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 aduermael/herm --skill image-visiongit clone --depth 1 https://github.com/aduermael/hermWrote 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/aduermael/herm/image-vision)<a href="https://agentmods.dev/skills/aduermael/herm/image-vision"><img src="https://agentmods.dev/badge/skills/aduermael/herm/image-vision.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.00602 |
| Opus 5 | $0.00027 | $0.00301 |
| Sonnet 5 | $0.00011 | $0.00120 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
image-vision 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 8d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Vision
Use the doc module with the exact attachment or local path. Do not guess or rewrite the path.
Read One File
Start with the simplest form:
local result = doc.read("/attachments/conversation-id/photo.jpeg")
print(result)
Images and PDFs default to vision when the vision callback is available. The built-in prompt extracts the document as structured Markdown and describes images, charts, diagrams, and other visual elements. This default is appropriate for most requests. Other supported document types default to structural extraction.
Treat the returned text as the visual model's analysis, then answer the user's question rather than merely repeating the extraction.
Supported visual inputs include PDF, PNG, JPEG, WebP, and GIF files.
Read Multiple Files
Issue all asynchronous reads before awaiting any result so vision requests can run in parallel:
local front = doc.readAsync("/attachments/conversation-id/front.jpeg")
local back = doc.readAsync("/attachments/conversation-id/back.jpeg")
local frontText = front:await()
local backText = back:await()
print(frontText)
print(backText)
Customize The Prompt
Set query only when the user needs a narrower analysis, special output format, or more detail than the default extraction:
local result = doc.read("/attachments/conversation-id/chart.jpeg", {
query = "Extract the chart's title, axis labels, legend, and data values as a Markdown table."
})
print(result)
Preserve the user's request in the query. Set mode = "vision" when an explicit visual-mode override is useful:
local result = doc.read("/attachments/conversation-id/report.pdf", {
mode = "vision",
query = "Describe the page layout and all diagrams."
})
print(result)
Use mode = "structural" for a PDF when machine-readable text matters and visual interpretation is unnecessary. When both appearance and exact embedded text matter, run vision and structural reads separately and distinguish their results. Do not substitute structural extraction for requested visual analysis.
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.
- 8d ago First seen · 70 lines · 54 tokens per session scan A 54aa995cbd6b
image-vision is a skill published in the GitHub repository aduermael/herm (232 stars, last pushed 26d ago), licensed MIT. It adds 54 tokens to every session and 602 once invoked, about $0.0003 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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