$ show AgriciDaniel/claude-obsidian
This repository is a local-first, source-cited knowledge base management system, delivered primarily as a Claude Code plugin and a standalone Python CLI tool. It manages Obsidian vaults with a strong emphasis on data integrity, provenance tracking, and deterministic operations. The architecture is a monolithic Python library (`claude_obsidian/`) with a formal transaction system for all vault mutations, a robust lint engine, and a retrieval pipeline (BM25 + optional semantic reranking). It features a sophisticated skill/agent framework for AI integration across multiple hosts (Claude, Cursor, Windsurf, etc.). While primarily an application, it incorporates significant data engineering and research data management (RDM) patterns. The provenance ledger system (`ledgers.py`) enforces strict schemas for sources and claims, including canonical URL handling, content-addressed identity, and evidence validation rules. The retrieval pipeline implements Anthropic's Contextual Retrieval pattern. This hybrid nature makes it a powerful tool for both knowledge management and structured research workflows. The codebase demonstrates exceptionally high craftsmanship in its transaction system, defensive I/O, and deterministic analysis tools. It is actively maintained with a comprehensive test suite and a formal release process. Its primary limitation is platform dependency on Linux/macOS for write operations (WSL required for Windows).
The LLM identified 5 files worth highlighting for reuse:
Core transaction engine implementing a robust inspect/approve/apply pattern for all vault mutations. Uses SHA-256 hashes for plan approval binding, journaling for crash recovery, and safe file I/O with directory file descriptors to prevent symlink attacks and race conditions. This is a masterclass in defensive filesystem programming and is the foundation of the vault's data integrity.
Defines and enforces strict schemas for source and claim ledgers. Includes canonical URL handling, content-addressed identity (SHA-256), temporal freshness rules, and evidence validation (e.g., accepted claims require non-synthetic support). This is a highly reusable data quality and provenance framework applicable to any research or knowledge management system.
A self-contained, deterministic implementation of `.gitignore` pattern matching in pure Python. Avoids subprocess calls to `git`, making it suitable for environments where `git` is unavailable or for deterministic analysis. Demonstrates careful handling of edge cases in gitignore semantics and is easily reusable.
Provides a strict JSON parser that rejects duplicate keys and non-finite numeric values. This is a critical security and data integrity primitive that is easily reusable across any Python project handling untrusted JSON input.
Implements a BM25 sparse retrieval index with a custom tokenizer that handles CJK languages via 1/2/3-gram segmentation. Includes stopword removal, Unicode normalization, and growth limits. Demonstrates non-obvious skill in information retrieval and multilingual text processing, forming the core of the vault's search capability.