How do I install the Onprem Document Vision skill?

Run npx skillmds@latest add infant83/onprem-document-vision in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Onprem Document Vision safe to use?

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Which AI agents work with Onprem Document Vision?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Onprem Document Vision free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Onprem Document Vision?

Infant83 (@infant83) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Onprem Document Vision by infant83 — Agent Skill

Name: Onprem Document Vision
Author: Infant83

Onprem Document Vision

Analyze rendered document pages with the on-prem OpenAI-compatible vision model when the active Cline or DeepAgents model cannot reliably inspect images. Use for image-heavy PDFs, scanned PDFs, DOCX files with screenshots or diagrams, layout-critical review, table or figure extraction from rendered pages, and any document task where plain text extraction is insufficient.

Infant83 Updated Sep 22, 2026

File contents

On-Prem Document Vision

Quick start

Use this skill when the current assistant model cannot inspect images well enough for document review.
Prefer this skill for scanned PDFs, screenshots inside DOCX, diagrams, tables, slide-like pages, and layout-sensitive review.
The helper calls the on-prem OpenAI-compatible endpoint directly and returns Markdown or JSON that the non-vision model can continue reasoning over.
Default model: Llama-4-Scout
Default API root: http://10.116.240.101:8030/openai
Authentication defaults to OPENAI_API_KEY

If this skill is installed under DeepAgents, replace ~/.cline/skills in the examples below with ~/.deepagents/agent/skills.

Environment

Optional overrides:

$env:OPENAI_BASE_URL="http://10.116.240.101:8030/openai"
$env:OPENAI_MODEL_VISION="Llama-4-Scout"
$env:OPENAI_API_KEY="your-api-key"

export OPENAI_BASE_URL="http://10.116.240.101:8030/openai"
export OPENAI_MODEL_VISION="Llama-4-Scout"
export OPENAI_API_KEY="your-api-key"

Main helper

Analyze a PDF directly:

python "$HOME/.cline/skills/onprem-document-vision/scripts/document_vision_review.py" `
  review "C:\path\to\file.pdf"

Analyze only the first 5 pages and write Markdown:

python "$HOME/.cline/skills/onprem-document-vision/scripts/document_vision_review.py" `
  review "C:\path\to\file.pdf" `
  --max-pages 5 `
  --markdown `
  --markdown-output "C:\temp\file-review.md"

Analyze a DOCX with custom instructions:

python "$HOME/.cline/skills/onprem-document-vision/scripts/document_vision_review.py" `
  review "C:\path\to\file.docx" `
  --instructions "Extract the visible Korean text, explain diagrams, and identify layout risks."

Analyze already-rendered page images in a directory:

python "$HOME/.cline/skills/onprem-document-vision/scripts/document_vision_review.py" `
  review "C:\temp\rendered-pages" `
  --pages "1-3,5"

Workflow

Decide whether plain text extraction is enough.
If the document includes images, scans, tables, or layout-critical content, use this helper instead of relying on the base LLM.
Read the helper output before editing or summarizing the document.
After meaningful document edits, re-render and re-run the helper when visual fidelity still matters.

Output

Default stdout format: JSON
--markdown: print Markdown to stdout
--json-output: write JSON file
--markdown-output: write Markdown file

The JSON result contains:

source path
input type
selected model and API root
analyzed pages
per-page analysis text
final document summary

Notes

PDF rendering uses pypdfium2 and does not require a separate Poppler install.
DOCX rendering reuses the shared doc skill renderer, which depends on LibreOffice plus the Python packages pypdfium2 and pillow.
If the helper fails because the vision endpoint or API key is missing, stop and fix the environment first.
For endpoint compatibility notes and prompt patterns, read references/vision-routing.md.

---name: onprem-document-visiondescription: Analyze rendered document pages with the on-prem OpenAI-compatible vision model when the active Cline or DeepAgents model cannot reliably inspect images. Use for image-heavy PDFs, scanned PDFs, DOCX files with screenshots or diagrams, layout-critical review, table or figure extraction from rendered pages, and any document task where plain text extraction is insufficient.---# On-Prem Document Vision## Quick start- Use this skill when the current assistant model cannot inspect images well enough for document review.- Prefer this skill for scanned PDFs, screenshots inside DOCX, diagrams, tables, slide-like pages, and layout-sensitive review.- The helper calls the on-prem OpenAI-compatible endpoint directly and returns Markdown or JSON that the non-vision model can continue reasoning over.- Default model: `Llama-4-Scout`- Default API root: `http://10.116.240.101:8030/openai`- Authentication defaults to `OPENAI_API_KEY`If this skill is installed under DeepAgents, replace `~/.cline/skills` in the examples below with `~/.deepagents/agent/skills`.## EnvironmentOptional overrides:```powershell$env:OPENAI_BASE_URL="http://10.116.240.101:8030/openai"$env:OPENAI_MODEL_VISION="Llama-4-Scout"$env:OPENAI_API_KEY="your-api-key"``````bashexport OPENAI_BASE_URL="http://10.116.240.101:8030/openai"export OPENAI_MODEL_VISION="Llama-4-Scout"export OPENAI_API_KEY="your-api-key"```## Main helperAnalyze a PDF directly:```powershellpython "$HOME/.cline/skills/onprem-document-vision/scripts/document_vision_review.py" ` review "C:\path\to\file.pdf"```Analyze only the first 5 pages and write Markdown:```powershellpython "$HOME/.cline/skills/onprem-document-vision/scripts/document_vision_review.py" ` review "C:\path\to\file.pdf" ` --max-pages 5 ` --markdown ` --markdown-output "C:\temp\file-review.md"```Analyze a DOCX with custom instructions:```powershellpython "$HOME/.cline/skills/onprem-document-vision/scripts/document_vision_review.py" ` review "C:\path\to\file.docx" ` --instructions "Extract the visible Korean text, explain diagrams, and identify layout risks."```Analyze already-rendered page images in a directory:```powershellpython "$HOME/.cline/skills/onprem-document-vision/scripts/document_vision_review.py" ` review "C:\temp\rendered-pages" ` --pages "1-3,5"```## Workflow1. Decide whether plain text extraction is enough.2. If the document includes images, scans, tables, or layout-critical content, use this helper instead of relying on the base LLM.3. Read the helper output before editing or summarizing the document.4. After meaningful document edits, re-render and re-run the helper when visual fidelity still matters.## Output- Default stdout format: JSON- `--markdown`: print Markdown to stdout- `--json-output`: write JSON file- `--markdown-output`: write Markdown fileThe JSON result contains:- source path- input type- selected model and API root- analyzed pages- per-page analysis text- final document summary## Notes- PDF rendering uses `pypdfium2` and does not require a separate Poppler install.- DOCX rendering reuses the shared `doc` skill renderer, which depends on LibreOffice plus the Python packages `pypdfium2` and `pillow`.- If the helper fails because the vision endpoint or API key is missing, stop and fix the environment first.- For endpoint compatibility notes and prompt patterns, read `references/vision-routing.md`.

Frequently asked questions