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The first vision plugin for DeepSeek Harness, and the vision bridge for every text-only coding agent. Paste an image, get structured JSON evidence (OCR, layout, semantics). | 全网第一个 DeepSeek Harness 视觉插件，为 DeepSeek、GLM 等纯文本模型外挂视觉能力，粘贴图片即得结构化 JSON 证据（OCR、版面、语义）。
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
ModLens solves the problem of text-only models being unable to process visual information, which limits their usefulness in coding tasks that involve screenshots, diagrams, or UI mockups. It provides a lightweight, non-invasive bridge that adds vision capabilities without requiring model changes, proxies, or complex configuration, and it works across multiple popular coding harnesses.
Deployment reality
The available setup signal starts with: npx -y @deepseek-ai/dsh plugin --profile web add @liustack/modlens@3.16.7. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build agents that can analyze screenshots, extract text from images, understand UI layouts, and reason about visual content in coding workflows. Real-world use cases include automated UI testing, documentation generation from screenshots, debugging visual issues, and enhancing code review with visual context. The ceiling is high: with structured JSON evidence, agents can perform complex multimodal reasoning, such as comparing design mockups to implemented code or extracting data from charts and diagrams.
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liustack/modlens is tracked as a TypeScript project in the AI Agent, LLM Tool area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 1.9k total stars, with +590 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 7 days ago, and the open issue queue is 7, about 0.37% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 50 forks and 1.9k watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need TypeScript teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 590. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyMIT is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
1.9k Stars
Reuse
50 Forks
Attention
1.9k Watchers
Maintenance
active
License
MIT
Open issues
7
ModLens is a plug-in vision engine that gives text-only AI models (like DeepSeek and GLM) the ability to read images pasted directly into chat, without saving files or passing paths. It is the first vision plugin for DeepSeek Harness (dsh) and works across multiple harnesses including Claude Code, Codex, OpenCode, and Pi.
Key Features
- Zero-config start: reuses existing logins from Claude Code, Codex, OpenCode, or Pi; no hooks, wrappers, or proxy daemons. - Evidence-based reading: provides full transcription, layout regions, and entity/relation lists. - Multiple vision engines: five built-in providers (Gemini, OpenAI, Anthropic, Antigravity CLI, etc.) with failover chain.
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
npx -y @deepseek-ai/dsh plugin --profile web add @liustack/modlens@3.16.773
Health Score
Active
Commit Activity
Feb 22, 2026
Created
Aug 15, 2026
Last push
+0
Today's growth
+0
7-day growth
+0
30-day growth
Forks
Open
Watchers
liustack
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An agentic skills framework & software development methodology that works.
affaan-m/ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
multica-ai/andrej-karpathy-skills
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
ModLens solves the problem of text-only models being unable to process visual information, which limits their usefulness in coding tasks that involve screenshots, diagrams, or UI mockups. It provides a lightweight, non-invasive bridge that adds vision capabilities without requiring model changes, proxies, or complex configuration, and it works across multiple popular coding harnesses.
Capabilities
Developers can build agents that can analyze screenshots, extract text from images, understand UI layouts, and reason about visual content in coding workflows. Real-world use cases include automated UI testing, documentation generation from screenshots, debugging visual issues, and enhancing code review with visual context. The ceiling is high: with structured JSON evidence, agents can perform complex multimodal reasoning, such as comparing design mockups to implemented code or extracting data from charts and diagrams.
Bottom Line
ModLens is ideal for developers using text-only coding agents who need vision capabilities without heavy setup. It is perfect for those who want a quick, non-invasive way to handle images in coding workflows. However, teams with strict data privacy requirements should be cautious about sending images to external services, and those using models that already support vision may not need it. The key trade-off is convenience and breadth of support versus potential privacy and dependency concerns.