realmyang / MLView
git clone https://github.com/realmyang/MLView.git
Interactive, source-linked ML workflow diagrams written by your coding assistant (Copilot, Codex or Claude Code) and opened in VS Code.
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LanguagePython
LicenseMIT
Created2026-09-08 (1mo ago)
Last push17d ago
Watchers16
OwnerUser
GitHub topics
agent-skillsclaude-codecodexdeveloper-toolsgithub-copilotmachine-learningvisualizationvscode-extension
Links
- GitHub: github.com/realmyang/MLView
- Homepage: https://github.com/realmyang/MLView#readme
- Releases · Issues · Hacker News mentions
README excerpt
# MLView **Use the LLM in your existing VS Code assistant to understand an ML workflow, then explore its source-linked interactive diagram.** MLView provides a skill for GitHub Copilot, Codex, and Claude Code, a common artifact format, and a VS Code viewer. Your assistant reads the source and configuration, interprets the workflow, and authors the diagram and findings. MLView requires no separate model API key. The selected assistant supplies the model, tools, permissions, and source-processing policy. Local helpers validate citations and structure; the viewer displays the result. Neither helper nor viewer executes the analyzed program. Citation validation does not prove the model's interpretation correct. This is an **experimental implementation**. See implementation and validation status for what has actually been tested. Workflow interpretation and findings come from the active assistant model. ## Get a diagram The detailed install and refinement instructions are in LLM_WORKFLOW.md. From this checkout, with Python 3.10+ and Node 20.18.1+: [code] Install the generated VSIX using VS Code's **Extensions: Install from VSIX**. The installer copies the self-contained skill into the target project's `.agents/skills/mlview/` directory for Codex and Copilot. For Claude Code, use the Claude plugin or the workspace install described in the runbook. Only install one copy of the skill in each discovery path. 1. Open the target project in VS Code and invoke `mlview` in your assistant's skill picker (`$mlview` in Codex). Ask, for example: “Explain training with this config. Show data, model, losses, parameter updates, validation, and anything unresolved.” 2. The assistant publishes a `*.mlview.json` file. Click it in the Explorer: it opens as the diagram (to see the JSON, use **View: Reopen Editor With…** → **Text Editor**). **MLView: Open Generated Diagram** opens it beside the editor you are in (or brings its open diagram to the front of its own group). Click a step, connection or finding to see its claim and quotes. Press Enter, double-click, or use an **Open** link to open the cited lines beside the diagram; focus stays in the diagram. Alt+Enter opens them and moves focus to the editor. 3. Ask the same assistant to refine the diagram. A valid new revision updates the panel; filtering and navigating the existing diagram do not call an LLM. To refine a particular node, edge, or finding, select it and use **Refine → Copy prompt**. Choose an intent or enter a specific question, then paste the prompt into the same assistant. The header is one row: the title, the assistant and revision, search, the severity toggles, **N not observed**, **Review**, a **⋯** menu (legend, flow animation, fit, exports, shortcuts) and **Refine…**. In a narrow panel, such as beside your code, search, the revision and **Review** move into the **⋯** menu, and a row that is still too full folds **N not observed** into it too, so **Refine…** stays in view. The status bar co