Summary

This project addresses the high maintenance overhead of traditional wikis by leveraging Large Language Models (LLMs) to automate knowledge management tasks. It targets users of Obsidian seeking to build a persistent, compounding personal knowledge base where LLMs handle cross-referencing, consistency checks, and content updates. The core benefit is transforming a knowledge base into a self-maintaining, growing asset.

How It Works

The system employs a three-layer architecture: immutable raw/ data sources, an LLM-maintained wiki/ layer for structured knowledge, and a TheSchema.md configuration file defining rules and workflows. Operations include Ingest (adding new data to raw/ and having the LLM process it), Query (asking questions for synthesized, cited answers), and Lint (auditing the wiki for consistency, contradictions, and isolated pages). This approach offloads tedious "bookkeeping" to LLMs, enabling knowledge to compound over time.

Quick Start & Requirements

1. Download the latest release .zip file from the Releases page.
2. Extract the contents into your Obsidian vault folder.
3. Open the folder as a vault in Obsidian.
4. Configure your chosen AI Agent (e.g., Claude Code, OpenClaw, Trae) by providing it with the rules in TheSchema.md.
   • Prerequisites: Obsidian, a compatible AI Agent.

Highlighted Details

• Automated maintenance of cross-references, consistency, and content updates via LLM.
• Structured knowledge organization into source summaries, entities, concepts, comparisons, and overviews.
• Full traceability through operation logs and source citations.
• Native integration with Obsidian features like bidirectional links, tags, and graph view.
• Knowledge base acts as a persistent, compounding asset.

Maintenance & Community

The project is authored by "Blink," described as an 18-year note-taking software veteran and AI + knowledge management practitioner. No specific community links (Discord, Slack) or roadmap details are provided in the README.

Licensing & Compatibility

The project is released under the MIT License. This permissive license generally allows for commercial use and integration into closed-source projects, with minimal restrictions beyond attribution.

Limitations & Caveats

The system's effectiveness is contingent on the capabilities of the chosen AI Agent and the accuracy of the TheSchema.md configuration. The README does not provide performance benchmarks or specific hardware requirements beyond those typically associated with running Obsidian and an AI agent.