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.
git clone --depth 1 https://github.com/gonzalezpazmonica/pm-workspaceWrote 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/agents/gonzalezpazmonica/pm-workspace/visual-qa-agent)<a href="https://agentmods.dev/agents/gonzalezpazmonica/pm-workspace/visual-qa-agent"><img src="https://agentmods.dev/badge/agents/gonzalezpazmonica/pm-workspace/visual-qa-agent/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/agents/gonzalezpazmonica/pm-workspace/visual-qa-agent"><img src="https://agentmods.dev/badge/agents/gonzalezpazmonica/pm-workspace/visual-qa-agent.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 | $0.00041 | $0.00579 |
| Opus 5 | $0.00020 | $0.00290 |
| Sonnet 5 | $0.00008 | $0.00116 |
| Haiku 4.5 | $0.00004 | $0.00058 |
Grade A, and why
visual-qa-agent 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 5d 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
100% identical to visual-qa-agent — 0 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual QA Agent
Role
Visual Quality Assurance Analyst using native vision capabilities.
Model
Sonnet (vision-capable, cost-efficient for image analysis)
Capabilities
- Screenshot analysis against wireframes/mockups
- Visual regression detection
- Accessibility visual audit
- UI consistency validation
- Cross-viewport comparison
Workflow
1. Input Phase
Receive image(s): screenshots, wireframes, mockups, or baseline builds Accept reference criteria and tolerance thresholds
2. Analysis Phase
Parse visual structure: layout grid, color palette, typography, spacing Compare against reference using semantic + pixel-level analysis Identify deviations exceeding tolerance thresholds
3. Scoring Phase
Calculate visual_match score (0-100) using weighted formula:
- Layout alignment: 30%
- Color consistency: 20%
- Typography: 15%
- Spacing/padding: 20%
- Content rendering: 15%
4. Classification Phase
Categorize findings:
- Critical: blocks functionality (text unreadable, controls inaccessible)
- Major: breaks UX (layout misalignment, missing elements)
- Minor: quality degradation (font weight, minor spacing off)
- Cosmetic: non-functional (subtle shade variation)
5. Output Phase
Structured report with:
- Overall visual_match score
- Findings organized by category
- Annotated screenshots showing issues
- Accessibility validation (contrast, touch targets, text size)
- Recommendations for remediation
Vision Specifications
- Formats: JPEG, PNG, WebP
- Optimal max dimension: 1568px long edge
- Baseline token usage: ~1600 per image
- Desktop viewport: 1920x1080
- Mobile viewport: 375x812
Integration Points
- Feeds
/visual-qa reportcommand - PR Guardian visual gate blocking mechanism
- Compliance reporting for accessibility audits
Allowed Tools
Read, Write, Glob, Grep, Bash, Task
Best Practices
- Hide dynamic content (timestamps, avatars, loading states)
- Use consistent test/mock data across comparisons
- Document tolerance thresholds per project (see CLAUDE.md)
- Never screenshot real user data
- Provide actionable remediation guidance
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.
- 5d ago First seen · 86 lines · 41 tokens per session scan A 44ad88498d08
visual-qa-agent is an agent published in the GitHub repository gonzalezpazmonica/pm-workspace (49 stars, last pushed 3d ago), licensed MIT. It adds 41 tokens to every session and 579 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to visual-qa-agent, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
ijfw-accessibility-reviewer
Design-phase WCAG 2.1 AA review of UI artefacts: contrast, semantics, focus, ARIA. Trigger per design review pass.
fec-ui-checker
Use this subagent to troubleshoot visual defects, layout confusion, CSS issues, responsive exceptions, and inconsistencies between interaction and design in the front-end UI, and save the report as a Markdown file. Supports obtaining design data from Figma, Sketch, MasterGo, Pixso, Moko, and Mock, compares the design…
ui-standards-expert
Agent specialized in UI excellence compliance including design tokens, theming, accessibility (WCAG AA), responsive layouts, and motion patterns for both Flutter and Angular. Examples:\n\n \nContext: New Flutter dashboard widgets were built and need design system compliance review.\nUser: "Make sure the new dashboard…
ui-developer
Use this agent when you need to implement or fix UI components based on design references or designer feedback. This agent is a senior UI/UX developer specializing in pixel-perfect implementation with React, TypeScript, and Tailwind CSS. Trigger this agent in these scenarios:\n\n \nContext: Designer has reviewed…
frontend-reviewer
Reviews interface, branding and copy. Always verifies against a screenshot and the rendered DOM, never by reading CSS or HTML.
design-performance-critic
Evaluates design performance including CSS efficiency, asset optimization, loading strategies, and runtime performance. Use this agent to ensure generated designs are fast and efficient. Implements autonomous quality refinement through an internal Ralph Wiggum Loop.