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 agentmods add skills/coinyak/onchainai/visual-qanpx skills add Coinyak/onchainai --skill visual-qagit clone --depth 1 https://github.com/Coinyak/onchainaiWrote 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/coinyak/onchainai/visual-qa)<a href="https://agentmods.dev/skills/coinyak/onchainai/visual-qa"><img src="https://agentmods.dev/badge/skills/coinyak/onchainai/visual-qa.svg" alt="Measured on agentmods" 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 | $0.00052 | $0.02196 |
| Opus 5 | $0.00026 | $0.01098 |
| Sonnet 5 | $0.00010 | $0.00439 |
| Haiku 4.5 | $0.00005 | $0.00220 |
Grade A, and why
visual-qa 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 4d 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 — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual QA
Use vision models to self-review screenshots against design intent. Catch spacing issues, alignment problems, color inconsistencies, responsive bugs, and accessibility gaps before shipping.
When to Use
- User asks to "review this design" or "check this screenshot"
- After building a page/component, before shipping
- User wants to compare implementation vs mockup/reference
- User asks "does this look right?" or "what's off about this?"
- Automated design QA step in a build workflow
Core Philosophy
- Screenshot first, then critique. Always look at the actual rendered output, not just the code.
- Be specific. "The spacing looks off" is useless. "The gap between the heading and paragraph is 32px but should be 16px based on the surrounding spacing rhythm" is useful.
- Prioritize impact. Not every pixel matters. Focus on what users will actually notice.
- Reference the intent. Compare against design tokens, mockups, or stated design goals.
How to Review
Step 1: Capture the Screenshot
Use one of these methods to get a screenshot:
Via browser tool:
browser: screenshot (captures the current page)
Via node screen capture (if available):
node: screen_record
Via Peekaboo (macOS):
exec: peekaboo screenshot
User-provided: The user may paste/attach a screenshot directly.
Step 2: Analyze with Vision
Use the image tool to analyze the screenshot:
image: [path or URL to screenshot]
prompt: "Review this UI screenshot for design quality..."
Step 3: Structured Review
Analyze the screenshot against these categories (in priority order):
Review Categories
1. Layout & Spacing
Check for:
- Consistent spacing rhythm (is everything on the spacing grid?)
- Alignment — are elements that should be aligned actually aligned?
- Padding consistency within similar components
- Container widths and max-widths
- Responsive behavior (if multiple viewport screenshots available)
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.
- 4d ago First seen · 329 lines · 52 tokens per session scan A 98eca5078b4d
visual-qa is a skill published in the GitHub repository Coinyak/onchainai (1 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 2,196 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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omd:apply
프로젝트 DESIGN.md를 UI/시각 작업의 brand context로 적용. 컴포넌트·색상·폰트·레이아웃 수정 같은 구체적 요청과 톤·분위기 표현 — KR '좀 더 따뜻하게', EN 'make it warmer/cooler', 日本語「もう少し暖かく」, 繁體中文「更溫暖一點」 — 모두에 트리거. DESIGN.md 부재 시 omd:init 우선. 화면 전체 신규 디자인은 omd:harness, 교정 기록은 omd:remember.
omd-apply
프로젝트 DESIGN.md를 UI/시각 작업의 brand context로 적용. 컴포넌트·색상·폰트·레이아웃 수정 같은 구체적 요청과 톤·분위기 표현 — KR '좀 더 따뜻하게', EN 'make it warmer/cooler', 日本語「もう少し暖かく」, 繁體中文「更溫暖一點」 — 모두에 트리거. DESIGN.md 부재 시 omd:init 우선. 화면 전체 신규 디자인은 omd:harness, 교정 기록은 omd:remember.