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 varunk130/ai-ux-skill-library --skill ai-personalization-ethicsgit clone --depth 1 https://github.com/varunk130/ai-ux-skill-libraryWrote 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/varunk130/ai-ux-skill-library/ai-personalization-ethics)<a href="https://agentmods.dev/skills/varunk130/ai-ux-skill-library/ai-personalization-ethics"><img src="https://agentmods.dev/badge/skills/varunk130/ai-ux-skill-library/ai-personalization-ethics/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/varunk130/ai-ux-skill-library/ai-personalization-ethics"><img src="https://agentmods.dev/badge/skills/varunk130/ai-ux-skill-library/ai-personalization-ethics.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 | $0.01742 |
| Opus 5 | $0.00030 | $0.00871 |
| Sonnet 5 | $0.00012 | $0.00348 |
| Haiku 4.5 | $0.00006 | $0.00174 |
Grade A, and why
ai-personalization-ethics 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Personalization & Ethics
Design adaptive interfaces that learn from users and improve over time - without crossing into surveillance, manipulation, or exclusion. The ADAPT framework ensures personalization serves the user's interests, not just engagement metrics.
Core Principle
Personalization is not a feature - it is a power dynamic. The system knows things about the user that the user may not know about themselves. With that knowledge comes responsibility: personalization must be transparent, controllable, and in service of the user's actual goals, not the platform's engagement targets.
The ADAPT Framework
| Letter | Principle | Design Question |
|---|---|---|
| A | Agency Preserved | Can the user see, understand, and override every personalization decision? |
| D | Data Minimized | Are you collecting only what's necessary, and being transparent about it? |
| A | Alternatives Accessible | Can the user easily access non-personalized or differently-personalized views? |
| P | Patterns Not Profiles | Are you personalizing based on behavior patterns, not invasive profiling? |
| T | Tested for Fairness | Have you verified that personalization doesn't discriminate across user groups? |
The Personalization Ladder
Not all personalization is created equal. Higher rungs are more valuable but more ethically complex.
| Rung | Personalization Type | Data Needed | Value to User | Ethical Risk |
|---|---|---|---|---|
| 1 | Segment-based | Demographics, role, industry | Low-medium (generic) | Low - broad groupings |
| 2 | Preference-based | Explicit user settings | Medium (user-controlled) | Very low - user chose this |
| 3 | Behavior-based | Usage patterns, interaction history | High (relevant) | Medium - user may not realize they're being tracked |
| 4 | Predictive | ML models inferring future needs | Very high (proactive) | High - AI "knows" things about the user |
| 5 | Contextual | Location, time, device, ambient signals | Highest (seamless) | Highest - feels invasive if done without consent |
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 · 137 lines · 60 tokens per session scan A c308421d0bef
ai-personalization-ethics is a skill published in the GitHub repository varunk130/ai-ux-skill-library (3 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,742 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.
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