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 ulises-jeremias/agent-toolkit --skill design-assessmentgit clone --depth 1 https://github.com/ulises-jeremias/agent-toolkitWrote 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/ulises-jeremias/agent-toolkit/design-assessment)<a href="https://agentmods.dev/skills/ulises-jeremias/agent-toolkit/design-assessment"><img src="https://agentmods.dev/badge/skills/ulises-jeremias/agent-toolkit/design-assessment/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/ulises-jeremias/agent-toolkit/design-assessment"><img src="https://agentmods.dev/badge/skills/ulises-jeremias/agent-toolkit/design-assessment.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 | $0.00061 | $0.02346 |
| Opus 5 | $0.00030 | $0.01173 |
| Sonnet 5 | $0.00012 | $0.00469 |
| Haiku 4.5 | $0.00006 | $0.00235 |
Grade A, and why
design-assessment 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Assessment (WHAT)
Assess a product's interface as a design unit delegated by project-assessment. Use this skill when the user asks for a design audit, visual review, UX assessment, accessibility evaluation, design-system compliance check, or distinctive-identity review.
Router contract (ADR-0002 Option A): project-assessment collects evidence via project-assessment-evidence, then delegates to this skill alongside technical-unit-assessment / management-unit-assessment when UI/design is in scope. This skill is also directly invokable for single-page or multi-screen UI audits. It reuses the single evidence framework — do not re-ask evidence already in the map.
Default guardrails
- Apply
project-assessment-evidencebefore scoring. If calling viaproject-assessment, reuse its evidence map — do not duplicate intake questions. - Apply
output-handshakebefore producing any final design assessment report, scorecard, or roadmap. - Never assign a maturity score without evidence. If evidence is missing, mark the indicator as Not assessed or score with Low confidence and state the assumption. Never emit fake precision like
72/100— use rating bands + severity/confidence. - Screenshots must not capture secrets. Observe-only, L1. Redact private data. Keep sensitive details out of reusable artifacts.
- Generic-template risk (distinctiveness) must be context-aware —
rounded cards = badis not a rule. Evaluate whether the aesthetic is distinctive for this product's subject and tone, not via a checklist. - Vision fallback: This skill is
vision-requiredfor full visual critique. If the harness cannot render screenshots/recordings, perform a text-only heuristic review (code, tokens, a11y attributes) and flag each finding with Low confidence — vision unavailable.
Design-unit intake
Ask before scoring (or reuse from router evidence map):
- Product & purpose: product name, audience, single job the page/ flow does
- Scope: screens/flows/components in scope, Figma files / variables, Storybook, Tailwind/config, design-system entry points
- Repositories & assets: app repo(s), component library, token files, asset locations
- Systems of record for design evidence: Figma, Storybook, WCAG reports, performance reports, screenshots, recordings, product analytics, UX research
- Assessment period & audience: who will act on findings
- Decision ownership: who validates subjective visual judgments
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.
- 10d ago First seen · 151 lines · 61 tokens per session scan A 28e249cf36ec
design-assessment is a skill published in the GitHub repository ulises-jeremias/agent-toolkit (16 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 2,346 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 2026-08-30.
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