foundations-behavioral-economics

foundations-behavioral-economics is a skill for Codex from vasilyu1983/AI-Agents-public. It costs 48 tokens per session (14,038 once invoked), scanned A, original, MIT.

A set of 16 behavioral-economics concepts about how framing, defaults, choices, habits, and rewards influence human decisions and repeated behavior.

In plain words
What is it for?
Use it for ethical pricing, onboarding, product choices, retention, advertising, business models, and behavior-change design.
Why use it?
It helps explain why people choose one option over another and provides a way to examine whether a design supports users or manipulates them with dark patterns.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: positional $N argument.

Good fit Use it for ethical pricing, onboarding, product choices, retention, advertising, business models, and behavior-change design.

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Install with agentmods
npx agentmods add skills/vasilyu1983/ai-agents-public/foundations-behavioral-economics
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 vasilyu1983/AI-Agents-public --skill foundations-behavioral-economics
Clone the repo
git clone --depth 1 https://github.com/vasilyu1983/AI-Agents-public

Made for: 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 foundations-behavioral-economics

README.md
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Your own site
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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.

agentmods 80×15 button for foundations-behavioral-economics

Your own site · 80×15
<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>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 14,038 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 warn 7 Sept 2026
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.
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 $0.00048 $0.14038
Opus 5 $0.00024 $0.07019
Sonnet 5 $0.00010 $0.02808
Haiku 4.5 $0.00005 $0.01404

Measured 9d ago against content hash c909173e54c6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

frameworks/shared-skills/skills/foundations-behavioral-economics/SKILL.md · 463 lines

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

Read the full file on GitHub · 463 lines

Files

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.

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 · 463 lines · 48 tokens per session scan A c909173e54c6

Subscribe to this mod's changes

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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