corrinehu/dsh-chat-imagine

dsh-chat-imagine

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Automatically call image-generation tools from the DeepSeek Harness (DSH) chat window (via API channels, or the local CLIs: mmx / codex / agy), display the generated image inline, and also recognize images using the corresponding CLI.

Overview

Two image-generation methods are supported:

API

• Uses OpenAI-compatible providers already configured in DSH and finds their available image models.

• For built-in providers (e.g. OpenRouter) whose base URL is left blank in DSH settings, the plugin falls back to DSH's built-in default endpoint, matching how chat routing resolves them.

CLI

The plugin scans the local machine for the MiniMax CLI (mmx), the OpenAI Codex CLI (codex), and the Google Antigravity CLI (agy); each one found becomes an available image-generation and image-recognition backend.

Image Generation

• Calling codex spends your ChatGPT account (Plus/Pro) quota rather than an API key. Requires the codex CLI installed and signed in to an account with image quota left (check with codex login status).
• Calling agy spends your Google account quota. Requires the agy CLI installed and signed in via the Antigravity app.
• When codex / agy is detected, the plugin also registers the skill cli-image-gen, which teaches the model to drive the CLIs for image generation when the generate_image tool fails (quota / region restrictions / parsing), and to finish with inline display via show_image_file.

Image Recognition

• Recognition uses the same CLI channels' vision capabilities (mmx's vision describe, codex's exec -i with image input + server-enforced JSON schema, agy's --json-schema). So installing any one of mmx / codex / agy enables image recognition — no need to install all three; if none is installed the plugin still works, only analyze_image returns a "CLI not found" notice.
• Reads an image (local path or http(s) URL) into structured JSON evidence: OCR full text and per-line text, reading-order layout regions, semantic entities and relations, visual notes, and an uncertainty list — any model (including text-only) can call it directly, with no vision-model switching.
• Channel auto-picks by speed (mmx → codex → agy); you can also pin a default with the visionBackend parameter of set_image_default.

Install

# npm (recommended; ships prebuilt artifacts)
dsh plugin --profile web add dsh-chat-imagine

# or install from the GitHub source
dsh plugin --profile web add github:corrinehu/dsh-chat-imagine

Usage

After installing the plugin, start a new conversation and ask for an image:

Create a cute blue whale logo.

The plugin checks available channels and models, then asks which one to use as the default:

Once set, you don't need to choose again. Just describe the image you want in the chat:

Generate a 16:9 sunrise over snowy mountains.

The result appears directly in the chat.

You can also use another image backend:

Just say so in the conversation, for example:

Use agy to generate a widescreen hand-drawn colored-pencil diagram explaining LLM post-training.

Image Recognition

Image recognition requires a local CLI: with any one of mmx / codex / agy installed, the analyze_image tool is available (any one suffices — no need to install all three); if none is installed the plugin still works, only image recognition is unavailable — calls return a "CLI not found" notice. Once a CLI is present, the tool reads an image (local path or http(s) URL) into structured JSON evidence — OCR full text and per-line text, layout regions in reading order, semantic entities and relations, visual notes, and an uncertainty list.

Read this image /tmp/screenshots/error.png and copy out the error text verbatim.

• Any model can use it: the tool drives the vision models on the CLI channels (MiniMax VLM / ChatGPT / Gemini); the current session does not need to switch to a vision model — the key difference from route-taking-over solutions like modlens.
• Contract ported from modlens: the same five-part evidence structure, deliberately without bounding boxes and confidence (the two fields vision models most easily fabricate).
• Channel selection: mmx (fastest, direct VLM, ~3-8s) → codex (server-enforced JSON schema, most reliable) → agy (Gemini, weekly quota shared). Pin a default with the visionBackend parameter of set_image_default; otherwise it auto-picks by speed.
• Graceful degradation: when a channel runs out of quota, say in the conversation to switch (the backend parameter, or just "use codex").

Notes

• Currently tested only in the DSH Web profile.
• Images are kept in DSH process memory. Historical image links stop working after restart; save images you want to keep from the chat UI.

License

MIT