What is OpenRFM AGENTS.md?

Repository instructions for reproducing KumoRFM-2, a machine-learning model that works with relational data such as rows linked across tables. The project includes Python code, training scripts, benchmark adapters, datasets, and experiment outputs.

Use it when developing the reproduction, training or testing the model, connecting benchmark data, or comparing results with target outputs.

It shows where source code and generated artifacts belong and provides quick commands for checking syntax and running a small training test.

How T-Lab/OpenRFM configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to T-Lab/OpenRFM. 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/T-Lab/OpenRFM/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/T-Lab/OpenRFM

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/t-lab/openrfm/agents-md/github.svg)](https://agentmods.dev/instructions/t-lab/openrfm/agents-md)

Your own site

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

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/t-lab/openrfm/agents-md"><img src="https://agentmods.dev/badge/instructions/t-lab/openrfm/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 733 This file is loaded in full into every session.

When invoked 733 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.

Model	Per session	Once invoked
Fable 5.1	$0.00733	$0.00733
Opus 5.5	$0.00293	$0.00293
Sonnet 5.5	$0.00147	$0.00147
Haiku 4.5	$0.00073	$0.00073

Measured 2mo ago against content hash 4e0d67cdb550, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

OpenRFM 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 2mo 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 · 46 lines

How it starts

The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Repository Guidelines

Project Structure & Module Organization

This repository is a KumoRFM-2 reproduction scaffold. The main Python package lives in implementation/kumorfm_repro/, with model code in model.py, synthetic data in data.py, training in train.py, RelBench/file-backed adapters in benchmark_adapter.py and relational_io.py, suite runners in relbench_suite.py and synthetic_suite.py, and target comparison in targets.py. Documentation and status notes live in implementation/README.md and progress_plan/reproduction_status.md. The source paper is in technical_report/. Generated datasets, manifests, checkpoints, and comparisons are under implementation/data/, implementation/runs/, and implementation/targets/; treat these as artifacts, not source modules.

Build, Test, and Development Commands

Work from the implementation directory:

cd /home/user/work/KumoRFM/implementation
python -m compileall kumorfm_repro

Use compileall as the fast syntax/import smoke check. Run a small synthetic training smoke test with:

python -m kumorfm_repro.train --steps 3 --batch-size 2 --context-size 8 --rows-per-child 4 --d-model 32 --layers 1 --output-dir runs/single_smoke

For two-GPU DDP checks:

torchrun --standalone --nproc_per_node=2 -m kumorfm_repro.train --steps 4 --batch-size 4 --context-size 8 --rows-per-child 4 --d-model 32 --layers 1 --amp --output-dir runs/ddp_smoke

For RelBench presets, inspect available suites with python -m kumorfm_repro.relbench_suite --list-presets. Use benchmark_adapter train-model and relbench_suite as fine-tuning smoke paths only. Paper evidence should come from synthetic pre-training plus icl_eval/icl_suite.

Coding Style & Naming Conventions

Use Python 3 with 4-space indentation, type hints where they clarify interfaces, and descriptive snake_case names for functions, variables, and CLI flags. Keep modules CLI-friendly via python -m kumorfm_repro.<module>. Prefer structured manifests (.json) and parquet/csv table IO over ad hoc text formats. Keep comments short and reserved for non-obvious model, data, or distributed-training behavior.

Read the full file on GitHub · 46 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.

2mo ago First seen · 46 lines · 733 tokens per session scan A 4e0d67cdb550

Subscribe to this mod's changes

OpenRFM AGENTS.md is an instructions file published in the GitHub repository T-Lab/OpenRFM (22 stars, last pushed 5mo ago), licensed MIT. It adds 733 tokens to every session, about $0.0029 per session on Opus 5.5. 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.

OpenRFM AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
OpenRFM AGENTS.mdT-Lab/OpenRFM	Instructions file	22	5mo ago	MIT	A	733	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	today	MIT	A	9,719	GitHub Copilot
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	today	MIT	A	5,516	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	today	MIT	A	4,680	Codex, OpenCode
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	today	MIT	A	3,093	GitHub Copilot
vscode model-prompts.instructions.mdmicrosoft/vscode	Instructions file	195k	today	MIT	A	2,022	GitHub Copilot

OpenRFM: Instructions file for Codex