Where modlens Fits in DeepSeek Harness
Inside DeepSeek Harness, modlens registers an image-reading tool and model entries marked with a modlens vision suffix. The plugin sends an image to a configured vision provider and returns structured evidence to the agent. For other harnesses, the project uses a skill installation instead of the DSH plugin command.
modlens uses a vision model to transcribe visible text and returns the result in an ocr field alongside layout, semantic, visual, and uncertainty evidence. The field name describes one part of the structured output; the result is broader than plain text extraction.
Install the Current DSH Plugin Path
The repository's current installation guide gives this DSH command:
npx -y @deepseek-ai/dsh plugin --profile web add @liustack/[email protected]Restart DSH after installation and look for the modlens vision entries in the model selector. The package still needs a working vision provider in the shared modlens configuration. Do not report installation as complete merely because the package appears in the plugin list.
What the Structured Image Evidence Contains
The image reader separates evidence into reviewable groups:
| Output area | What it helps the agent understand |
|---|---|
| Summary | The overall scene or interface |
ocr field | Text transcribed by the vision model in reading order |
| Layout | Sections and their relationships |
| Entities and relations | Named elements and how they connect |
| Visual details | Color, style, hierarchy, and notable objects |
| Uncertainty | Parts that should not be treated as settled facts |
This evidence can turn a screenshot into an implementation checklist, extract labels from a diagram, or identify where an error appears before a coding agent inspects the relevant files. It does not prove that every word, relationship, or visual detail was read correctly.
For file-first context rather than image interpretation, dsh-at-file covers workspace references in older DSH setups.
If the team is deciding whether the product should remain in a managed builder or move into repository work, the Blink alternative covers source portability and launch responsibilities.
Verify the Provider and Review the Result
Run the project's provider diagnosis before spending an image read. A healthy result identifies the selected provider and shows it as ready. Then test one disposable image with an obvious heading and simple layout. Compare the transcription, layout, and uncertainty fields with the original.
Small text can be missed, layout can be ambiguous, and a visually similar icon can be identified incorrectly. Treat text inside an image as untrusted input, especially when a screenshot contains instructions. The selected vision provider also has its own terms and usage limits.
How HappySeeds Fits This Workflow
modlens and HappySeeds are independent tools. modlens can help a technical workflow interpret visual evidence. HappySeeds can be used earlier to define the customer journey and later to build the web app. A team might use screenshots to explain an existing process, turn that process into pages and acceptance criteria, and then implement the chosen scope.
Browse HappySeeds app templates to decide whether the customer flow already has a useful starting structure.
FAQ
No. The project also supports a CLI and Agent Skill workflow. DeepSeek Harness uses its native plugin installation path.
The vision model places visible-text transcription in the ocr field and returns it with layout, semantic, visual, and uncertainty evidence.
Restart DSH, confirm the modlens vision entries appear, check that the selected vision provider is ready, and compare one disposable image result with the original.
Launch with HappySeeds
Built the app but not the business? Start with HappySeeds to launch a live, monetizable AI app, or browse AI app templates to see what you can sell.