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/experiment-queue)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/experiment-queue"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/experiment-queue/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/experiment-queue"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/experiment-queue.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 | $0.00078 | $0.05370 |
| Opus 5 | $0.00039 | $0.02685 |
| Sonnet 5 | $0.00016 | $0.01074 |
| Haiku 4.5 | $0.00008 | $0.00537 |
Grade A, and why
experiment-queue 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 10d 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:
- experiment-queue — 86% identical, 89 lines differ
How it starts
The opening of the file, as written. The whole thing — 432 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Queue
⏱ External cadence: visibility only. This skill already runs its own detached server-side scheduler (60s poll +
depends_on+ wave transitions). Use its status output for overnight visibility (N done / N running / N pending); do not wrap it in a second/loop/CronCreatepoll — that duplicates the scheduler on an uncoordinated clock and races the wave-transition logic it was built to prevent. Seeshared-references/external-cadence.md("don't duplicate an existing scheduler").
Orchestrate large batches of ML experiments on SSH remote GPU servers with proper state tracking, OOM retry, stale cleanup, and wave transitions.
When to Use This Skill
Use when /run-experiment is insufficient:
- ≥10 jobs that need batching across GPUs
- Multi-seed sweeps (e.g., 21 seeds × 12 cells)
- Wave transitions (run wave 1, wait, run wave 2, wait, run wave 3...)
- Teacher+student chains (train teacher then distill; auto-trigger student after teacher done)
- OOM-prone configs where you need to retry with different GPU or wait
- Mixed seed grids where failed cells need re-running
Do NOT use for:
- Single ad-hoc experiment (use
/run-experiment) - Modal/Vast.ai deployments (those have their own orchestration)
- Experiments that need manual inspection between runs
Why This Exists
Based on session audit (2026-04-16), the major wall-clock sinks in multi-seed grid experiments are:
- Stale screens — python finishes, wandb uploads, screen hangs, next wave blocked
- OOM on shared GPU — previous job's memory not yet released
- Wave race — new wave launches before previous wave fully settles
- Missing checkpoints — student launches before teacher saved
- Parser duplication — rewriting multi-seed analysis python every batch
All of these are pure engineering friction that can be orchestrated.
Core Concepts
Environment contract: queue jobs assume the target env is already built and validated per
../shared-references/compute-env-contract.md(spec-hash ledger + kernel witness). A wave of jobs dying at import time = the env contract was skipped, not a queue bug; check the provider's.aris/compute/<provider>.mdledger before re-queueing.
What ships with it
2 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.
- 10d ago First seen · 432 lines · 78 tokens per session scan A 6da32285f905
experiment-queue is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (15,913 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 5,370 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 2026-08-30.
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