monitor-experiment
Monitor running experiments, check progress, collect results. Use when user says \"check results\", \"is it done\", \"monitor\", or wants experiment output.
npx skills add majiayu000/claude-skill-registry --skill monitor-experiment-wanshuiyin-auto-claude-code-res --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.
# Monitor Experiment Results Monitor: $ARGUMENTS ## Workflow ### Step 1: Check What's Running First identify the backend from `AGENTS.md`, run notes, or launch summary: local, SSH, Vast.ai, or Modal. Monitor the backend that was actually used; do not assume a plain SSH screen session when the run was launched through Vast.ai or Modal. ```bash ssh <server> "screen -ls" ``` For Vast.ai, also check instance state, SSH reachability, hourly cost, and whether `auto_destroy` is pending. For Modal, check the Modal run/app logs, function status, timeout, volume outputs, and cloud cost exposure. ### Step 2: Collect Output from Each Screen For each screen session, capture the last N lines: ```bash 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 ```bash ssh <server> "ls -lt <results_dir>/*.json 2>/dev/null | head -20" ``` If JSON results exist, fetch and parse them: ```bash ssh <server> "cat <results_dir>/<latest>.json" ``` ### Step 3.5: Pull W&B Metrics (when `wandb: true` in AGENTS.md) If the project enables W&B, pull metrics before interpret
- Workflow
- Step 1: Check What's Running
- Step 2: Collect Output from Each Screen
- Step 3: Check for JSON Result Files
- Step 3.5: Pull W&B Metrics (when wandb: true in AGENTS.md)
- Step 4: Summarize Results
- Step 5: Interpret
- Step 6: Feishu Notification (if configured)
- Key Rules
ssh <server> "screen -ls" ssh <server> "screen -S <name> -X hardcopy /tmp/screen_<name>.txt && tail -50 /tmp/screen_<name>.txt" ssh <server> "ls -lt <results_dir>/*.json 2>/dev/null | head -20" ssh <server> "cat <results_dir>/<latest>.json" python3 - <<'PY'
What does the monitor-experiment skill do?
Monitor running experiments, check progress, collect results. Use when user says \"check results\", \"is it done\", \"monitor\", or wants experiment output.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill monitor-experiment-wanshuiyin-auto-claude-code-res --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 majiayu000/claude-skill-registry, a repository with 534 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.