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Automated security scan — not a certification.
by Anionex
为纯文本模型"看图“设计更好的视觉工具箱和技能，支持多图理解，图片问答，前端UI还原、GUI 自动化等，并可选无缝接入多个主流agent，直接识别粘贴图片｜ A vision toolkit and skill designed for text-only llms — image Q&A, long-screenshot OCR, frontend UI restoration, and GUI automation, with optional seamless integration for Codex, Claude Code, Pi, Oh My Pi, and OpenCode
# Add to your Claude Code skills
git clone https://github.com/Anionex/agent-vision-toolkitGuides for using ai agents skills like agent-vision-toolkit.
Last scanned: 8/6/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-08-06T06:30:41.833Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how agent-vision-toolkit compares with popular alternatives.
agent-vision-toolkit is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Anionex. 为纯文本模型"看图“设计更好的视觉工具箱和技能，支持多图理解，图片问答，前端UI还原、GUI 自动化等，并可选无缝接入多个主流agent，直接识别粘贴图片｜ A vision toolkit and skill designed for text-only llms — image Q&A, long-screenshot OCR, frontend UI restoration, and GUI automation, with optional seamless integration for Codex, Claude Code, Pi, Oh My Pi, and OpenCode. It has 1,253 GitHub stars.
Yes. agent-vision-toolkit passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Anionex/agent-vision-toolkit" and add it to your Claude Code skills directory (see the Installation section above).
agent-vision-toolkit is primarily written in Python. It is open-source under Anionex on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh agent-vision-toolkit against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
What it thinks is what it sees — give any text-only coding agent eyes: image Q&A, long-screenshot OCR, frontend UI restoration, and GUI automation, as a vision toolkit plus a skill, with optional drop-in integration for Codex, Claude Code, Pi, Oh My Pi, and OpenCode.
🎯 An agent's vision capability doesn't have to live in the model — it can live in the harness.
🌐 中文 ｜ English
If your agent already runs on a text-only model such as DeepSeek but is held back by the lack of multimodality — unable to see images, with every attempt to use an image tool blocked by the system — this repository provides tools, skills, and proxy integrations that let text-only models handle visual tasks on equal or even better footing. The goal is to make the experience of using a text-model agent as seamless as using a multimodal one, and ultimately let a tool-equipped text-model agent outperform a native multimodal agent that does not use this toolkit and its methods.
This repository provides two kinds of components:
All code has been verified in real Codex + DeepSeek sessions, and the same pipeline has been live-verified end-to-end in Claude Code, Pi, Oh My Pi, and OpenCode.
If this project helps you or gives you some inspiration, feel free to star🌟 & fork.
Want to sponsor this project? See FUNDING.md or email davidyang042@gmail.com.
2026-08-18 — Skill renamed: the included agent skill is now vision-skills (formerly vision-tools), so the name describes the capability rather than the underlying tools.
2026-08-13 — Native DeepSeek Harness support is now available. The new dsh-vision-toolkit linked package brings this toolkit into DSH Web and Headless profiles as a native Profile Bundle. It provides 10 structured visual tools for intent-aware image Q&A, grounding, detection, tracing, cropping, pixel diff, long-screenshot OCR, foreground extraction, dominant-color analysis, and HTML screenshots, while adding DSH Credentials, a managed isolated runtime, previewable Artifacts, Web Settings, and Agent-scoped progressive tool exposure.
The package is tracked here as a Git submodule and maintained independently at Anionex/dsh-vision-toolkit. Clone this repository with --recurse-submodules, or run git submodule update --init --recursive in an existing checkout.
The included vision-skills skill contains complete examples that an agent can follow directly.
When to use them, the order in which to call tools, and how to verify the result are all documented in the corresponding skill guides:
| Use case | What the agent learns to do |
|---|---|
| Extract long screenshots, chat histories, and scrolling pages | Find low-content cut bands, OCR each chunk in order, preserve chat speakers/timestamps/quotes, merge only duplicated overlap, and surface risky boundaries for verification. See the Telegram reference run → |
| Rebuild a UI from a screenshot or design | Reuse project components and assets first, then combine code-native UI, extracted visuals, rendered screenshots, and visual comparison to align a page or component. |
| Restore an icon, logo, illustration, or other graphic | Extract a transparent PNG from the source image, or rebuild an editable/scalable SVG when needed, then verify shape, color, and alpha edges. |
| Turn a sketch, diagram, or whiteboard into structured code | Recover nodes, labels, connections, and directions as editable Mermaid, Graphviz, or another structured representation. |
| Operate a GUI from screenshots | Locate a control, perform one action, capture the screen again, and verify the resulting state before continuing. |
| More use cases | Other step-by-step visual-agent playbooks are being added gradually. |
Left: the original infographic screenshot. Right: an editable reconstruction built with HTML/CSS. View the HTML source →
*Left: the hand-drawn reference. Right: the restored JupyterLab workspace made from it. See the UI restoration playbook for the workflow. Executed in Codex with `deepseek-v4-flash
