Explore Project-Oriented Editing and Extensible AI/Tooling #360

Background

As AI workflows expand from individual documents to entire projects, the application may benefit from becoming more project-oriented.

The goal is not to turn the application into a general-purpose IDE. Instead, it should adopt the project-management capabilities that are useful specifically for Markdown authoring, document processing, transformation, and publishing.

At the same time, future AI capabilities may increasingly depend on external models, local services, tools, and integrations. A lightweight extension or integration model may therefore become useful.

Part 1: Project-Oriented Editor

Explore project-level capabilities such as:

• Opening and managing a folder or project.
• Navigating files through a project tree.
• Creating, renaming, moving, and deleting project files.
• Searching across the project.
• Understanding project-relative links and assets.
• Providing project-level context to AI workflows.
• Tracking files involved in AI tasks.
• Supporting document-centric project metadata or configuration.

The intention is to adopt useful project concepts from editors such as VS Code without reproducing the complexity of a full IDE.

Part 2: Extensions and Integrations

Explore whether the application should provide an area such as:

Appearance / Extensions / Integrations

This could allow users to discover and optionally enable additional capabilities without requiring complicated manual configuration.

Possible integrations might include:

• Local AI servers.
• Alternative AI providers.
• Document conversion or publishing tools.
• Translation services.
• Emerging AI protocols or tool ecosystems.
• Other document-processing services.

Design Principles

Any extension or integration system should prioritize:

• Simple installation and configuration.
• Clear permissions and user control.
• Security and isolation.
• Compatibility with the existing Work panel.
• Minimal impact on users who do not need extensions.
• Maintainability of the core application.
• The ability to evolve as AI tooling changes.

Suggested Acceptance Criteria

Because this is primarily an exploratory task, success does not necessarily require implementing a complete extension ecosystem.

The exploration should ideally produce:

• A clearer definition of which project-oriented features belong in the core application.
• A proposed architecture for integrations or extensions.
• A model for how extensions could expose capabilities to the AI Work panel.
• A permissions/security model for tools that can read or modify project content.
• An approach for discovering, installing, enabling, disabling, and configuring integrations.
• Identification of features that should remain built-in rather than becoming extensions.
• One small proof-of-concept integration if practical.

Important Considerations

An extension system introduces substantially more complexity than the earlier Work panel features.

In particular, it may introduce security, compatibility, installation, versioning, distribution, and maintenance concerns.

For that reason, this work should remain exploratory until the project-wide AI workflow has demonstrated enough value to justify the additional complexity.

Longer-Term Vision

If successful, the application could evolve into a document-focused AI workspace where users can:

• Work naturally with individual Markdown documents.
• Manage larger Markdown projects.
• Delegate multi-file tasks to AI.
• Run long-running document-processing workflows.
• Produce finished publications.
• Extend the environment with additional AI models and specialized tools.

The emphasis should remain on document-centric workflows rather than general-purpose software development.