Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/vast-gpuWrote 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/raja21068/autoresearch/vast-gpu)<a href="https://agentmods.dev/skills/raja21068/autoresearch/vast-gpu"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/vast-gpu/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.
<a href="https://agentmods.dev/skills/raja21068/autoresearch/vast-gpu"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/vast-gpu.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00046 | $0.04119 |
| Opus 5 | $0.00023 | $0.02060 |
| Sonnet 5 | $0.00009 | $0.00824 |
| Haiku 4.5 | $0.00005 | $0.00412 |
Grade A, and why
vast-gpu 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 7d 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.
This is a copy
91% identical to vast-gpu — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vast.ai GPU Management
Manage vast.ai GPU instance: $ARGUMENTS
Overview
Rent cheap, capable GPUs from vast.ai on demand. This skill analyzes the training task to determine GPU requirements, searches for the best-value offers, presents options with estimated total cost, and handles the full lifecycle: rent → setup → run → destroy.
Users do NOT specify GPU models or hardware. They describe the task — the skill figures out what to rent.
Prerequisites: The vastai CLI must be installed (requires Python ≥ 3.10) and authenticated:
pip install vastai
vastai set api-key YOUR_API_KEY
If your system Python is < 3.10, create a virtual environment with Python ≥ 3.10 (e.g.,
conda create,pyenv,uv venv, etc.) and installvastaithere.
SSH public key must be uploaded at https://cloud.vast.ai/manage-keys/ BEFORE creating any instance. Keys are baked into instances at creation time — if you add a key after renting, you must destroy and re-create the instance.
State File
All active vast.ai instances are tracked in vast-instances.json at the project root:
[
{
"instance_id": 33799165,
"offer_id": 25831376,
"gpu_name": "RTX_3060",
"num_gpus": 1,
"dph": 0.0414,
"ssh_url": "ssh://[email protected]:58955",
"ssh_host": "1.208.108.242",
"ssh_port": 58955,
"created_at": "2026-03-29T21:12:00Z",
"status": "running",
"experiment": "exp01_baseline",
"estimated_hours": 4.0,
"estimated_cost": 0.17
}
]
This file is the source of truth for /run-experiment and /monitor-experiment to connect to vast.ai instances.
Workflow
Action: Provision (default)
Analyze the task, find the best GPU, and present cost-optimized options. This is the main entry point — called directly or automatically by /run-experiment when gpu: vast is set.
Step 1: Analyze Task Requirements
Read available context to determine what the task needs:
- From the experiment plan (
refine-logs/EXPERIMENT_PLAN.md):- Compute budget (total GPU-hours)
- Hardware hints (e.g., "4x RTX 3090")
- Model architecture and dataset size
- Run order and per-milestone cost estimates
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.
- 7d ago First seen · 381 lines · 46 tokens per session scan A 2e8c182c9922
vast-gpu is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 4,119 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to vast-gpu, differing in 22 lines, and is treated as a copy.
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