# experiment-queue

Orchestrates ML experiments on remote SSH GPU servers. Manages OOM retries, cleans stale screen sessions, and coordinates multi-seed wave execution.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 437 installs · ★ 18090
- Author: wanshuiyin
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/experiment-queue/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/experiment-queue

## What it does

The skill manages batches of ten or more experiments, multi-seed sweeps, and multi-stage distillation runs across remote GPU instances. It takes job descriptions from a YAML manifest or a parameter grid. Before dispatching work, it verifies SSH connectivity, remote paths, and checkpoint presence.

Scheduling runs via an independent background script with a 60-second polling cycle. It monitors VRAM against a default 500 MiB threshold and closes dangling `screen` instances. When a job hits an out-of-memory failure, the queue pauses for 120 seconds before attempting a retry, allowing up to three attempts.

Do not wrap this skill inside `/loop` or `CronCreate`. Adding an uncoordinated timer conflicts with the internal scheduler and corrupts wave transitions. The remote conda environment must also be fully prepared before queueing jobs.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-queue -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/experiment-queue/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/experiment-queue/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-queue -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/experiment-queue/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-queue -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/experiment-queue/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install wanshuiyin/Auto-claude-code-research-in-sleep experiment-queue
```

2. **Manually: put the files in place**
- File: `.github/skills/experiment-queue/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-queue -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --path skills/experiment-queue
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/experiment-queue/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-queue -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/experiment-queue/SKILL.md

## When to use it
- orchestrate multi-seed ML grid sweeps across remote GPUs over SSH
- retry failed jobs automatically after out-of-memory errors
- terminate lingering screen sessions between execution waves
- block downstream distillation runs until teacher checkpoints exist

## Example prompts
- Run batch experiments using manifest.yaml on gpu-server
- Start a multi-seed sweep for run_distill.py across 8 GPUs
- Set up an experiment queue with OOM retry for seeds 100, 200, and 300

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: c41b7a91801bf32a.

BEGIN THIRD-PARTY FILE c41b7a91801bf32a
```text
---
name: experiment-queue
description: SSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention. Use when user says "batch experiments", "队列实验", "run grid", "multi-seed sweep", "auto-chain experiments", or when /run-experiment is insufficient for 10+ jobs that need orchestration.
argument-hint: "[manifest-or-grid-spec]"
allowed-tools: Bash(*), Read, Grep, Glob, Edit, Write, Skill(run-experiment), Skill(monitor-experiment)
---

# 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` / `CronCreate` poll — that
> duplicates the scheduler on an uncoordinated clock and races the
> wave-transition logic it was built to prevent. See
> [`shared-references/external-cadence.md`](../shared-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:

1. **Stale screens** — python finishes, wandb uploads, screen hangs, next wave blocked
2. **OOM on shared GPU** — previous job's memory not yet released
3. **Wave race** — new wave launches before previous wave fully settles
4. **Missing checkpoints** — student launches before teacher saved
5. **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
```
END THIRD-PARTY FILE c41b7a91801bf32a

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First 2660 of 20108 bytes. © wanshuiyin · MIT · https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/experiment-queue/SKILL.md

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---
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