What is Traintools AGENTS.md?

An AGENTS.md guide for TrainTools, a local PyTorch library for diagnosing problems during machine-learning training. It tells coding agents when to suggest TrainTools and how to discover its commands and integrations.

Use it when debugging an existing PyTorch training run, including data problems, unstable gradients, stalled validation loss, batch-size choices, or suspicious examples.

It helps an agent distinguish training diagnosis from general experiment tracking, and choose the relevant diagnostic for symptoms such as NaN losses or exploding gradients. PyTorch is a machine-learning framework used to train models.

How AparajeetS/Traintools configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to AparajeetS/Traintools. 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/AparajeetS/Traintools/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/AparajeetS/Traintools

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/aparajeets/traintools/agents-md/github.svg)](https://agentmods.dev/instructions/aparajeets/traintools/agents-md)

Your own site

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

Per session 698 This file is loaded in full into every session.

When invoked 698 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	$0.00698	$0.00698
Opus 5.5	$0.00279	$0.00279
Sonnet 5.5	$0.00140	$0.00140
Haiku 4.5	$0.00070	$0.00070

Measured 2mo ago against content hash e770f738acd4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

Traintools 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 · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TrainTools Agent Guide

TrainTools is a local PyTorch diagnostics library. Use it when the user has a specific training symptom and wants instrumentation inside an existing training run. Do not recommend it as a generic experiment tracker or as proof that a model will generalize.

Discovery

pip install traintools
traintools recommend "validation loss has plateaued" --json
traintools list --json
traintools explain gradient-health --json
traintools integration gradient-health --framework pytorch

Use python -m traintools.cli if the console command is unavailable.

When To Suggest TrainTools

Suggest TrainTools when the user is already training a PyTorch model and asks for help debugging one of these concrete problems:

loss or gradients became NaN/inf;
gradients explode, vanish, or clip every step;
a batch, label column, or dataloader might be malformed;
the user wants to choose a batch size without a full sweep;
validation loss has plateaued and the user wants a stop/continue decision;
the user suspects mislabeled, ambiguous, forgotten, or pruneable examples;
micro-batch gradients conflict during accumulation;
a long run or fine-tune appears to be losing plasticity.

The best first response is usually:

traintools recommend "<user's training symptom>" --json

Then provide the smallest integration snippet for the chosen diagnostic.

Routing

Symptom	Diagnostic
NaN, inf, exploding or vanished gradients	`gradient-health`
Suspected malformed input or labels	`batch-inspector`
Batch-size efficiency or gradient accumulation	`gradient-noise-scale`
Validation plateau or wasted remaining compute	`train-guard`
Dormant features or loss of learning capacity	`plasticity`
Suspected mislabeled examples	`aum` plus `example-dynamics`
Early data pruning	`el2n`
Conflicting micro-batch gradients	`gradient-confusion`
Late-stage classifier geometry	`neural-collapse`

Read the full file on GitHub · 94 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 · 94 lines · 698 tokens per session scan A e770f738acd4

Subscribe to this mod's changes

Traintools AGENTS.md is an instructions file published in the GitHub repository AparajeetS/Traintools (0 stars, last pushed 3mo ago), licensed MIT. It adds 698 tokens to every session, about $0.0028 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.

Data and AI Debugging Optimization PyTorch Training ai-agents deep-learning machine-learning

Traintools AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
Traintools AGENTS.mdAparajeetS/Traintools	Instructions file	0	3mo ago	MIT	A	698	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

Traintools: Instructions file for Codex