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Monitor Experiment Results

Checks running ML experiments over SSH, W&B, or cloud backends and summarizes their results.

Source Download skill
Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Stars
18,482
Licence
MIT
Files
1 file

What it does

When you have machine learning experiments running on a server or cloud service, this skill tells the AI agent how to check on them: whether they are still running, what their logs say, which result files have been written, and what the metrics look like. The agent then presents the raw numbers in a comparison table, flags anything odd such as NaN values or divergence, and suggests next steps. It can also send an optional completion notification to Feishu if that is configured.

Great for

  • Researchers who launch training jobs on a remote GPU server and want an agent to check progress and summarize results
  • Teams that track training with Weights & Biases and want links to run dashboards included in their reports
  • People running experiments on Vast.ai or Modal who want cost and instance-state reminders alongside results

Skip it if

  • Anyone without running experiments or access to the servers they run on, since the skill has nothing to monitor
  • Situations where the agent lacks SSH or cloud credentials, because the commands will fail and the skill cannot work around that
  • Cloud jobs where the user does not want the agent querying billing or instance state, since the skill asks it to check cost exposure

Try asking

  • “Check results: is the training run on my server finished yet?”
  • “Monitor the experiment and summarize the metrics against the baseline in a table.”
  • “Pull the W&B metrics for my latest run and tell me whether the loss is diverging.”
  • “Is it done? Collect the output from all screen sessions on my GPU box.”
Step-by-step guide: how to use Monitor Experiment ResultsInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 1 original files, the MIT licence and an attribution note.

    monitor-experiment.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip monitor-experiment.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip monitor-experiment.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Keep an AGENTS.md file in the project that records the backend and whether W&B is enabled, so the agent monitors the right place.
  • Ask for the baseline configuration explicitly so the delta comparison uses matching settings.
  • For Vast.ai or Modal jobs, ask the agent to report instance state and accrued cost so you can stop idle paid resources.

What's inside

  • SKILL.md

Redistributed unmodified from wanshuiyin/Auto-claude-code-research-in-sleep under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.