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
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/run-experiment)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/run-experiment"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/run-experiment/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/run-experiment"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/run-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk fail
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 | $0.00047 | $0.03437 |
| Opus 5 | $0.00023 | $0.01718 |
| Sonnet 5 | $0.00009 | $0.00687 |
| Haiku 4.5 | $0.00005 | $0.00344 |
Grade A, and why
run-experiment 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 8d 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:
- run-experiment — 91% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Experiment
Deploy and run ML experiment: $ARGUMENTS
Workflow
Step 1: Detect Environment
Read the project's CLAUDE.md to determine the experiment environment:
- Local GPU (
gpu: local): Look for local CUDA/MPS setup info - Remote server (
gpu: remote): Look for SSH alias, conda env, code directory - Vast.ai (
gpu: vast): Check forvast-instances.jsonat project root — if a running instance exists, use it. Also checkCLAUDE.mdfor a## Vast.aisection. - Modal (
gpu: modal): Serverless GPU via Modal. No SSH, no Docker, auto scale-to-zero. Delegate to/serverless-modal.
Modal detection: If CLAUDE.md has gpu: modal or a ## Modal section, the entire deployment is handled by /serverless-modal. Jump to Step 4: Deploy (Modal) — Steps 2-3 are not needed (Modal handles code sync and GPU allocation automatically).
Environment contract (../shared-references/compute-env-contract.md): before
building or trusting any environment, read the provider's env ledger
(.aris/compute/<provider>.md) — an unchanged spec hash means warm-reuse, a
changed one means rebuild. New env → write the declarative spec first, render it
for this provider's shape, and never declare it ready on import-success alone:
run the seeded kernel witness, and after any rebuild/doc edit run the
agent-follows-doc pass (a fresh subagent executes the documented invocation
verbatim and reports doc-vs-reality divergence).
Vast.ai detection priority:
- If
CLAUDE.mdhasgpu: vastor a## Vast.aisection:- If
vast-instances.jsonexists and has a running instance → use that instance - If no running instance → call
/vast-gpu provisionwhich analyzes the task, presents cost-optimized GPU options, and rents the user's choice
- If
- If no server info is found in
CLAUDE.md, ask the user.
Step 2: Pre-flight Check
Check GPU availability on the target machine:
Remote (SSH):
ssh <server> nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader
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.
- 8d ago First seen · 314 lines · 47 tokens per session scan A 4d481ce95d8b
run-experiment 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 47 tokens to every session and 3,437 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
serving-llms-vllm
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
serving-llms-vllm
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
tensorrt-llm
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.