experiment-queue
SSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention.
Sorting a stream of records into a picture
What it does
> ⏱ External cadence: visibility only. This skill already runs its own > detached server-side scheduler (60s poll + depends_on + wave transitions).
Read more
> 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").
Use when user says "batch experiments", "队列实验", "run grid", "multi-seed sweep", "auto-chain experiments", or when /run-experiment is…
You don’t need to name it. Ask for the job in your own words — your AI loads the skill when the request matches.
What it can touch
Includes 2 scripts
Your AI can run these when the skill is used — on your computer in a coding tool (which asks first unless you have told it not to), or in the AI’s own sandbox in Claude and ChatGPT.
How we know
Found in the skill’s folder: scripts/build_manifest.py, scripts/queue_manager.py.
Uses terminal commands
It shows your AI commands to run. In a coding agent that means your terminal; in a chat app it usually can’t.
How we know
The SKILL.md contains shell code blocks.
Lets your AI run any terminal command without asking, while the skill is in use
While the skill is working, Claude Code will not stop to ask before each terminal command. The permission ends when you send your next message.
How we know
SKILL.md line 1: “allowed-tools: Bash(*), Read, Grep, Glob, Edit, Write, Skill(run-experiment), Skill(monitor-experiment)”. Read 2026-10-05, commit 2132036.
SKILL.md, line 1 ↗No serious risk pattern found
Launch7’s automated check read all 3 text files in this skill and matched none of its serious risk patterns. This is not a guarantee: a check like this can miss new or disguised tricks, and the author can change the files after the check.
How we know
Read 2026-10-05, commit 2132036 by pipeline/trust/analyze.py — 40+ patterns for hidden text, prompt injection, downloads that run, credential access and more. It reads; it never runs the skill.
Recently maintained
Its repository has had changes recently.
How we know
Last push to wanshuiyin/Auto-claude-code-research-in-sleep: 2026-09-29 (6 days before the scan).
Read automatically from the skill’s own files on GitHub on 5 Oct 2026. Launch7 does not run or manually review skills: this lists what the check saw, and nothing here means “safe”. How we check
Add it to your AI
Partly. Upload it in Claude’s settings — the instructions work; the scripts need the full folder.
Built in your browser from the SKILL.md on GitHub — nothing passes through Launch7.
In Claude, open Customize → Skills, press +, then Create skill → Upload a skill, and choose the .zip.
Make sure code execution is switched on (Settings → Capabilities). Skills need it.
Start a new chat and ask for the job in your own words — Claude picks the skill up when it fits.
This skill ships 2 scripts beside its instructions. The download here is the SKILL.md alone — enough for the instructions, not for the parts that run code. Use the command route, or take the full folder from the repository.
How Claude does this: support.claude.com · checked 28 Sep 2026 · “Supported” means documented by the vendor; Launch7 has not run it end to end
More from wanshuiyin
All from this publisher →- alphaxiv
Quick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback.
- arxiv
Search, download, and summarize academic papers from arXiv.
- auto-paper-improvement-loop
Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds.
- auto-review-loop-llm
Autonomous research review loop using any OpenAI-compatible LLM API.
- auto-review-loop-minimax
Autonomous multi-round research review loop using MiniMax API.