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AutomationPython

Experiment Status

by Xiangyue-Zhang

Experiment Status is an Automation skill for Claude Code, published by Xiangyue-Zhang in auto-deep-researcher-24x7.

1.3K stars111 forkson Xiangyue-Zhang/auto-deep-researcher-24x7Added 2026/08/20Repository updated 2026/06/03
ai-agentautonomous-agentclaude-codedeep-learningexperiment-automationgpuhyperparameter-tuningllm-agentmachine-learningmlopspytorchresearch-automation
Install in seconds
Install Experiment Status
Copy Experiment Status into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7/tree/main/skills/experiment-status ~/.claude/skills/experiment-status

Requires Node.js. Downloads this skill only β€” not the rest of the repository β€” into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/experiment-status/SKILL.md in Xiangyue-Zhang/auto-deep-researcher-24x7
Installs to
~/.claude/skills/experiment-status
Collection
One of 8 skills cataloged from this repository
Category
Automation β€” 2226 skills

What Experiment Status does

Experiment Status checks the current state of autonomous experiment loops, including progress, recent decisions, training process status, and GPU utilization. Use it to monitor long-running experiments and review results without interrupting execution.

Experiment Status is cataloged under Automation on DirSkills. Experiment Status comes from a repository tagged ai-agent, autonomous-agent, claude-code, deep-learning and experiment-automation.

Documentation

README

experiment-status

Check the current status of your autonomous experiment agent.

Usage

Claude Code: /experiment-status
Claude Code: /experiment-status --project /path/to/project
Codex: $experiment-status

Behavior

  1. Read PROJECT_BRIEF.md β€” show the research goal
  2. Read MEMORY_LOG.md β€” show key results and recent decisions
  3. Read .cycle_counter β€” show how many cycles completed
  4. Check for running training processes via the configured execution backend
  5. If training is running, tail the log file for latest output
  6. Show GPU utilization through the configured backend
  7. Check if HUMAN_DIRECTIVE.md exists (pending directive)

This is the opening of the README. Read the full README on GitHub.

Commands Experiment Status provides

Slash commands named in this skill’s SKILL.md, listed in the order they first appear.

  • /experiment-status

Frequently asked about Experiment Status

  • What else does Xiangyue-Zhang publish alongside Experiment Status?

    Experiment Status is one of 8 skills that DirSkills catalogs from Xiangyue-Zhang/auto-deep-researcher-24x7, the repository it ships in. Its siblings there include Auto Experiment, Conf Search and Daily Papers. Each one is a separate skill with its own page in this directory, installs the same way Experiment Status does, and is maintained by Xiangyue-Zhang in that same repository. The rest of the collection is listed on the Xiangyue-Zhang/auto-deep-researcher-24x7 page.

  • How does Experiment Status compare to other Automation skills?

    Experiment Status ranks #701 by stars among the 2226 Automation skills in this catalog. The most-starred ones next to it are Autonomous Loops, Autonomous Agent Harness and Automation Audit Ops. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Experiment Status against them. Open each page to compare what they document and how they install.

More from Xiangyue-Zhang/auto-deep-researcher-24x7

Experiment Status is one of 8 skills cataloged on DirSkills from Xiangyue-Zhang/auto-deep-researcher-24x7.

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2026/08/20

Auto Experiment

Auto Experiment launches an autonomous THINK→EXECUTE→REFLECT loop that runs deep learning experiments 24/7. Use it to iterate on GPU training, monitor runs at zero LLM cost, and automatically decide next steps from results.
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Daily Papers

Daily Papers searches arXiv for recent papers relevant to a user's research interests and deduplicates recommendations. Use it inside Claude Code or Codex to get a daily ranked briefing of new papers with detailed analysis.
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GPU Monitor

GPU Monitor checks GPU status, running experiments, and available resources by running nvidia-smi and summarizing the output. Use it to quickly see which GPUs are free or in use for training.
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Obsidian Sync refreshes an Obsidian dashboard and daily notes from the current state of a Deep Researcher project. Use it when you want progress documentation updated from experiment state, memory logs, and cycle counters.
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Paper Analysis performs deep analysis of a single academic paper, extracting figures from the arXiv source and producing a structured review covering problem, method, experiments, and insights.
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