omni-lpr: Instructions file for Codex

AGENTS.md

omni-lpr AGENTS.md is an instructions file for Codex, OpenCode from habedi/omni-lpr. It costs 1,309 tokens per session, scanned A, original, MIT.

Repository instructions for Omni-LPR, a self-hosted service that reads vehicle license plates from images or video. They cover the project’s mission, priorities, coding rules, writing style, and file layout.

In plain words
What is it for?
Working on license-plate detection and text recognition, REST or MCP endpoints, model settings, tests, logging, and schema validation.
Why use it?
They help agents preserve correctness, standards, test coverage, logging, and validation while changing a system that exposes REST and MCP interfaces.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md.

This is habedi/omni-lpr's own configuration. It tells Codex and OpenCode how to work on omni-lpr itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything omni-lpr configures →

Reuse

Borrowing it

Nothing to install: this file belongs to habedi/omni-lpr. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/habedi/omni-lpr/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/habedi/omni-lpr

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.

agentmods badge for omni-lpr AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/habedi/omni-lpr/agents-md.svg)](https://agentmods.dev/instructions/habedi/omni-lpr/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/habedi/omni-lpr/agents-md"><img src="https://agentmods.dev/badge/instructions/habedi/omni-lpr/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,309 This file is loaded in full into every session.
When invoked 1,309 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.01309 $0.01309
Opus 5 $0.00655 $0.00655
Sonnet 5 $0.00262 $0.00262
Haiku 4.5 $0.00131 $0.00131

Measured 8d ago against content hash bcf6e5fea13c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

omni-lpr 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 8d 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 · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

This file provides guidance to coding agents collaborating on this repository.

Mission

Omni-LPR is a self-hostable server that provides automatic license plate recognition (ALPR) capabilities. It exposes processing endpoints through a REST API and the Model Context Protocol (MCP), enabling integration with general software applications and AI agents.

Priorities, in order:

  1. Correctness of license plate detection, text recognition, and model configurations.
  2. Standard-compliant implementations of the REST API and the MCP interface.
  3. Test suite coverage, including unit, integration, and E2E cases.
  4. Structured logging, detailed error handling, and schema validation.

Core Rules

  • Use English for code, comments, docs, and tests.
  • Prefer small, focused changes over broad refactoring.
  • Add comments only when they clarify non-obvious behavior.
  • Do not add features, error handling, or abstractions beyond what is needed for the current task.
  • Keep dependencies small. Do not add heavy ML libraries, external packages, or extra services without prior discussion.

Writing Style

  • Use Oxford commas in inline lists: "a, b, and c" not "a, b, c".
  • Do not use em dashes. Restructure the sentence, or use a colon or semicolon instead.
  • Avoid colorful adjectives and adverbs. Write "graph generator" not "powerful graph generator".
  • Prefer using noun phrases for checklist items, not imperative verbs. Write "negative weight detection" not "detect negative weights".
  • Headings in Markdown files must be in title case: "Build from Source" not "Build from source". Minor words (a, an, the, and, but, or, for, in, on, at, to, by, of, from, and with) stay lowercase unless they are the first word.
  • Write correct and complete sentences.
  • Avoid made-up words, abbreviations, and colons in the middle of sentences.
  • Don't use pretentious language and made-up words.

Repository Layout

  • src/omni_lpr/__init__.py: Package initialization and metadata.
  • src/omni_lpr/__main__.py: CLI entry point and server startup logic.
  • src/omni_lpr/api_models.py: Pydantic data schemas for API requests and responses.
  • src/omni_lpr/errors.py: Exception definitions and API error handlers.
  • src/omni_lpr/event_store.py: Client event log store.
  • src/omni_lpr/mcp.py: MCP server implementation.
  • src/omni_lpr/rest.py: Starlette REST API endpoints and Swagger documentation configuration.
  • src/omni_lpr/settings.py: Application configuration settings parsing environment variables.
  • src/omni_lpr/tools.py: Core ALPR operations, model management, and backend interfaces.
  • tests/: Test suite containing unit, integration, and end-to-end test cases.
  • examples/: Client integration examples for the REST and MCP endpoints.
  • docs/: Repository documentation.
  • Dockerfile: Deployment container definitions.
  • Makefile: Script runner definitions for development tasks.

Read the full file on GitHub · 134 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.

  1. 8d ago First seen · 134 lines · 1,309 tokens per session scan A bcf6e5fea13c

Subscribe to this mod's changes

omni-lpr AGENTS.md is an instructions file published in the GitHub repository habedi/omni-lpr (26 stars, last pushed 2mo ago), licensed MIT. It adds 1,309 tokens to every session, about $0.0065 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.

Related

Other instructions, from other repositories

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,153 tokens

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

next.js AGENTS.md

AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,469 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).

microsoft/vscode · 5,001 tokens

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens