ModLens

Give a text-only model sight, and just paste the image.

🥇 The FIRST vision plugin for DeepSeek Harness (dsh) 🥇

简体中文 · Troubleshooting · Configuration · Output contract · Security · ModSearch (web)

The flagship DeepSeek and GLM chat models are text-only and cannot read images. ModLens is a plug-in vision engine that gives a text-only model sight. ModLens reads images pasted straight into the chat, no saving to a file and passing a path first.

Talk to us

Issues are welcome any time: open one. And come find me on X: @liustack. What you built with it, which harness you are on, what should come next. New releases land there first, and a proper community space is on the way.

Highlights

🥇 The first vision plugin for DeepSeek Harness (dsh): one command, npx -y @deepseek-ai/dsh plugin --profile web add @liustack/modlens@latest, and the text-only DeepSeek model behind dsh reads images through a native read_image tool. If dsh warns declares no dsh.bundle, pnpm's release-age gate installed an old version: see troubleshooting for the one-line fix.

Pasting an image works two ways. ① Just paste. On a text-only model the pasted image lands as a private temp file and its path enters the composer — the same interaction OpenCode and Pi ship — and the read_image tool takes it from there. ② Pick a (modlens vision) entry in the model selector (it remembers your choice, so once is enough), then paste: the thumbnail stays visible in your message, closer to the Codex app feel, and the image is converted to structured evidence at request time, answered by the same underlying route. The plugin auto-discovers every provider route carrying text-only DeepSeek or GLM models and adds a wrapped entry per route (a stock install gets DeepSeek-V4-Flash (modlens vision) and DeepSeek-V4-Pro (modlens vision); extra routes like opencode-go or zai get their own); the two families' own vision models are excluded automatically. Which paste route applies is the host's per-model call: only a model its metadata positively confirms text-only is taken over, anything unconfirmed is left alone, so vision models keep their native paste (details).

Paste an image and it reads it. No saving to a file and passing a path first.

• The lightest touch on the market. No hooks, no wrappers, no local proxy daemon, not a single line changed in any harness config: on the skill harnesses it is exactly one skill folder, on dsh exactly one plugin. Uninstalling is deleting a folder, and your agents are back to stock.
• Zero-config start. Reuses what Claude Code, Codex, OpenCode, or Pi already have set up: the multimodal models on your machine go straight to work. Nothing at all? Antigravity CLI is a free no-key channel, and a free Gemini key brings a read down to 5-10 seconds.
• Evidence, not imagination. Full transcription, reading-order layout regions, entity and relation lists. The model quotes specifics.
• Install once, use everywhere. Verified on real machines in Claude Code, Codex, Pi, and OpenCode.

Installation

Step 1, hand it to your AI. Send it this line:

Install and configure the modlens skill following https://github.com/liustack/modlens/blob/main/INSTALL.md, then run the health check and tell me the result.

The install starts by checking what your machine already has. An existing login in Claude Code, Codex, OpenCode, or Pi can be enough: modlens asks before reusing any of them, and the health check tells you where things stand.

Step 2, only if the health check comes back empty, set up a free engine. The recommended choice is a free Gemini API key (about three minutes at Google AI Studio, no credit card), which also makes every read 5-10 seconds. A free OpenAI-compatible key from another platform works too. To avoid any sign-up, install Antigravity CLI instead, then sign in:

curl -fsSL https://antigravity.google/cli/install.sh | bash
agy                                                           # sign in, then exit

The install also inventories vision reachable through your other local harness CLIs (Codex, OpenCode, Pi) and asks, per harness, whether modlens may reuse it. Granted logins join the engine pool as equals, and every reused read is labeled with whose quota it spent.

Usage

Once installed, just chat. Paste an image or drop a path, ask anything, and the skill triggers on its own: the image goes to a vision engine and the answer comes back grounded in what it read.

Vision engines: five built-in providers, four reusable CLIs, one failover chain

ModLens does not depend on any single vision service. Nine sources of vision in total: five built-in providers, any one of which is enough, plus four local agent CLIs whose logins can be reused. The built-ins:

Provider	What it needs	Speed per read	Good for
gemini-api	a free Gemini API key (3 minutes, no card)	5-10s	the recommended default
openai	any OpenAI-compatible endpoint (key + baseUrl + model)	5-10s	qwen-vl, GLM, self-hosted gateways
anthropic	an Anthropic API key	5-10s	machines already holding one
antigravity-cli	the free agy CLI, one browser sign-in, no key	15-45s	zero-signup starts
claude-cli	a signed-in Claude Code	20-45s	riding your existing Claude subscription

Without a pinned provider, every configured engine forms one failover chain: the fast API providers try first, the agent CLIs back them up, the first good result wins, and meta.attempts records every attempt so a fallback is never silent.

openai is a universal socket, not just OpenAI

Any endpoint speaking the OpenAI chat-completions protocol with image input plugs straight in — that covers most of the vision-model world:

modlens config set openai.baseUrl https://dashscope.aliyuncs.com/compatible-mode/v1   # qwen-vl
modlens config set openai.apiKey  <key>
modlens config set openai.model   qwen3-vl-plus

The same three keys work for GLM's open platform, SiliconFlow, OpenRouter, a self-hosted vLLM/Ollama, or any gateway of your own. If your favorite vision model has an OpenAI-compatible API, ModLens can drive it.

