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/gkmngrgn/rayt/gemini-mdgit clone --depth 1 https://github.com/gkmngrgn/raytWrote 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.
[](https://agentmods.dev/instructions/gkmngrgn/rayt/gemini-md)<a href="https://agentmods.dev/instructions/gkmngrgn/rayt/gemini-md"><img src="https://agentmods.dev/badge/instructions/gkmngrgn/rayt/gemini-md.svg" alt="Measured on agentmods" height="20"></a>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 5.1 | $0.00563 | $0.00563 |
| Opus 5 | $0.00282 | $0.00282 |
| Sonnet 5 | $0.00113 | $0.00113 |
| Haiku 4.5 | $0.00056 | $0.00056 |
Grade A, and why
rayt GEMINI.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 yesterday.
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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Overview
This project is a ray tracer implementation based on Peter Shirley's "Ray Tracing in One Weekend" book series. It's a hybrid Python and Rust application, with the performance-critical ray tracing logic written in Rust and exposed as a Python extension using pyo3 and maturin. The Python part of the project provides a command-line interface (CLI) using the click library and leverages numba for CPU-based performance optimization and numba-cuda for GPU acceleration.
The project is structured to allow rendering on different backends:
- Numba (CPU): Utilizes Numba's JIT compilation to accelerate the Python code on the CPU.
- CUDA (GPU): Offloads the rendering process to NVIDIA GPUs using Numba's CUDA support.
- Rust: The core ray tracing logic, including vector operations, hittable objects, materials, and camera, is implemented in Rust for maximum performance.
Building and Running
The project uses uv as a package manager. The following commands are available to build and run the ray tracer:
Basic Rendering (CPU)
uv run one-weekend --image-width=300 --samples-per-pixel=20 > image.ppm
High-Quality Rendering (CPU)
uv run one-weekend --image-width=1200 --samples-per-pixel=100 > image.ppm
GPU Rendering
To use the GPU-accelerated renderer, you need a CUDA-enabled NVIDIA GPU and the necessary drivers.
uv run one-weekend --image-width=300 --samples-per-pixel=20 --engine=gpu > image.ppm
Testing GPU Availability
You can check if a compatible GPU is available with the following command:
uv run test-gpu
Development Conventions
- Hybrid Python/Rust: The project follows a hybrid development model. The core, performance-sensitive code is in Rust (
src/), while the CLI, rendering orchestration, and optimization bindings are in Python (src/rayt/). - Build System:
maturinis used to build the Rust code into a Python extension. The configuration is inpyproject.toml. - Dependencies: Python dependencies are managed with
uvand are listed inpyproject.toml. Rust dependencies are managed withcargoand are listed inCargo.toml. - CLI: The command-line interface is built with
clickand is defined insrc/rayt/cli.py. - Code Style: The Python code follows standard Python conventions. The Rust code follows standard Rust conventions.
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.
- yesterday First seen · 50 lines · 563 tokens per session scan A e9bf96d45c0d
rayt GEMINI.md is an instructions file published in the GitHub repository gkmngrgn/rayt (6 stars, last pushed 10mo ago), licensed MIT. It adds 563 tokens to every session, about $0.0028 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 2026-09-04.
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