dsh-vision-toolkit
Anionex
Vision tasks for text-only models: intent-aware image Q&A, long-screenshot OCR, UI reproduction, grounding, and pixel diff.
npm packageTypeScripttoday
modlens
liustack/modlens
Vision bridge for text-only models: paste an image, get structured JSON evidence (OCR, layout, semantics).
Install
Add modlens to DeepSeek Harness
via npm
Resolves a published tarball rather than cloning the repository.
via GitHub · npm package
Due diligence
Before you install modlens
- Source of record: liustack/modlens — present in the community registry that DSH's own plugin market installs from.
- Licensed under MIT.
- Detected: terminal surface. Read the source before granting these.
- A listing here is not a security review. Plugins run with your agent's permissions.
What modlens does
modlens is a DeepSeek Harness plugin that gives text-only DeepSeek and GLM models an image-reading path. It handles images pasted into the chat or supplied by path, then returns structured JSON evidence such as OCR, reading-order layout regions, entities and relations. The plugin uses the native modlens_read_image tool. In the standard paste flow, the host places the image in a private temporary file and puts its path in the composer. A (modlens vision) model entry instead keeps the thumbnail in the message and converts it to evidence at request time. ModLens discovers provider routes carrying text-only models and creates wrapped entries for them, while leaving confirmed vision models on their native paste path.
It is intended for agents whose selected model cannot inspect images, provided at least one vision engine is reachable. The README describes built-in providers and reuse of existing logins from Claude Code, Codex, OpenCode or Pi; reads can therefore consume an API key, endpoint quota or a reused local CLI login. The terminal surface matters: setup and operation may inspect local harness CLIs and invoke their authenticated routes, and pasted images pass through a temporary local file. It is the wrong choice when the selected model already has native vision support, when image data must not be sent to an external vision engine, or when no configured provider or reusable CLI can read images.
modlens documentation
How it behaves
modlens_read_image is the native tool exposed to the text-only model. A pasted image can arrive as a private temporary-file path in the composer, or through a selected (modlens vision) model entry, where the thumbnail remains in the message until request time. The result is structured evidence intended to ground the model’s answer in what the image contains. A path can also be supplied directly in chat.
Model routing
The plugin auto-discovers provider routes whose metadata positively confirms that they carry text-only DeepSeek or GLM models. It adds a wrapped (modlens vision) entry for each such route. Confirmed native vision models are excluded, and unconfirmed models are left alone. A stock setup is documented as exposing DeepSeek-V4-Flash (modlens vision) and DeepSeek-V4-Pro (modlens vision); additional routes such as opencode-go or zai can receive their own entries.
Vision engines
The README describes six built-in providers and four reusable local agent CLIs. Documented provider examples include gemini-api, which needs a Gemini API key, openai, which takes an OpenAI-compatible key, baseUrl and model, and anthropic, which needs an Anthropic API key. antigravity-cli is also documented as a no-key channel after sign-in. Existing logins in Claude Code, Codex, OpenCode and Pi can be offered for reuse, with each reused read labelled by the quota it used.
Local access and operation
The setup checks available local credentials and asks before reusing them. The documented Antigravity sign-in command is agy. The plugin can inspect local harness CLI availability and invoke an approved route, so its terminal and credential access should be reviewed alongside the image data sent to the selected provider.
Written from the project's own documentation and kept in sync with it. Where the two disagree, the source is authoritative — read the README on GitHub
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