Reusing what your machine already has

Two more sources of vision need zero new keys, each behind one explicit consent recorded in config:

• The harness you are talking in right now. Running inside Claude Code with a subscription signed in? claude-cli reads images through it out of the box. The install flow asks the same question for whichever harness you install into.
• Every other agent CLI on the machine. modlens doctor discovers them, you grant per harness, and they join the same failover chain with no priority over your own keys. Every reused read is labeled in meta.warnings with whose quota it spent, so nothing is ever silently billed:

Reused CLI	What it needs	Grant with	Rides as
Codex	a signed-in Codex CLI with a vision model	config set reuse.codex true	agent lane, 15-45s
OpenCode	a vision model configured in OpenCode	config set reuse.opencode true	agent lane, 15-45s
Pi	model credentials held by Pi	config set reuse.pi true	an API key upgrades to the 5-10s inline lane, OAuth drives Pi itself
Grok	a signed-in Grok CLI (SuperGrok)	config set reuse.grok true	agent lane, 15-45s

Picking and routing

Two knobs: modlens config set provider <name> states a preference (the chain still backs it up), -p <name> pins exactly one with no fallback. Machines behind a proxy set HTTPS_PROXY or modlens config set proxy <url> and the API providers route through it. Details: the CLI manual for defaults and flags, Configuration for every key, and Security for who fetches what on remote URLs.

See it work

Unedited runs, all driving a text-only DeepSeek-V4-Flash.

The newest one first: pasting a screenshot straight into DeepSeek Harness on the DeepSeek-V4-Flash (modlens vision) variant. The paste keeps its native thumbnail, the trajectory shows the image arriving "already transcribed by the modlens vision bridge", and the answer walks the UI element by element.

A tweet screenshot in the Codex desktop app. It reads the author, the caption, the photo itself (down to what both people are wearing), the timestamp, and every engagement number: 5.4M views, 1.6K replies, 5.7K reposts, 116K likes.

Three images pasted at once. The model reads them one by one, spots that they belong to one visual family, and describes each illustration's content and style.

The stress test: a scatter plot comparing 128 AI models. It reads both axes, the log scale, the per-provider color coding, the highlighted region, and every DeepSeek model called out with dashed markers. Dense charts are where vision bridges most often fail.

And the paste path, end to end, in a Claude Code terminal on DeepSeek. The pasted image arrives as a path rather than pixels, the skill triggers on its own, the guard confirms the model truly has no vision, and the slide's full content comes back: titles, layout, background, plus an honestly stated uncertainty about the truncated filename.

Documentation

Doc	Read it when
Install guide	Installing the skill step by step (written for an agent)
CLI manual	The CLI the skill drives: flags, config, doctor
Troubleshooting	A command failed and the message needs decoding
Configuration	Setting a key, switching providers, fixing config
Output contract	Parsing the JSON or building on it
Harness setup	Wiring it into Codex, Claude Code, Pi, or OpenCode
Security	File permissions, image content as untrusted input
CHANGELOG	Finding what changed in a version

Contributing

ModLens does not accept pull requests. The project is maintained by a single author who reviews every line, which is a deliberate choice for reliability. Two effective ways to contribute:

• Open an issue. Bugs, suggestions, confusing errors, unclear docs. Issues are read and shape what gets built next.
• Fork it. Under MIT your copy is fully yours to modify and publish.

Shameless plug

This project runs on LIUSTACK Skills: shaping before you build, coding while you build, dig when it breaks, snapshot when you hand off. Lighter than Superpowers, and stronger.

npx -y skills add liustack/liustack -g

⭐ If it helps, star ModLens and liustack. Stars are how the next developer finds them.

Star History

Disclaimer

Provided as-is under the MIT License below. The author makes no warranty and gives no endorsement for any particular use, commercial use included. Your use of upstream engines (Antigravity CLI, the Gemini, OpenAI, and Anthropic APIs, and any OpenAI-compatible endpoint) is governed by their own terms and quotas, which you are responsible for.

License

MIT