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.
npx skills add deciqAI/knowledge-skills --skill loss-aversion-prospect-theorygit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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.
[](https://agentmods.dev/skills/deciqai/knowledge-skills/loss-aversion-prospect-theory)<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>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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.
- One-line: before calling a risk choice irrational, identify the reference point and check if the decision flips when reframed gain vs. loss.
- Check fit — if the loss is genuinely catastrophic and irreversible, asymmetric aversion is rational; use Kelly/antifragile, not debiasing.
- 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]
- 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]
- Close: restate decision in EV terms and name explicitly how reference-point and probability-weighting influenced it.
[WAIT — do not advance until user responds]
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.
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.
- 9d ago First seen · 115 lines · 118 tokens per session scan A 1e27917c535c
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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