Weights & Biases Monitor

Monitor and analyze Weights & Biases training runs. Use when checking training status, detecting failures, analyzing loss curves, comparing runs, or monitoring experiments. Triggers on "wandb", "training runs", "how's training", "did my run finish", "any failures", "check experiments", "loss curve", "gradient norm", "compare runs".

This skill appears to implement exactly what it claims (W&B run monitoring) but there are mismatches you should address before use: 1) The scripts require the wandb Python package and a Python interpreter/virtualenv but the skill manifest doesn't declare this — install wandb in an isolated venv before running. 2) Provide a WANDB_API_KEY (or run 'wandb login') when running headless; treat that key like any secret (store in a secure vault or environment only for the session). 3) Edit the SKILL.md or invocation commands to remove the hard-coded path (~/clawd/venv/...) or run the scripts with your own Python path to avoid surprises. 4) Note the default entity/projects in watch_runs.py are author-specific — change them to your org or pass explicit arguments to avoid querying someone else’s projects. 5) The code excerpts in the distribution you provided are truncated in places; to raise confidence, inspect the full files for any network calls outside wandb.Api (e.g., requests, socket) or obfuscated code. If you want, provide the complete untruncated files and I can re-check for any hidden endpoints or questionable behavior. Running these scripts in a disposable environment (isolated container or dedicated VM) first is recommended so you can verify behavior and confirm no unexpected data exfiltration occurs.

Type: OpenClaw Skill Name: wandb-monitor Version: 1.0.0 The OpenClaw AgentSkills skill bundle for 'wandb-monitor' is classified as benign. All Python scripts (`characterize_run.py`, `check_runs.py`, `compare_runs.py`, `run_details.py`, `watch_runs.py`) exclusively interact with the Weights & Biases (W&B) API to fetch and analyze training run data, which aligns perfectly with the skill's stated purpose. There is no evidence of data exfiltration to unauthorized endpoints, malicious execution, persistence mechanisms, or obfuscation. The `SKILL.md` instructions are clear, guide the agent to use the provided scripts, and do not contain any prompt injection attempts to deviate from the intended functionality or access sensitive, unrelated data.