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- Loss Aversion Psychology
loss-aversion-psychology_skill
136
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill flpbalada/my-opencode-config --skill loss-aversion-psychology- SKILL.md7.2 KB
Overview
This skill teaches how to leverage loss aversion—people feeling losses roughly twice as strongly as equivalent gains—in product design and messaging. It explains where and when loss framing moves behavior, and provides an ethical framework, templates, and implementation guidance. Use it to turn existing user value into protective, motivating messages without resorting to manipulation.
How this skill works
The skill analyzes user journeys to identify what users already own (access, progress, streaks, settings) and reframes options as potential losses rather than only gains. It guides selection of loss vs gain frames by user stage, action reversibility, and ethical impact. Concrete copy patterns, timing recommendations, and an implementation checklist help teams test and ship loss-framed interventions responsibly.
When to use it
- Designing retention, anti-churn, or re-engagement flows
- Crafting pricing, upgrade, or downgrade messaging
- Creating urgency in conversion funnels or time-limited offers
- Building streaks, progress bars, and habit-forming features
- Onboarding where establishing ownership (endowment) increases commitment
Best practices
- Identify genuine user value before framing it as a potential loss
- Be specific and concrete (e.g., number of saved items, days of streak)
- Time messages so they’re relevant—too early is ignored, too late breeds resentment
- Keep loss recoverable: show clear actions to prevent or reverse loss
- Avoid manufactured scarcity, hidden deadlines, or guilt-based triggers
Example use cases
- Trial expiration: show precisely which saved items, projects, or settings will be lost and how to keep them
- Streaks and habits: remind users they’ll “lose X days” of progress rather than only promising future gains
- Pricing upgrade: highlight features the user currently enjoys that will be removed on downgrade
- Onboarding: emphasize the personal data or progress at stake if setup isn’t completed
- Reactivation: tell lapsed users what current benefits they’re missing out on now
FAQ
No. When used transparently to help users avoid real, clear harms or loss of their own value, it’s ethical. It becomes manipulative when scarcity or consequences are fabricated or hidden.
When should I prefer gain framing?
Prefer gain framing for new users, exploratory contexts, low-stakes actions, or when the action builds trust. Use loss framing for existing users with invested value or when preventing real negative outcomes is the goal.