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/liangdabiao/godogen/visual-qanpx skills add liangdabiao/Godogen --skill visual-qagit clone --depth 1 https://github.com/liangdabiao/GodogenWrote 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/liangdabiao/godogen/visual-qa)<a href="https://agentmods.dev/skills/liangdabiao/godogen/visual-qa"><img src="https://agentmods.dev/badge/skills/liangdabiao/godogen/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 | $0.00040 | $0.01324 |
| Opus 5 | $0.00020 | $0.00662 |
| Sonnet 5 | $0.00008 | $0.00265 |
| Haiku 4.5 | $0.00004 | $0.00132 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual QA
$ARGUMENTS
CRITICAL: Your job is to find problems, not confirm things look fine. Do not rationalize, justify, or explain away what you see. If it looks wrong, report it.
Backend
- Default (Gemini): Run the script below. All queries go to Gemini 3 Flash.
--nativeflag in arguments: Use Claude vision — read every image with the Read tool, analyze directly. Do NOT run the Gemini script.--bothflag in arguments: Run Gemini first, then do native analysis. Aggregate verdicts (details below).
Mode Detection
From the arguments — freeform text with file paths:
- Reference image mentioned + 1 screenshot → Static mode
- Reference image + multiple frames → Dynamic mode — frames are 0.5s apart (2 FPS cadence)
- No reference, just a question about screenshots → Question mode
Gemini Execution
Parse the arguments to construct the command. The script is at ${CLAUDE_SKILL_DIR}/scripts/visual_qa.py.
# Static
python3 ${CLAUDE_SKILL_DIR}/scripts/visual_qa.py --log .vqa.log [--context "Goal: ... Requirements: ... Verify: ..."] reference.png screenshot.png
# Dynamic
python3 ${CLAUDE_SKILL_DIR}/scripts/visual_qa.py --log .vqa.log [--context "..."] reference.png frame1.png frame2.png ...
# Question
python3 ${CLAUDE_SKILL_DIR}/scripts/visual_qa.py --log .vqa.log --question "the question" screenshot.png [frame2.png ...]
Always pass --log .vqa.log. Print the script output as your response.
Native Execution
Read every image file referenced in the arguments using the Read tool. Analyze using the criteria and output format below. Never look at code — only images.
After producing output, append a debug log entry:
printf '%s\n' "$(cat <<'LOGEOF'
{"ts":"$(date -u +%Y-%m-%dT%H:%M:%SZ)","mode":"MODE","model":"native","query":"QUERY","files":["FILE1","FILE2"],"output":"FIRST_LINE..."}
LOGEOF
)" >> .vqa.log
Aggregated Mode (--both)
- Run Gemini script, capture output
- Read all images with Read tool, do native analysis using criteria below
- Produce combined verdict:
- Either says
fail→fail - Either says
warningand neitherfail→warning - Both
pass→pass
- Either says
- Merge issue lists from both, deduplicate by location + description
- Label each issue source:
[gemini],[native], or[both] - Log both outputs to
.vqa.log
What ships with it
4 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.
- 5d ago First seen · 156 lines · 40 tokens per session scan A 76733e75f25a
visual-qa is a skill published in the GitHub repository liangdabiao/Godogen (119 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 1,324 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 2026-08-30.
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