Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/proofsh/obsidian-notes-rag/claude-mdgit clone --depth 1 https://github.com/proofsh/obsidian-notes-ragWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01233 | $0.01233 |
| Opus 5 | $0.00616 | $0.00616 |
| Sonnet 5 | $0.00247 | $0.00247 |
| Haiku 4.5 | $0.00123 | $0.00123 |
Grade A, and why
obsidian-notes-rag CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
obsidian-notes-rag is an MCP (Model Context Protocol) server that provides semantic search over Obsidian notes. It uses OpenAI embeddings by default (or Ollama/LM Studio for local processing) with sqlite-vec for vector storage.
PyPI: https://pypi.org/project/obsidian-notes-rag/ GitHub: https://github.com/proofgeist/obsidian-notes-rag
Commands
# Install dependencies
uv sync --dev
# Run tests
uv run pytest -v
# Type checking
uv run pyright
# Interactive setup wizard
uv run obsidian-rag setup
# Index vault (manual refresh)
uv run obsidian-rag index
# Run the MCP server (stdio transport)
uv run obsidian-rag serve
# Watch vault for changes
uv run obsidian-rag watch
# Search from CLI
uv run obsidian-rag search "query"
obsidian-rag similar "Path/To/Note.md"
obsidian-rag context "Path/To/Note.md"
Architecture
Data Flow
Obsidian Vault → VaultIndexer → Embedder (OpenAI/Ollama/LMStudio) → VectorStore (sqlite-vec)
↓
MCP Client ← MCPServer (mcp 2.x) ← search_notes/get_similar/etc.
Key Components (src/obsidian_rag/)
- config.py:
Configdataclass,load_config()/save_config()for TOML config file, cross-platform paths viaplatformdirs - indexer.py:
VaultIndexerscans markdown files,chunk_markdown()uses Chonkie RecursiveChunker with markdown-aware rules,OpenAIEmbedder/OllamaEmbedder/LMStudioEmbeddergenerate embeddings,create_embedder()factory selects provider - store.py:
VectorStorewraps sqlite-vec with KNN vector search, two tables (chunks metadata + chunks_vec virtual table), handles upsert/delete by file path. Thread-safe (check_same_thread=False+threading.Lock). - server.py: MCPServer (mcp 2.x decorator API, successor to FastMCP) exposing 5 tools:
search_notes,get_similar,get_note_context,get_stats,reindex - watcher.py:
VaultWatcheruses watchdog with debouncing (default 2s) to incrementally re-index on file changes - cli.py: Click-based CLI with
setupwizard,--provideroption, commands for indexing, searching, similar, context, watching, and service management
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 114 lines · 1,233 tokens per session scan A 04f3dcce6ddd
obsidian-notes-rag CLAUDE.md is an instructions file published in the GitHub repository proofsh/obsidian-notes-rag (15 stars, last pushed 6d ago), licensed MIT. It adds 1,233 tokens to every session, about $0.0062 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Instructions for Azure/GPT-RAG, a project described as: Sharing the learning along the way we been gathering to enable Azure OpenAI at enterprise scale in a secure manner. GPT-RAG core is a Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large…
GPT-RAG release.instructions.md
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