Monitor experiment

Skill brycewang-stanford/Auto-Empirical-Research-Skills/skills/42-wanshuiyin-ARIS/skills/skills-codex/monitor-experiment

Monitor running experiments, check progress, collect results. Use when user says "check results", "is it done", "monitor", or wants experiment output.From its SKILL.md

Install

npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill monitor-experiment

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

SKILL.md

1.8 KB, 409 tokens by cl100k_base, as published. Nobody here has run it

Monitor Experiment Results

Monitor: $ARGUMENTS

Workflow

Step 1: Check What's Running

ssh <server> "screen -ls"

Step 2: Collect Output from Each Screen

For each screen session, capture the last N lines:

ssh <server> "screen -S <name> -X hardcopy /tmp/screen_<name>.txt && tail -50 /tmp/screen_<name>.txt"

If hardcopy fails, check for log files or tee output.

Step 3: Check for JSON Result Files

ssh <server> "ls -lt <results_dir>/*.json 2>/dev/null | head -20"

If JSON results exist, fetch and parse them:

ssh <server> "cat <results_dir>/<latest>.json"

Step 4: Summarize Results

Present results in a comparison table:

| Experiment | Metric | Delta vs Baseline | Status |
|-----------|--------|-------------------|--------|
| Baseline  | X.XX   | —                 | done   |
| Method A  | X.XX   | +Y.Y              | done   |

Step 5: Interpret

Compare against known baselines
Flag unexpected results (negative delta, NaN, divergence)
Suggest next steps based on findings

Step 6: Feishu Notification (if configured)

After results are collected, check ~/.codex/feishu.json:

Send experiment_done notification: results summary table, delta vs baseline
If config absent or mode "off": skip entirely (no-op)

Key Rules

Always show raw numbers before interpretation
Compare against the correct baseline (same config)
Note if experiments are still running (check progress bars, iteration counts)
If results look wrong, check training logs for errors before concluding

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.