loss-aversion-prospect-theory

loss-aversion-prospect-theory is a skill for Claude Code, Codex from deciqAI/knowledge-skills. It costs 118 tokens per session (1,903 once invoked), scanned A, original, MIT.

An explanation of how people judge gains and losses relative to a personal reference point rather than by their absolute value. It shows why an equivalent loss often feels worse than a gain feels good and why uncertainty can be misjudged.

In plain words
What is it for?
It helps analyse negotiations, pricing and incentives, product adoption, insurance choices, and decisions involving uncertain gains or losses.
Why use it?
It helps explain resistance to changes, deals, prices, or bets that appear reasonable when viewed only through expected outcomes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps analyse negotiations, pricing and incentives, product adoption, insurance choices, and decisions involving uncertain gains or losses.

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Install with agentmods
npx agentmods add skills/deciqai/knowledge-skills/loss-aversion-prospect-theory
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add deciqAI/knowledge-skills --skill loss-aversion-prospect-theory
Clone the repo
git clone --depth 1 https://github.com/deciqAI/knowledge-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for loss-aversion-prospect-theory

README.md
[![agentmods](https://agentmods.dev/badge/skills/deciqai/knowledge-skills/loss-aversion-prospect-theory/github.svg)](https://agentmods.dev/skills/deciqai/knowledge-skills/loss-aversion-prospect-theory)
Your own site
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/loss-aversion-prospect-theory"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/loss-aversion-prospect-theory/github.svg" alt="Measured on agentmods" height="20"></a>

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Your own site · 80×15
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Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,903 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00118 $0.01903
Opus 5 $0.00059 $0.00951
Sonnet 5 $0.00024 $0.00381
Haiku 4.5 $0.00012 $0.00190

Measured 9d ago against content hash 1e27917c535c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

loss-aversion-prospect-theory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

loss-aversion-prospect-theory/SKILL.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Loss Aversion and Prospect Theory

Overview

People evaluate outcomes relative to a reference point (not absolute wealth), weight losses ~2.25x as heavily as equivalent gains, are risk-averse in gain frames and risk-seeking in loss frames, and distort probabilities (overweighting small, underweighting large). The same physical outcome feels different depending on framing — this skill diagnoses and corrects that asymmetry.

Composes with sunk-cost-fallacy, framing-effect, expected-value-and-kelly, anchoring, pricing-strategy.

When to Use

  • A decision involves uncertainty and the chooser is visibly averse to a "loss" framing
  • People are refusing positive-EV bets because the downside feels disproportionately bad
  • Negotiations are stuck because concessions feel like losses from an anchored reference point
  • A product launch, pricing, or incentive is producing unexpected adoption patterns
  • Small-probability events are being over- or under-insured against
  • An investor is holding a losing AI / Nvidia / semiconductor position waiting to "get back to breakeven," or is reacting to an AI-capex, AI-valuation, or AI-adoption drawdown (e.g. the DeepSeek shock) rather than re-deriving forward EV
  • Someone says "loss aversion," "prospect theory," "reference point," "endowment effect," "status quo bias," "disposition effect"

Not when: the asymmetric weighting is rational (genuinely catastrophic stakes); the reference point is legitimate; the decision is small and one-shot.

Coaching Novices (Adaptive Front Door)

  • Engine mode: user has a concrete case → run The Process directly.
  • Coach mode: user is unfamiliar → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

  1. One-line: before calling a risk choice irrational, identify the reference point and check if the decision flips when reframed gain vs. loss.
  2. Check fit — if the loss is genuinely catastrophic and irreversible, asymmetric aversion is rational; use Kelly/antifragile, not debiasing.
  3. Elicit the specific decision: what's being chosen, and what reference point makes one option feel like a "loss"?

[WAIT — do not advance until user responds]

  1. Work through EV for each option; shift the reference point; test gain vs. loss reframing; flag over/underweighted probabilities.

[WAIT — do not advance until user responds]

  1. Close: restate decision in EV terms and name explicitly how reference-point and probability-weighting influenced it.

[WAIT — do not advance until user responds]

Read the full file on GitHub · 115 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 115 lines · 118 tokens per session scan A 1e27917c535c

Subscribe to this mod's changes

loss-aversion-prospect-theory is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 118 tokens to every session and 1,903 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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