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 skills/simple-agent-lab/autotrainess/trainnpx skills add simple-agent-lab/AutoTrainess --skill traingit clone --depth 1 https://github.com/simple-agent-lab/AutoTrainessWrote 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/skills/simple-agent-lab/autotrainess/train)<a href="https://agentmods.dev/skills/simple-agent-lab/autotrainess/train"><img src="https://agentmods.dev/badge/skills/simple-agent-lab/autotrainess/train.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 | $0.00019 | $0.00333 |
| Opus 5 | $0.00010 | $0.00167 |
| Sonnet 5 | $0.00004 | $0.00067 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
train 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.
What it actually says
train
Purpose
Run the simplest valid benchmark-oriented training workflow with LlamaFactory, choose the training mode that matches the current stage and evidence, and export a model ready for evaluation.
When to use
- When training data is ready and the next step is to run model training.
- When the current stage requires supervised fine-tuning.
- When current evidence supports reinforcement learning.
Core rules
- Use
hiyouga/LlamaFactoryfor all training work. - Read shared/llamafactory.md before running training.
- Choose the training mode that matches the current stage and evidence.
- Keep the workflow minimal and reproducible.
- Export
final_model/for downstream evaluation. - Do not switch to another framework or a custom training loop.
Run caution
Long training jobs are allowed, but should be started deliberately. Prefer a short validation run first unless there is already clear evidence that a long run is necessary.
Workflow
- Read shared/llamafactory.md.
- Decide whether the current stage requires sft/stage.md or rl/stage.md.
- Follow the selected stage document.
- Run training through the provided script in
scripts/. - Export
final_model/for evaluation.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 35 lines · 19 tokens per session scan A a1f2243f4305
train is a skill published in the GitHub repository simple-agent-lab/AutoTrainess (21 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 333 once invoked, about $0.0001 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.
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