What is ezrag AGENTS.md?

Repository instructions for EzRAG, an Obsidian plugin that adds semantic search using Google Gemini. Semantic search finds content by meaning rather than only matching exact words.

Use them when changing EzRAG’s indexing, file processing, queues, connection handling, or Gemini integration.

They give newcomers the project map, key design concepts, and commands for developing and testing the plugin.

ezrag AGENTS.md — Instructions file for Codex and OpenCode by benbjurstrom: Repository instructions for EzRAG, an Obsidian plugin that adds…

Install

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.

agentmods

npx agentmods add instructions/benbjurstrom/ezrag/agents-md

Clone the repo

git clone --depth 1 https://github.com/benbjurstrom/ezrag

Made for: Codex, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/benbjurstrom/ezrag/agents-md.svg)](https://agentmods.dev/instructions/benbjurstrom/ezrag/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/benbjurstrom/ezrag/agents-md"><img src="https://agentmods.dev/badge/instructions/benbjurstrom/ezrag/agents-md.svg" alt="Measured on agentmods" height="20"></a>

Per session 6,960 This file is loaded in full into every session.

When invoked 6,960 The same file — it is already loaded in full.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What 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	$0.06960	$0.06960
Opus 5	$0.03480	$0.03480
Sonnet 5	$0.01392	$0.01392
Haiku 4.5	$0.00696	$0.00696

Measured 5d ago against content hash 23256c03e656, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ezrag AGENTS.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 5d 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.

AGENTS.md · 718 lines

How it starts

The opening of the file, as written. The whole thing — 718 lines — stays where its author put it; the contents beside it link to each section on GitHub.

EzRAG - Obsidian Plugin for Semantic Search via Google Gemini

Quick Start for Developers

New to this codebase? Start here:

Read this file for high-level overview and module guide
Read ARCHITECTURE.md for detailed design, data models, and implementation notes
Key entry points:
- main.ts - Plugin lifecycle (start here to understand initialization)
- src/lifecycle/indexingLifecycleCoordinator.ts - Centralized runner/connection gating & store provisioning
- src/indexing/indexingController.ts - Indexing lifecycle management
- src/indexing/indexManager.ts - Core indexing logic
- src/indexing/filePreparationService.ts / documentMetadata.ts / documentReplacer.ts - Shared file ingestion helpers
- src/indexing/persistentQueue.ts - Queue orchestration, retries, and connection-aware scheduling
- src/gemini/geminiService.ts - Gemini API integration

Build and test:

npm install
npm run dev  # Watch mode for development

Critical concepts to understand:
- Runner pattern: Only one machine indexes per vault
- Hot path optimization: No remote checks during file changes
- Queue persistence: Uploads survive restarts
- Smart reconciliation: Rebuild doesn't create duplicates

What is EzRAG?

EzRAG is an Obsidian plugin that indexes your notes into Google Gemini's File Search API, enabling semantic search and AI-powered chat over your vault. Key features:

Automatic Indexing: Continuously syncs selected notes to Gemini as you edit
Smart Change Detection: Uses content hashing to avoid redundant uploads
Multi-Device Support: "Runner" pattern designates one machine to handle indexing
Chat Interface: Query your notes using natural language
MCP Server (planned): External tools can query your vault via Model Context Protocol

How It Works

The Runner Pattern (Critical Concept)

In multi-device setups (laptop + desktop), only one machine (the "runner") handles indexing:

Read the full file on GitHub · 718 lines

Changes

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.

5d ago First seen · 718 lines · 6,960 tokens per session scan A 23256c03e656

Subscribe to this mod's changes

ezrag AGENTS.md is an instructions file published in the GitHub repository benbjurstrom/ezrag (19 stars, last pushed 8mo ago), licensed 0BSD. It adds 6,960 tokens to every session, about $0.0348 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.

Data and AI RAG and vectors

GPT-RAG release.instructions.md

Instructions for Azure/GPT-RAG, a project described as: Enterprise-grade accelerator for agentic RAG on Azure. Built on Microsoft Foundry with Foundry IQ as the default retrieval backend, Microsoft Agent Framework orchestration, Zero-Trust architecture and IaC.

Azure/GPT-RAG · 207 tokens

gpt-rag-ingestion api-frontend.instructions.md

Instructions for Azure/gpt-rag-ingestion, a project described as: The GPT-RAG Data Ingestion service automates processing of diverse documents—PDFs, images, spreadsheets, transcripts, and SharePoint—readying them for Azure AI Search. It applies smart chunking, generates text and image embeddings, and enables rich…

Azure/gpt-rag-ingestion · 208 tokens

ezrag AGENTS.md