What is GPUPlane AGENTS.md?

A guide for coding agents working on GPUPlane, a local system that schedules and monitors machine-learning training jobs on one computer or a small group of GPUs. It describes the project's boundaries, workflow, conventions, and measurements.

It is for running, monitoring, and analyzing training experiments, including deciding whether to keep or revert a result.

It tells agents how to run experiments safely and keeps the project focused on local processes and SQLite instead of adding cloud or container infrastructure.

How EricYuan2007/GPUPlane configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to EricYuan2007/GPUPlane. 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/EricYuan2007/GPUPlane/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/EricYuan2007/GPUPlane

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/ericyuan2007/gpuplane/agents-md/github.svg)](https://agentmods.dev/instructions/ericyuan2007/gpuplane/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/ericyuan2007/gpuplane/agents-md"><img src="https://agentmods.dev/badge/instructions/ericyuan2007/gpuplane/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/ericyuan2007/gpuplane/agents-md"><img src="https://agentmods.dev/badge/instructions/ericyuan2007/gpuplane/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 1,751 This file is loaded in full into every session.

When invoked 1,751 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	$0.01751	$0.01751
Opus 5.5	$0.00700	$0.00700
Sonnet 5.5	$0.00350	$0.00350
Haiku 4.5	$0.00175	$0.00175

Measured 2mo ago against content hash 587db89dc657, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

GPUPlane 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

GPUPlane CLAUDE.md — 92% identical, 6 lines differ

AGENTS.md · 145 lines

How it starts

The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — GPUPlane agent guide

Canonical agent guidance for this repo. CLAUDE.md mirrors it; keep both in sync when editing. Load this before working in GPUPlane.

GPUPlane is an agent-native training control plane for personal GPUs. This file is the short rulebook an AI agent (Claude Code, Codex, Cursor, …) follows when running or extending it. The full design lives in docs/.

What this project is (and isn't)

Is: a control plane that schedules training jobs on a single workstation / small GPU fleet, observes them with training semantics (run / checkpoint / evaluation), and exposes a read+write MCP surface so an agent can drive the experiment loop.
Isn't: a W&B/MLflow replacement (no experiment tracking ambition), a Docker/K8s orchestrator (jobs are bare processes on the agent host), or a multi-tenant cloud product.

Stay on the bare-process + SQLite side of the line. Do not add containers, queues-as-a-service, or cloud SDKs to satisfy a request that "would be nicer in the cloud" — the whole value proposition is low-infra local GPU.

The experiment loop

run-experiment  →  monitor-experiment  →  analyze-results  →  (keep / revert)
submit_job         get_run_summary         compare_runs         checkpoint path
                   list_events             get_best_checkpoint  git keep/revert
                   diagnose_run            evaluate_checkpoint

The three skills in .claude/skills/gpu-training/ encode this loop. Prefer invoking them over improvising with the MCP tools directly — they carry the conventions below.

Conventions

Metrics

Training scripts must log metrics via the GPUPlane SDK or TensorBoard. Without metrics, diagnose_run / anomaly detection / recommendation have nothing to read. Metric names are namespaced (train/loss, eval/loss).
One primary metric drives every "best" decision. It resolves through experiment → project → global inheritance; cross-scope comparisons use only their common scope or an explicit metric. Default eval/loss (minimize) or eval/accuracy (maximize). Don't multi-objective.

Read the full file on GitHub · 145 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 · 145 lines · 1,751 tokens per session scan A 587db89dc657

Subscribe to this mod's changes

GPUPlane AGENTS.md is an instructions file published in the GitHub repository EricYuan2007/GPUPlane (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 1,751 tokens to every session, about $0.0070 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 2026-08-31.

GPUPlane AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
GPUPlane AGENTS.mdEricYuan2007/GPUPlane	Instructions file	0	2mo ago	Apache-2.0	A	1,751	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	yesterday	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

GPUPlane: Instructions file for Codex