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/saviaWrote 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/savia/visual-qa-agent)<a href="https://agentmods.dev/agents/gonzalezpazmonica/savia/visual-qa-agent"><img src="https://agentmods.dev/badge/agents/gonzalezpazmonica/savia/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/savia/visual-qa-agent"><img src="https://agentmods.dev/badge/agents/gonzalezpazmonica/savia/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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- visual-qa-agent — 100% identical, 0 lines differ
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.
- 2d ago First seen · 86 lines · 41 tokens per session scan A 44ad88498d08
visual-qa-agent is an agent published in the GitHub repository gonzalezpazmonica/savia (50 stars, last pushed today), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-06.
Other agents, from other repositories
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feature-verifier
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kb-interaction-mapper
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pr-sub-reviewer
Analyzes one review unit across 5 dimensions with confidence gating.
func-verifier
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p5s-coverage-orchestrator
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