ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.
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/wanshuiyin/Auto-claude-code-research-in-sleepnpx agentmods add skills/wanshuiyin/auto-claude-code-research-in-sleep/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/wanshuiyin/auto-claude-code-research-in-sleep/vast-gpu)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/vast-gpu"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/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/wanshuiyin/auto-claude-code-research-in-sleep/vast-gpu"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/vast-gpu.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 353 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.04329 |
| Opus 5 | $0.00023 | $0.02165 |
| Sonnet 5 | $0.00009 | $0.00866 |
| Haiku 4.5 | $0.00005 | $0.00433 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- vast-gpu — 91% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 395 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 · 395 lines · 46 tokens per session scan A 38e0f7c622e8
vast-gpu is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (15,970 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 4,329 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
gke-compute-classes
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google-cloud-filestore-autoscale
Inspects Google Cloud Filestore capacity and utilization, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds…
terraform-module-library
Build reusable Terraform modules for AWS, Azure, GCP, and OCI infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, or implementing reusable IaC components.
remote-compute-ssh
Evaluate and use SSH Remote Compute before choosing where to run GPU, high-memory, parallel, batch, model-inference, bioinformatics, or other long-running scientific work; supports short remote commands and asynchronous jobs with automatic harvest and analysis.
compute-env-setup
Prepare reproducible setup instructions and validate a user-managed named software environment on an Open Science SSH Compute Host, including direct SSH and Slurm hosts. Use when a remote job needs packages, modules, cache variables, or a repeatable activation that the host does not already provide.
aws-lambda-microvms
Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or…