mastisk

To automate your personal wiki, you use autonomous agents that ingest, classify, and synthesize multi-source data like RSS feeds and GitHub activity into structured articles. Mastisk automates this collection and synthesis process, linking related notes to maintain a persistent knowledge graph.

RAG-backed question answering for personal knowledge bases lets you query your synthesized wiki articles and notes with answers grounded in your personal identity and preferences. Mastisk facilitates this by retrieving context from your local knowledge graph to answer research questions.

Yes, this knowledge management skill integrates with local LLM backends and iCloud-synced markdown vaults to maintain a persistent, searchable knowledge graph. Mastisk uses these local integrations to ensure your synthesized articles and research backlog remain accessible and privately synced.

Classifying and synthesizing notes into a searchable knowledge graph requires autonomous agents to process multi-source data and automatically link related notes. Mastisk orchestrates agents that ingest raw inputs, classify them, and generate synthesis pages to surface open research questions.

Yes, you can use autonomous agents to manage a research backlog across articles, notes, and podcasts by ingesting and synthesizing multi-source data. Mastisk facilitates research backlog management by automatically collecting data from RSS feeds and podcasts into structured wiki articles.

Limitations of using local LLMs for personal knowledge synthesis include relying on the processing capacity of your local hardware and the need for manual markdown vault synchronization. Mastisk mitigates this by integrating with iCloud-synced vaults but still requires local backend availability for RAG-backed Q&A.