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 --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 **SSH server:** ```bash ssh <server> "screen -ls" ``` **Vast.ai instance** (read `ssh_host`, `ssh_port` from `vast-instances.json`): ```bash ssh -p <PORT> root@<HOST> "screen -ls" ``` Also check vast.ai instance status: ```bash vastai show instances ``` **Modal** (when `gpu: modal` in CLAUDE.md): ```bash modal app list # List running/recent apps modal app logs <app> # Stream logs from a running app ``` Modal apps auto-terminate when done — if it's not in the list, it already finished. Check results via `modal volume ls <volume>` or local output. ### 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 CLAUDE.md) **Sk
- 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 CLAUDE.md)
- Step 4: Summarize Results
- Step 5: Interpret
- Step 6: Feishu Notification (if configured)
- Key Rules
ssh <server> "screen -ls" ssh -p <PORT> root@<HOST> "screen -ls" vastai show instances modal app list # List running/recent apps modal app logs <app> # Stream logs from a running app 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" List recent runs in the project ssh <server> "python3 -c \"
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 --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.