pricing, trials, cancellations, migration risk, retention, warnings, plan limits, or messaging about what users may lose; avoid fake fear tactics and prefer status-quo-bias skill for resistance to change itself.

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

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# Loss Aversion Psychology

People feel losses more strongly than similar gains.
Use only for real user value and real risk.

## Goal

Show what user may lose.
Motivate useful action without manipulation.

## Rules

- Use loss framing only for real losses.
- Be specific: items, progress, access, time, or money.
- Give clear action to prevent loss.
- Do not fake urgency or scarcity.
- Do not hide options.
- Prefer gain framing for new users and exploration.

## Useful Frames

- Access: "You will lose access to [feature]."
- Progress: "Your [streak/progress] will reset."
- Work: "[count] saved items will be unavailable."
- Money: "Unused credits expire on [date]."

## Flow

1. Identify what user already has.
2. Confirm potential loss is real.
3. Confirm action benefits user.
4. Write concrete loss frame.
5. Add recovery or prevention action.
6. Check ethics.
7. Test against gain framing.

## Ethical Check

Proceed only when:

- Loss is real.
- Deadline is real.
- User understands tradeoff.
- User has control.
- Message would feel fair if psychology were explained.

## Resources

- [Thinking, Fast and Slow - Daniel Kahneman](https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555)
- [Predictably Irrational - Dan Ariely](https://www.amazon.com/Predictably-Irrational-Hidden-Forces-Decisions/dp/0061353248)
- [Prospect Theory Original Paper - Kahneman & Tversky](https://www.jstor.org/stable/1914185)

## Output

```md
## Loss Aversion Review

Feature/message: [name]
User already has:
- [asset / progress / access]

Potential loss:
- [real loss]

Current frame:
- [copy]

Proposed frame:
- [copy]

Action offered:
- [what user can do]

Ethical check:
- Real loss: [yes/no]
- Clear control: [yes/no]
- Honest timing: [yes/no]

Test:
- Compare against [gain frame]
- Metric: [conversion / retention / satisfaction]
```