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 vasilyu1983/AI-Agents-public --skill foundations-behavioral-economicsgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/foundations-behavioral-economics)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-behavioral-economics"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-behavioral-economics/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/vasilyu1983/ai-agents-public/foundations-behavioral-economics"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-behavioral-economics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 173 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00048 | $0.14038 |
| Opus 5 | $0.00024 | $0.07019 |
| Sonnet 5 | $0.00010 | $0.02808 |
| Haiku 4.5 | $0.00005 | $0.01404 |
Grade A, and why
foundations-behavioral-economics 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 — 463 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Behavioral Economics Foundations
16 canonical behavioral-economics and behavior-design primitives for product, pricing, choice design, and retention. Each primitive is domain-agnostic and ethically bounded. Primitives 1–11 cover decision-time effects (framing, anchoring, choice). Primitives 12–16 cover repetition-time effects (habit formation, reinforcement, working memory, contextual retrieval, planned action) — the canonical mechanisms behind retention, behavior change, and the "neuroscience of product" claims commonly made without mechanism. Consumer applied recipes (CRO, business models, content strategy, product management, paid advertising) are the downstream layer — these primitives are the upstream canon.
Ethical obligation: every primitive in this skill is a tool for understanding and influencing human decision-making. Each has a "Misuse boundary" subsection. Read it before applying any technique. The test from Thaler and Sunstein: "Would you be embarrassed if the technique appeared on the front page of a newspaper?" If yes, it is a dark pattern, not a nudge.
When to Apply
Apply behavioral-economics when:
- User-facing decision surface — pricing page, onboarding default, churn flow, retention nudge
- Habit-formation or cue-preservation in redesigns
- Loss-aversion / framing matters and downside is concrete
- Choice architecture — defaults, decoys, ordering, anchoring
- Conversion or activation experiment design where biases are exploitable ethically
Skip and use simpler alternatives when:
- Decision is between two AI systems or backend strategies (no human in the loop) — use foundations-decision-theory
- Causal "did the nudge work?" question — use foundations-causal-inference to measure
- Strategic multi-actor pricing — use foundations-game-theory (Bertrand, Vickrey)
- The proposed pattern requires deceiving the user about real value — fails the ethical gate; redesign, don't nudge
- Audience or market context is unknown — biases are not universal; lift bands won't generalise
- Lift required > 30% — behavioral nudges rarely deliver that; the underlying offer/value-prop is the problem, not framing
What ships with it
24 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.
- agents/openai.yaml 355 B
- assets/templates/behavioral-economics/01-prospect-theory.md 6.2 KB
- assets/templates/behavioral-economics/02-loss-aversion.md 5.1 KB
- assets/templates/behavioral-economics/03-anchoring.md 4.9 KB
- assets/templates/behavioral-economics/04-defaults.md 5.3 KB
- assets/templates/behavioral-economics/05-social-proof.md 4.9 KB
- assets/templates/behavioral-economics/06-scarcity.md 5.6 KB
- assets/templates/behavioral-economics/07-hyperbolic-discounting.md 6.3 KB
- assets/templates/behavioral-economics/08-mental-accounting.md 5.9 KB
- assets/templates/behavioral-economics/09-choice-architecture.md 7.1 KB
- assets/templates/behavioral-economics/10-dual-system.md 6.1 KB
- assets/templates/behavioral-economics/11-decoy-effect-asymmetric-dominance.md 6.6 KB
- assets/templates/behavioral-economics/12-habit-loop.md 7.4 KB
- assets/templates/behavioral-economics/13-reinforcement-schedules.md 8.2 KB
- assets/templates/behavioral-economics/14-cognitive-load-working-memory.md 8.0 KB
- assets/templates/behavioral-economics/15-context-dependent-retrieval.md 7.8 KB
- assets/templates/behavioral-economics/16-implementation-intentions.md 7.7 KB
- assets/templates/behavioral-economics/README.md 12 KB
- data/sources.json 42 KB
- learnings.consolidated.md 608 B
- learnings.md 1.2 KB
- references/formal-theory-map.md 2.5 KB
- references/patterns-scenarios-traps.md 8.0 KB
- references/primitives-overview.md 11 KB
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 · 463 lines · 48 tokens per session scan A c909173e54c6
foundations-behavioral-economics is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 48 tokens to every session and 14,038 once invoked, about $0.0002 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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