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 AIDotNet/OpenCowork --skill product-design-design-qagit clone --depth 1 https://github.com/AIDotNet/OpenCoworkWrote 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/aidotnet/opencowork/product-design-design-qa)<a href="https://agentmods.dev/skills/aidotnet/opencowork/product-design-design-qa"><img src="https://agentmods.dev/badge/skills/aidotnet/opencowork/product-design-design-qa/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/aidotnet/opencowork/product-design-design-qa"><img src="https://agentmods.dev/badge/skills/aidotnet/opencowork/product-design-design-qa.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 | $0.00070 | $0.01778 |
| Opus 5 | $0.00035 | $0.00889 |
| Sonnet 5 | $0.00014 | $0.00356 |
| Haiku 4.5 | $0.00007 | $0.00178 |
Grade A, and why
product-design-design-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 11d 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.
This is a copy
91% identical to design-qa — 25 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design QA
Use this internal helper to compare a prototype's source design against the rendered implementation before handoff.
Do not use this skill for broad UX critique, design critique, product audits, or flow reviews. Use audit for those user-facing requests.
Use this skill before every Product Design build handoff.
A passing QA run requires both:
- a source visual target: Figma node, image, screenshot, mockup, or source capture
- a rendered implementation: local URL, deployed URL, app screen, component, or screenshot
If either artifact cannot be opened, captured, or compared, write design-qa.md with final result: blocked and name the blocker. Do not let the build skill hand off as done.
Critical Overrides
Follow critical-overrides.
Workflow
Compare the intended design to the implementation as a product-quality reviewer, not as a generic aesthetic critic. The output must be a prioritized fix list grounded in evidence from both artifacts.
Do not write the QA review from memory, code, or file paths alone. Open or capture both the source design and the implementation first, then compare what is actually visible.
Do not pretend separate image views are side-by-side comparison. Put the source image and the implementation screenshot together in the same comparison input, then judge the visible differences from that combined input.
-
Identify the comparison target.
- Determine the source design: Figma node, image, design board, screenshot, spec, or mockup.
- Determine the implementation: local URL, deployed URL, app screen, component, screenshot, or code-rendered view.
- Match the same viewport, state, theme, device density, route, content, auth state, and interaction state before judging.
- If artifacts do not represent the same state, call that out first and avoid false precision.
-
Capture evidence.
- For Figma, use design context and screenshot tools when available.
- For web/app implementations, open the target in a browser and capture screenshots at the intended viewport.
- Capture additional states when relevant: mobile/desktop, hover/focus/active, empty/loading/error, dark/light, and key responsive breakpoints.
- Save paths or URLs for screenshots when available so findings can cite evidence.
- Capturing screenshots is not enough. Put the source image and the implementation screenshot together in the same comparison input before judging.
What ships with it
5 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.
- 11d ago First seen · 134 lines · 70 tokens per session scan A a74b3a8af9a4
product-design-design-qa is a skill published in the GitHub repository AIDotNet/OpenCowork (635 stars, last pushed 8d ago), licensed Apache-2.0. It adds 70 tokens to every session and 1,778 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to design-qa, differing in 25 lines, and is treated as a copy.
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