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/t-lab/openrfm/claude-mdgit clone --depth 1 https://github.com/T-Lab/OpenRFMWrote 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/t-lab/openrfm/claude-md)<a href="https://agentmods.dev/instructions/t-lab/openrfm/claude-md"><img src="https://agentmods.dev/badge/instructions/t-lab/openrfm/claude-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 | $0.04694 | $0.04694 |
| Opus 5 | $0.02347 | $0.02347 |
| Sonnet 5 | $0.00939 | $0.00939 |
| Haiku 4.5 | $0.00469 | $0.00469 |
Grade A, and why
OpenRFM 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 4d 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 — 204 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.
Overview
KumoRFM-2 reproduction: a relational foundation model pre-trained on synthetic SCM tasks, then evaluated in-context (no fine-tuning) on RelBench benchmarks. Source paper: technical_report/KumoRFM-technical-report.pdf.
Package: implementation/kumorfm_repro/. Work from implementation/.
cd /home/user/work/KumoRFM/implementation
python -m compileall kumorfm_repro # fast syntax check
Critical Architectural Constraints
The paper's approach is pre-train on synthetic data once, then evaluate on RelBench via in-context learning (ICL). Do NOT fine-tune on each RelBench task separately.
The benchmark_adapter train-model command does per-task fine-tuning. Use it only for smoke testing the file-backed adapter. For reproduction evidence, use:
kumorfm_repro.train— synthetic pre-training (produces a checkpoint)kumorfm_repro.icl_evalorkumorfm_repro.icl_suite— ICL evaluation on RelBench tasks using that checkpoint
Training Stability Rules
After extensive experimentation, these are the only known stable configurations:
| Config | AMP | Context | Model | Stability |
|---|---|---|---|---|
| Baseline + AMP | yes | ≤24 | AttnBlock/TableEncoder | Stable (99.9% step success) |
| Improved + no AMP | no | ≤16 | ImprovedAttnBlock/ImprovedTableEncoder | Stable (untested at scale) |
| Improved + AMP | yes | ≤16 | ImprovedAttnBlock + safe pooling + FP32 MHA + typed graph attention | 120-step d_model=64 typed-graph smoke stable with low AMP scale; not long-run-proven |
| Baseline + AMP | yes | ≥32 | AttnBlock | NaN in all steps |
| d_model ≥ 384 | any | any | any | NaN |
The Improved model (RoPE, attention pooling, typed pairwise graph attention) has richer representations and now has an AMP-safe short-run path after fixing all-masked attention pooling, running MHA in FP32 under autocast, and lowering the default GradScaler initial scale. It has passed a 120-step, context-16, d_model-64 two-GPU AMP smoke with zero skipped steps and a 500-step version with saved best checkpoint. The 500-step checkpoint shows context-target sensitivity on F1 ICL ablations, but has not yet beaten the root-only average, so the Baseline model with AMP remains the only long-run-proven config.
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.
- 4d ago First seen · 204 lines · 4,694 tokens per session scan A 409ed432af77
OpenRFM CLAUDE.md is an instructions file published in the GitHub repository T-Lab/OpenRFM (22 stars, last pushed 3mo ago), licensed MIT. It adds 4,694 tokens to every session, about $0.0235 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.
Other instructions, from other repositories
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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.
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.
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.
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).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.