run-experiment
Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill run-experiment --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.
# Run Experiment Deploy and run ML experiment: $ARGUMENTS ## Workflow ### Step 1: Detect Environment Read the project's `CLAUDE.md` to determine the experiment environment: - **Local GPU** (`gpu: local`): Look for local CUDA/MPS setup info - **Remote server** (`gpu: remote`): Look for SSH alias, conda env, code directory - **Vast.ai** (`gpu: vast`): Check for `vast-instances.json` at project root
What does the run-experiment skill do?
Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill run-experiment --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 brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 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.