Building an Agentic OS with Claude Code and Obsidian

The Concept of an Agentic OS

Instead of treating Obsidian as a passive note-taking tool, this workflow turns it into a visual and functional wrapper for Claude Code. By centralizing terminal access, metrics, and automations within a local vault, the user creates a "Command Center" that provides insights and control beyond what the standard desktop app or terminal offers. The system is designed to be fully customizable, allowing users to visualize specific data points like token usage, calendar schedules, or automated research reports.

Voice Architecture and Routing

The system utilizes a local voice-to-text pipeline to enable hands-free interaction. The architecture follows a three-tier routing logic:

1. Skills (Tier 1): Direct execution of predefined tasks (e.g., running a morning report).
2. Metrics (Tier 2): Queries against existing data or reports that do not require new research.
3. Headless Execution (Tier 3): Complex, multi-step tasks that trigger Claude Code in the background to perform deep research or project planning.

Transcription is handled locally via Faster Whisper, while routing is managed by Claude Haiku (chosen for speed and cost-efficiency). Responses are converted back to speech using Kokoro.

Building a Skill and Automation Backbone

The core of the system is the codification of daily tasks into repeatable skills. The recommended process for building this library is:

1. Stream of Consciousness: Record a verbal explanation of daily/weekly workflows and have Claude analyze them to identify potential skills.
2. Log Analysis: Review historical Claude Code logs to identify actual usage patterns rather than theoretical ones.
3. Refinement: Implement these as manual skills first to ensure reliability before promoting them to automated routines.

Structured Memory via Obsidian

Rather than relying on complex RAG architectures, the system uses a structured folder hierarchy (e.g., Raw, Wiki, Output) combined with "Master Index" markdown files. This acts as a map for the AI, allowing it to navigate thousands of files efficiently. By providing a clear directory structure and explicit navigation instructions within the claudemc configuration, users can reduce token consumption and improve the speed and accuracy of the AI's responses.