train-submit

train-submit is a skill for Claude Code, Codex from Shallow-dusty/claude-plugins. It costs 73 tokens per session (633 once invoked), scanned A, original, no licence file.

A tool for sending prepared machine-learning training code and settings to Kaggle, Google Colab, or a remote computer over SSH. It starts the training run on the selected platform.

In plain words
What is it for?
Use it to submit training jobs to Kaggle, Colab, or an SSH-accessible host after preparing the code and configuration locally.
Why use it?
It removes the need to manually copy training files and launch experiments on each remote platform.

Skill for Claude CodeCodex

Part of the trainhub plugin — 4 skills shipped together

Install

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.

agentmods
npx agentmods add skills/shallow-dusty/claude-plugins/train-submit
Any agent
npx skills add Shallow-dusty/claude-plugins --skill train-submit
Clone the repo
git clone --depth 1 https://github.com/Shallow-dusty/claude-plugins

Made for: Claude Code, Codex.

Or install trainhub, the plugin that ships this one along with the rest of its 4 skills.

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.

agentmods badge for train-submit

README.md
[![agentmods](https://agentmods.dev/badge/skills/shallow-dusty/claude-plugins/train-submit.svg)](https://agentmods.dev/skills/shallow-dusty/claude-plugins/train-submit)
Your own site
<a href="https://agentmods.dev/skills/shallow-dusty/claude-plugins/train-submit"><img src="https://agentmods.dev/badge/skills/shallow-dusty/claude-plugins/train-submit.svg" alt="Measured on agentmods" height="20"></a>
Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 633 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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.

ModelPer sessionOnce invoked
Fable 5 $0.00073 $0.00633
Opus 5 $0.00036 $0.00316
Sonnet 5 $0.00015 $0.00127
Haiku 4.5 $0.00007 $0.00063

Measured 3d ago against content hash 8cd61e4eedfa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

train-submit 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 3d 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.

trainhub/skills/train-submit/SKILL.md · 47 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

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.

  1. 3d ago First seen · 47 lines · 73 tokens per session scan A 8cd61e4eedfa

Subscribe to this mod's changes

train-submit is a skill published in the GitHub repository Shallow-dusty/claude-plugins (2 stars, last pushed 4mo ago), with no licence file. It adds 73 tokens to every session and 633 once invoked, about $0.0004 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens