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 prepforeverything/prepkit-product --skill product-engagement-designgit clone --depth 1 https://github.com/prepforeverything/prepkit-productWrote 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/prepforeverything/prepkit-product/product-engagement-design)<a href="https://agentmods.dev/skills/prepforeverything/prepkit-product/product-engagement-design"><img src="https://agentmods.dev/badge/skills/prepforeverything/prepkit-product/product-engagement-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/prepforeverything/prepkit-product/product-engagement-design"><img src="https://agentmods.dev/badge/skills/prepforeverything/prepkit-product/product-engagement-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 | $0.00060 | $0.02003 |
| Opus 5 | $0.00030 | $0.01001 |
| Sonnet 5 | $0.00012 | $0.00401 |
| Haiku 4.5 | $0.00006 | $0.00200 |
Grade A, and why
product-engagement-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 10d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Engagement Design
When To Use
- A product concept depends on repeated behavior, retention loops, or activation momentum
- The team is proposing streaks, badges, levels, rewards, social proof, nudges, or other gamified mechanics
- A PRD includes behavior-shaping mechanics that need ethical guardrails and counter-metrics
- The question is not "should we build this feature?" but "how should the behavior loop work without harming trust?"
- The problem involves a freemium model, PLG conversion funnel, activation mechanics, onboarding-to-value flow, or in-context upsell design — load
references/plg-flow-design.md
Key Concepts
- Behavior loop: trigger -> action -> reward -> investment
- Intrinsic vs. extrinsic motivation: amplify existing user value before adding rewards
- Progression design: levels, badges, streaks, and milestones only help when they reinforce real product progress
- Counter-metrics: track harm signals alongside engagement lift
- Ethical guardrails: avoid coercion, fake urgency, exploitative loss aversion, and hollow achievements
- Show, Don't Tell: design the user path so value is experienced before it is pitched — apply to activation flows, freemium onboarding, and in-context upsell placement. The free experience must be genuinely valuable, not a crippled version of the paid product. See
references/plg-flow-design.md. - Deceptive design taxonomy: formal categories of manipulative interface patterns — Trick Wording (misleading copy that hides what an action does), Sneaking (practices hidden until after a commitment is made), Obstruction (making desired user actions like cancelling or unsubscribing deliberately difficult); these extend beyond gamification mechanics to interaction-level manipulation present in any product interface — WHY: without named categories, "it feels manipulative" cannot be acted on; WHAT/HOW: audit each screen against these three categories and ask whether a user could accurately describe what they agreed to before they acted
- Regulatory exposure: deceptive patterns increasingly violate GDPR consent and transparency rules (Articles 6–7, Planet49 ruling), EU DSA Article 25 (explicit deceptive design prohibition for all online platforms), and FTC Act Section 5 enforcement — WHY: ethical failures in engagement design carry legal risk, not only reputational risk; products launched into regulated markets without a deceptive-design review carry compliance exposure; WHAT/HOW: before launch, run the self-audit checklist in
references/deceptive-design-taxonomy.mdagainst every consent flow, cancellation path, and social proof claim
What ships with it
7 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.
- references/behavioral-design-patterns.md 2.5 KB
- references/deceptive-design-taxonomy.md 11 KB
- references/gamification-antipatterns.md 2.3 KB
- references/hook-model-canvas.md 2.0 KB
- references/octalysis-core-drives.md 2.1 KB
- references/plg-flow-design.md 5.3 KB
- references/product-quality-gates.md 1.5 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.
- 10d ago First seen · 99 lines · 60 tokens per session scan A 35dbf99b30ce
product-engagement-design is a skill published in the GitHub repository prepforeverything/prepkit-product (2 stars, last pushed 5mo ago), licensed MIT. It adds 60 tokens to every session and 2,003 once invoked, about $0.0003 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.
Other skills, from other repositories
webgl-holographic-foil
A self-contained WebGL2 hero: thin-film interference over a crushed-foil surface whose palette shifts with the viewing angle; move the cursor to tilt the film.
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
html-ppt-taste-brutalist
16:9 HTML deck in tactical-telemetry / CRT-terminal taste. Deactivated-CRT charcoal slides, white-phosphor monospace, hazard-red accent, scanline overlay, ASCII syntax, density over decoration. Distilled from Leonxlnx/taste-skill brutalist-skill (Tactical Telemetry mode).
visual-ralph
Visual Ralph orchestration for frontend UI from generated references, static references, or live URL targets, using $ralph with built-in visual verdict and pixel-diff evidence until the implementation matches and leaves a reproducible design system.
accessibility
Consolidated accessibility skill entrypoint for WCAG 2.2, ARIA Authoring Practices, cognitive accessibility, Section 508, EN 301 549, design intent verification, and the Accessibility Planner workflow.
make-resume
A Chinese-language tool for creating editable HTML resumes that can be changed in a browser and printed to PDF. It uses available resume templates when they are installed and otherwise provides a simpler fallback.