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 agentlas-ai/agentlas-desktop --skill design-qagit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/design-qa)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/design-qa"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/design-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 5.1 | $0.00029 | $0.00717 |
| Opus 5 | $0.00015 | $0.00358 |
| Sonnet 5 | $0.00006 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
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 3d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Purpose
Acts as the mandatory quality gate before prototype delivery by conducting a 1:1 side-by-side visual comparison between the pinned visual target (assets/target-design.png) and the live browser rendering (implementation.png), iterating until pixel-faithful alignment is achieved.
Preconditions
- The pinned visual target (
assets/target-design.pngor equivalent source mockup) must exist. - A rendered prototype view (
implementation.pngcaptured from the local browser runtime) must be available. - If either artifact cannot be retrieved or rendered, mark
final result: blockedand halt handoff. - Adhere to
$qa-rubricand$critical-overrides.
Steps
- Normalize Viewport & Pixel Density:
- Align the implementation capture (
implementation.png) andassets/target-design.pngto the exact same CSS pixel dimensions, aspect ratio, and device scale factor. - For mobile apps, evaluate the inner screen content at 1:1 CSS pixels rather than container bezels.
- Align the implementation capture (
- Side-by-Side Comparison & Inspection:
- Place
assets/target-design.pngandimplementation.pngtogether in a side-by-side comparison view. - Systematically inspect the 5 required fidelity surfaces:
- Typography: Font family, optical weights, sizes, line heights, letter spacing, and wrapping.
- Spacing & Rhythm: Margins, padding, grid alignment, section gaps, border radii, and elevation.
- Colors & Tokens: Color palette accuracy, background/foreground contrast, and semantic state styling.
- Image Quality & Assets: Asset fidelity, resolution, sharpness, and absence of makeshift CSS/SVG approximations.
- Copy & Content: Accurate matching of headings, labels, metrics, and visible body copy.
- Place
- Iterative Remediation Loop:
- Classify observed differences into P0 (blocker), P1 (major), P2 (moderate), and P3 (minor polish).
- For any P0/P1/P2 findings:
- Apply targeted code/CSS fixes to the prototype.
- Re-capture the browser screenshot at the identical viewport and state.
- Re-compare against
assets/target-design.png.
- Repeat until no actionable P0/P1/P2 differences remain.
- Generate QA Report: Document findings, remediation history, and evidence paths in
design-qa.mdat the project root withfinal result: passed.
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.
- 3d ago Changed · +4 lines 13e4c63ff7d9
- 7d ago First seen · 44 lines · 29 tokens per session scan A b1ab0088fb4d
design-qa is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 29 tokens to every session and 717 once invoked, about $0.0001 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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