dsh-vision-toolkit
Extends text-only LLMs with vision capabilities for practical engineering tasks—image analysis, OCR, UI automation—without switching models or platforms, red…
让纯文本模型更好地做视觉任务的DeepSeek Harness插件:带意图的图片问答、长截图 OCR、UI 还原等|DeepSeek Harness-native integration for agent-vision-toolkit: image Q&A, long-screenshot OCR, UI restoration, grounding, pixel diff, Artifacts, and Web UI.
- Ask Claude to extract text from long screenshots using OCR capabilities
- Generate UI component code by uploading interface screenshots for restoration
- Find visual differences between two interface versions using pixel comparison
Extends text-only LLMs with vision capabilities for practical engineering tasks—image analysis, OCR, UI automation—without switching models or platforms, reducing latency and infrastructure complexity.
Engineering teams using DeepSeek models who need vision-based automation for screenshot analysis, UI testing, or document OCR without additional vision APIs.
https://github.com/Anionex/dsh-vision-toolkit
By Anionex
How to Get It
claude plugins install Anionex/dsh-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 long screenshots using OCR capabilities
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): 585 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): 585 GitHub stars; contributors unknown; last commit 0d ago; license MIT.