Summary
The application “Obsidian Analyzer” inspects an Obsidian vault, suggests improvements, and automates link creation.
Core analysis features
MultiVaultAnalyzer performs a full-vault scan, discovering folders, analyzing them individually, finding cross-folder connections, and computing a health score. It returns a VaultAnalysis dataclass with aggregate metrics and folder breakdowns Generated reports summarize metrics like total notes, words, links, orphaned notes and more, exporting to Markdown for user review Automatic and safe linking
AutoLinker automatically inserts wiki links into notes based on content analysis suggestions SafeAutoLinker provides guarded link insertion with safety levels and backup creation to limit risk AI-powered capabilities
AISemanticLinker uses OpenAI to detect semantic relationships between notes and suggest bidirectional links where confidence is high ContentGapAnalyzer identifies missing content or bridge notes in the vault using AI analysis and can generate detailed gap reports Command‑line tools and examples
The scripts/analyze_vault.py CLI analyzes an entire vault and outputs a summary along with optional JSON/Markdown reports scripts/auto_link.py exposes auto-linking from the terminal with options for confidence threshold, dry-run mode, and interactive selection Additional scripts such as ai_safe_auto_link.py combine AI link suggestions with the safe auto-linker for cautious automated linking Overall, the project provides tooling to inspect an Obsidian vault’s structure, recommend improvements, automatically insert safe links, discover content gaps, and report on vault health. The application’s CLI scripts and example programs demonstrate workflows for vault analysis, auto-linking, and AI-assisted enhancements.
Implementation Summary
Install Python: The project targets Python 3.8 or newer as specified in pyproject.toml.
Dependencies: Required packages include openai, psutil, pytest, and others listed in the dependencies array. Development tools like black and flake8 are listed under optional dependencies.
Examples: A simple example script demonstrates analyzing a vault path and printing recommendations for notes.
CLI interface: The scripts/analyze_vault.py script provides a command-line interface with options for specifying the vault path, folders to analyze, and output location.
Test configuration: Pytest looks for tests under the tests directory and defines several custom markers.
Notes
The project README summarizes the core capabilities and how it is structured. Lines 1‑22 describe the main analyzers, automatic linking, AI‐powered features, and CLI scripts. Installation guidance in the README notes that the project requires Python ≥3.8 and lists key dependencies (e.g., openai, psutil, pytest). These dependencies also appear in pyproject.toml under [project] and [project.optional-dependencies]. The package exposes its main entry points in init.py so they can be imported directly. Example usage for analyzing a vault and requesting note recommendations is shown in examples/basic_usage.py. The command‑line interface for multi-folder analysis resides in scripts/analyze_vault.py, which prints a summary and can export Markdown and JSON reports. Summary
The repository contains “Obsidian Analyzer,” a toolset for inspecting and improving an Obsidian vault. It analyzes folders, finds cross-folder links, computes a vault health score, and can automatically add wiki links. AI helpers detect semantic relationships between notes and locate content gaps. The project includes scripts for vault analysis, auto-linking, and safe link insertion, along with example programs and test suites. Installation requires Python 3.8 or newer and the dependencies listed in pyproject.toml. After installing, users can run the provided CLI scripts or use the Python API to analyze notes, generate reports, and safely insert suggested links.