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 aditya-ariosity/ux-ui-skills --skill ai-product-designgit clone --depth 1 https://github.com/aditya-ariosity/ux-ui-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/aditya-ariosity/ux-ui-skills/ai-product-design)<a href="https://agentmods.dev/skills/aditya-ariosity/ux-ui-skills/ai-product-design"><img src="https://agentmods.dev/badge/skills/aditya-ariosity/ux-ui-skills/ai-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/aditya-ariosity/ux-ui-skills/ai-product-design"><img src="https://agentmods.dev/badge/skills/aditya-ariosity/ux-ui-skills/ai-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 | $0.00105 | $0.01551 |
| Opus 5 | $0.00053 | $0.00776 |
| Sonnet 5 | $0.00021 | $0.00310 |
| Haiku 4.5 | $0.00011 | $0.00155 |
Grade A, and why
ai-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 yesterday.
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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Product Design
Design the human-system relationship. The experience must help users understand capability, judge output quality, correct errors, and retain control over consequences.
1. Test whether AI belongs
Define:
- user goal and current workflow;
- value AI may add: speed, scale, synthesis, prediction, personalization, or automation;
- deterministic alternative and why it is insufficient;
- acceptable error, latency, cost, and review burden;
- affected people, data, objects, and downstream actions;
- business outcome and user outcome;
- evidence needed to continue investing.
Do not invent organization policy, retention, approval roles, architecture, quality thresholds, or launch percentages. Mark recommended controls as proposals and assign unresolved values to the accountable product, policy, security, legal, data, or engineering owner.
Prefer deterministic interaction when rules are stable, correctness must be exact, or users can complete the task faster without probabilistic behavior.
2. Map capability and risk
Read references/capability-risk-and-trust.md. Create:
Task | AI role | Inputs and sources | Expected quality | Failure modes | Consequence | Human control | Escalation
Separate suggestion, drafting, classification, recommendation, and execution. A system that can generate a plan does not automatically have permission to carry it out.
3. Select the interaction form
Use this decision map:
Task condition | Interaction form | Required controls
- local text, data, or design transformation | inline assistance | preview, compare, accept, reject, undo;
- known parameters and repeatable output | structured controls | defaults, constraints, validation, editable inputs;
- bounded choice among options | recommendation with evidence | ranked options, source links, tradeoffs, dismiss;
- ambiguous or iterative intent | conversational interaction | active scope, context window, correction, history;
- repeated review and approval | batch workflow | queue, status, bulk actions, item-level override;
- multi-step action across systems | agentic workflow | permission, preview, approval, execution log, verification, undo.
What ships with it
6 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.
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.
- yesterday Changed · +9 lines · +35 tokens per session 4db0caf47ef7
- 8d ago First seen · 151 lines · 70 tokens per session scan A 17c24dc0ca6a
ai-product-design is a skill published in the GitHub repository aditya-ariosity/ux-ui-skills (2 stars, last pushed 2d ago), licensed MIT. It adds 105 tokens to every session and 1,551 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 2026-09-04.
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