experiment-queue
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.
npx skills add majiayu000/claude-skill-registry --skill experiment-queue --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Experiment Queue 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** —
- When to Use This Skill
- Why This Exists
- Core Concepts
- Job Manifest
- Job State Machine
- Wave Orchestration
- Workflow
- Step 1: Parse Manifest / Build from Grid
- Step 2: Pre-flight
- Step 3: Launch Scheduler
- Step 4: Monitoring
- Step 5: Post-completion
- Grid Spec Syntax
- Wave Chaining
REPLACE the placeholder path before running, or pre-export PROJECT_DIR:
mkdir -p "$LOCAL_RUN_DIR"
Layer 0: self-contained (CC 1.0+ exposes $CLAUDE_SKILL_DIR).
if [ -n "${CLAUDE_SKILL_DIR:-}" ] && [ -f "$CLAUDE_SKILL_DIR/scripts/queue_manager.py" ]; then
fi
Layers 1-3: legacy chain via tools/experiment_queue/ shims.
if [ -z "$QUEUE_TOOLS" ]; then
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
ssh <server> "mkdir -p \"$REMOTE_RUN_DIR/logs\" \"\$HOME/.aris_queue\""What does the experiment-queue skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill experiment-queue --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.