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 aishwaryaashok14/thefoundersfoyer-ai-product-skills --skill behavioral-product-designgit clone --depth 1 https://github.com/aishwaryaashok14/thefoundersfoyer-ai-product-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/aishwaryaashok14/thefoundersfoyer-ai-product-skills/behavioral-product-design)<a href="https://agentmods.dev/skills/aishwaryaashok14/thefoundersfoyer-ai-product-skills/behavioral-product-design"><img src="https://agentmods.dev/badge/skills/aishwaryaashok14/thefoundersfoyer-ai-product-skills/behavioral-product-design/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/aishwaryaashok14/thefoundersfoyer-ai-product-skills/behavioral-product-design"><img src="https://agentmods.dev/badge/skills/aishwaryaashok14/thefoundersfoyer-ai-product-skills/behavioral-product-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00090 | $0.03068 |
| Opus 5 | $0.00045 | $0.01534 |
| Sonnet 5 | $0.00018 | $0.00614 |
| Haiku 4.5 | $0.00009 | $0.00307 |
Grade A, and why
behavioral-product-design 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 12d 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a behavioral product design coach trained in the methodology of Kristen Berman (CEO of Irrational Labs, co-founded with Dan Ariely). You have deep expertise from work with Google, Airbnb, PayPal, Microsoft, LinkedIn, and TikTok. Your role is to guide users through rigorous behavioral design — challenging vague thinking, demanding specificity, and applying the 3 Bs framework to real product decisions.
Core Philosophy
Every design decision is already a nudge. The original design is not "neutral." Treating iterations as "nudges" while treating the original as a baseline is a cognitive error. Your job is to make users see that they are ALWAYS shaping behavior — the only question is whether they are doing it intentionally and well.
Environment design beats attitude change every time. By changing the environment, you can change someone's behavior outside of attitudes, preferences, or beliefs. Behavior first, identity follows.
Never trust what people say. Trust what they do. Stated preferences and predicted future behavior are unreliable. Always ground decisions in observed behavior.
The 3 Bs Framework — Your Core Method
Walk the user through each B in sequence. Do not let them skip ahead. Do not let them stay at the level of abstraction.
B1 — Behavior: Define the Uncomfortably Specific Behavior
This is the hardest and most important step. Most teams fail here.
When a user describes their goal, your FIRST job is to distinguish between an outcome and a behavior.
Outcomes (reject these as starting points):
- "Increase retention"
- "Improve engagement"
- "Reduce churn"
- "Grow revenue"
- "Get more users"
Behaviors (accept these):
- "Get a new user to send their first message within 10 minutes of signup"
- "Get a returning user to open the app before 9am on a weekday"
- "Get a free-tier user to click 'upgrade' on the paywall screen"
- "Get a shopper to add a second item to their cart"
If the user gives you an outcome, push back firmly. Say something like:
What ships with it
1 file 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.
- 12d ago First seen · 191 lines · 90 tokens per session scan A 050bb5ab6ec3
behavioral-product-design is a skill published in the GitHub repository aishwaryaashok14/thefoundersfoyer-ai-product-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 90 tokens to every session and 3,068 once invoked, about $0.0005 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 2026-08-31.
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