Workspace Genie

Workspace Genie is a lightweight Visual Studio Code extension that helps generate documentation for codebases using a language model. It scans files in the open workspace, sends contextual prompts to a model, and writes the model-produced documentation into workspace output files.

Primary goals:

• Make it easy to generate per-file documentation from natural-language prompts.
• Provide a clear demo of integrating VS Code chat participants and language models.
• Keep the implementation concise and easy to adapt for demos or forks.

Highlights

• Workspace scanning and file discovery: utils/fs_scanner.js.
• Model invocation and streaming capture: utils/model_pipeline.js.
• Lightweight logging: utils/scribe.js (append-only timestamped log).
• VS Code command and chat participant: genie.processWorkspace and genie.assistant.

Chat Participant API

Genie demonstrates a simple Chat Participant API implemented in extension.js. The extension registers a chat participant that can be invoked from VS Code's chat UI. The participant listens for short trigger commands (messages that start with @genie) and responds by running the same document-processing pipeline the command palette action uses. Two example chat triggers supported by the extension are:

• @genie : Starts an interactive chat with Workspace Genie and uses the prompt + current workspace context to generate documentation or suggestions.
• @genie /createDocs : Explicitly instructs Genie to scan the workspace and create documentation files (same behavior as genie.processWorkspace).

Because the chat participant is a thin adapter over the existing model_pipeline and fs_scanner utilities, the same pattern can be reused for other conversational features (for example: code reviewers, changelog writers, test-case generators, or refactoring assistants). To add a new capability, implement a new command or chat trigger, reuse the existing context-collection helpers, and provide a different instruction template to the model.

Reusability and extending functionality

The architecture intentionally separates responsibilities into small modules (fs_scanner.js, model_pipeline.js, scribe.js) and a lightweight extension entry (extension.js). This makes it straightforward to reuse the chat participant integration for additional features:

• New chat commands: Add additional @genie triggers that map to different templates (e.g., @genie /review, @genie /generateTests).
• Different output flows: Swap or extend model_pipeline to support other output formats (issues, unit tests, PR descriptions) while keeping the chat interface identical.
• Composable prompts: Create templates that combine file context, recent commit messages, or test failures before sending to the model.

Testing note

The current implementation and automated tests were validated only with GPT-family models (models compatible with GPT-style prompting and streaming). If you use non-GPT model providers or vendor-specific APIs, you may need to adapt the model_pipeline.js integration and prompt/response handling to match those providers' conventions.

Requirements

• Node.js (v16+)
• Visual Studio Code (recent stable release)

Distribution

• The extension is also available on the Visual Studio Marketplace (search for "Workspace Genie").

Installation

npm install

Usage (quickstart)

1. Open the repository folder in VS Code.
2. Run the command palette action: Process Workspace for Documentation (genie.processWorkspace).
3. Enter a prompt describing the documentation you want (e.g., "Create a short developer-facing README and usage examples").
4. Generated documentation files are written to the genie_outputs/ folder at the workspace root. Workflow events and errors are appended to genie-log.txt.

Notes on behavior

• The extension keeps chat history in memory and builds a small prompt that includes the target file's content before sending it to the configured language model.
• Outputs are versioned: if genie_outputs/<filename>.md already exists, the code will create _<n> suffixes to avoid overwriting.

File structure (important files)

workspace-genie/
├── extension.js                # VS Code extension entry (chat participant + command)
├── utils/
│   ├── fs_scanner.js          # Recursively find workspace files
│   ├── model_pipeline.js      # Send file contents to model and save outputs
│   └── scribe.js              # Timestamped logging helper
├── images/
│   └── genie_banner.svg       # Banner for social/marketing
├── scripts/
│   ├── GENIE_BANNER_EXPORT_INSTRUCTIONS.txt
│   └── merge_banner.js        # (optional) script to composite images using sharp
├── test/
│   └── extension.test.js      # Simple, local-run tests
├── package.json
├── README.md
└── LICENSE

Development

• Run lint:

npm run lint

• Run the local test script (no VS Code integration required):

npm test

Exporting the banner image

If you need PNG/JPEG versions of the banner, see scripts/GENIE_BANNER_EXPORT_INSTRUCTIONS.txt. Common commands use ImageMagick, Inkscape, or Node + sharp. Example (ImageMagick):

# convert SVG to PNG (1200x627)
magick convert images\genie_banner.svg -background none -resize 1200x627 images\genie_banner.png

Security & privacy

• The extension reads files from the workspace and sends content to a configured language model. Do not use it on sensitive or private repositories without reviewing the model provider's data handling policy.

Contributing

• Open issues or PRs with focused changes. Keep changes small and explain your motivation.
• If you add features that call external services, document required environment variables and auth steps.

License

This project is available under the MIT License — see the LICENSE file.

Contact / attribution

If you'd like me to prepare a short LinkedIn post and packaging (zip + images) for you to share, I can produce that next.

Example prompts

Here are a few sample prompts to try with Workspace Genie:

• "Create a short README for this file explaining purpose, usage, and example code snippets."
• "Summarize the responsibilities of the functions in this file and list potential edge-cases."
• "Generate unit test ideas for the following code and include one example test."
• "Write a high-level architecture summary for this project suitable for new contributors."
• "Produce a short changelog entry describing recent refactors and why they were done."

Troubleshooting

• "No models found" or model selection errors:

  • Ensure you have configured a language model provider in your VS Code environment. Some VS Code language model APIs require vendor-specific setup.

• Network or authentication errors when calling the model:

  • Check network access and any API keys required by your model provider; avoid exposing sensitive keys in the workspace.

• Permission denied when writing to genie_outputs/:

  • Ensure your workspace is writeable and that no external process is locking files. On Windows, run VS Code as the same user.

• Large repositories are slow to scan:

  • Exclude large directories (node_modules, build folders) from your workspace or adapt utils/fs_scanner.js to skip additional paths.

• Tests fail locally:

  • Run npm run lint first to catch code-style issues. The included test is a minimal sanity check; more robust integration tests require a configured VS Code test environment.