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 magnus919/agent-skills --skill product-design-and-uxgit clone --depth 1 https://github.com/magnus919/agent-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/magnus919/agent-skills/product-design-and-ux)<a href="https://agentmods.dev/skills/magnus919/agent-skills/product-design-and-ux"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-design-and-ux/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/magnus919/agent-skills/product-design-and-ux"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-design-and-ux.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00107 | $0.01703 |
| Opus 5.5 | $0.00043 | $0.00681 |
| Sonnet 5.5 | $0.00021 | $0.00341 |
| Haiku 4.5 | $0.00011 | $0.00170 |
Grade A, and why
product-design-and-ux 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 28d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Design and UX
Turn validated evidence and chosen scope into observable behavior people can complete and recover from. Design the interaction, not the brand, pixels, CSS, component implementation, or a particular tool.
Entry Check
Confirm the approved scope, outcome, users or roles, evidence links, constraints, measurable experience goals, and open product decisions. Record missing inputs rather than inventing them.
- Need stakeholder evidence, validation, or raw research? Use product-discovery.
- Need prioritization, scope, or why a feature is being built? Use product-methodology.
- Need WCAG, ARIA, native semantics, or accessibility testing depth? Use web-accessibility.
- Need an approved interaction contract translated into a software specification and delivery gates? Use spec-driven-development.
- Need to design an AI-assisted, generated, predictive, recommendatory, or tool-using interaction? Read AI interaction and uncertainty and use the AI interaction contract alongside the general contract. Route model evaluation and observability design to agent-evals-and-observability, governance to ai-governance, runtime authority to agent-production-operations, product measurement to product-analytics-and-measurement, accessibility to web-accessibility, and cost/value decisions to ai-operating-economics.
- Need a person to delegate to an agent they use outside the product, then inspect or resume the work across apps? Read external agent interactions and fill the external agent handoff alongside the AI interaction and task-flow contracts. Do not assume the product hosts that agent or can see its private conversation.
What ships with it
25 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.
- evals/business-app-rubric-challenges.md 1.5 KB
- evals/evals.json 16 KB
- evals/openai-reference-inputs.json 196 B
- README.md 2.8 KB
- references/ai-interaction-and-uncertainty.md 7.5 KB
- references/boundaries-and-traceability.md 1.9 KB
- references/content-and-cognitive-demand.md 4.1 KB
- references/embedded-business-agent.md 6.2 KB
- references/engineering-handoff.md 4.1 KB
- references/external-agent-interactions.md 6.2 KB
- references/information-architecture.md 3.2 KB
- references/interaction-pattern-selection.md 3.7 KB
- references/interface-contracts-and-responsive-behavior.md 3.7 KB
- references/scenario-probes.md 2.1 KB
- references/source-index.md 5.9 KB
- references/task-flows-and-state-models.md 2.2 KB
- references/usability-testing-and-privacy.md 4.5 KB
- templates/ai-interaction-contract.md 2.4 KB
- templates/engineering-handoff.md 905 B
- templates/external-agent-handoff.md 3.6 KB
- templates/interface-contract.md 703 B
- templates/outcomes-to-design.md 627 B
- templates/screen-state-inventory.md 410 B
- templates/task-flow-state-model.md 737 B
- templates/usability-study.md 1.0 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.
- 28d ago Changed · +6 lines f4d93ad4389b
- 1mo ago Changed · +2 lines 74e76f4ff152
- 1mo ago Changed · +4 lines · +13 tokens per session 7f9f9b5a5e1c
- 2mo ago First seen · 64 lines · 94 tokens per session scan A e93cbfb38ef3
product-design-and-ux is a skill published in the GitHub repository magnus919/agent-skills (166 stars, last pushed 2d ago), licensed MIT. It adds 107 tokens to every session and 1,703 once invoked, about $0.0004 per session on Opus 5.5. 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.
product-design-and-ux compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| product-design-and-uxmagnus919/agent-skills | Skill | 166 | 2d ago | MIT | A | 107 | Claude Code, Codex |
| cocosketchSnowflake-Labs/cocoplus | Skill | 727 | 2d ago | MIT | A | 20 | Claude Code |
| sketch-autolayoutSnowflake-Labs/cocoplus | Skill | 727 | 2d ago | MIT | A | 37 | Claude Code, Codex |
| anatomyNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 15 | Claude Code, Codex |
| archifyNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 12 | Claude Code, Codex |
| infographic-creatorantvis/Infographic | Skill | 7.1k | 17d ago | MIT | A | 25 | Claude Code |
| latex-postersbeita6969/ScienceClaw | Skill | 1.0k | yesterday | MIT | A | 60 | Claude Code |