agent-vision-toolkit
Enables LLM agents to process visual inputs—screenshots, diagrams, UI elements—without manual transcription, reducing integration friction and expanding auto…
给纯文本 LLM agent 装上眼睛:图片问答、OCR、截图分析、视觉定位等一套视觉工具箱 + skill,并可无缝接入 Codex、Claude Code、OpenCode、Pi | Give text-only LLM agents vision: image Q&A, OCR, screenshot understanding, visual grounding, image-to-SVG - a vision toolkit & skill, with drop-in integration for Codex, Claude Code, OpenCode, Pi
- Ask Claude to extract text from screenshots and identify UI elements automatically
- Automate visual testing by having Claude analyze application screenshots for layout issues
- Generate SVG coordinates from images to locate and interact with specific UI components
Enables LLM agents to process visual inputs—screenshots, diagrams, UI elements—without manual transcription, reducing integration friction and expanding automation scope for code generation and analysis workflows.
Engineering teams automating UI testing, documentation generation, or visual debugging tasks with Claude or compatible agents.
https://github.com/Anionex/agent-vision-toolkit
By Anionex
How to Get It
claude plugins install Anionex/agent-vision-toolkit
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Extract text from screenshots and identify UI elements automatically
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 284 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 284 GitHub stars; contributors unknown; last commit 0d ago; license MIT.