Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
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".
skill openclawclawhub Free
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".
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/characterize_run.py, scripts/check_runs.py, scripts/compare_runs.py, scripts/run_details.py, scripts/watch_runs.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- chrisvoncsefalvay
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Weights & Biases
Monitor, analyze, and compare W&B training runs.
Setup
wandb login # Or set WANDB_API_KEY in environment
Characterize a Run (Full Health Analysis)
~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/characterize_run.py ENTITY/PROJECT/RUN_ID Analyzes: Loss curve trend (start → current, % change, direction) Gradient norm health (exploding/vanishing detection) Eval metrics (if present) Stall detection (heartbeat age) Progress & ETA estimate Config highlights Overall health verdict Options: --json for machine-readable output.
Watch All Running Jobs
~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/watch_runs.py ENTITY [--projects p1,p2] Quick health summary of all running jobs plus recent failures/completions. Ideal for morning briefings. Options: --projects p1,p2 — Specific projects to check --all-projects — Check all projects --hours N — Hours to look back for finished runs (default: 24) --json — Machine-readable output
Compare Two Runs
~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/compare_runs.py ENTITY/PROJECT/RUN_A ENTITY/PROJECT/RUN_B Side-by-side comparison: Config differences (highlights important params) Loss curves at same steps Gradient norm comparison Eval metrics Performance (tokens/sec, steps/hour) Winner verdict
Python API Quick Reference
import wandb api = wandb.Api() # Get runs runs = api.runs("entity/project", {"state": "running"}) # Run properties run.state # running | finished | failed | crashed | canceled run.name # display name run.id # unique identifier run.summary # final/current metrics run.config # hyperparameters run.heartbeat_at # stall detection # Get history history = list(run.scan_history(keys=["train/loss", "train/grad_norm"]))
Metric Key Variations
Scripts handle these automatically: Loss: train/loss, loss, train_loss, training_loss Gradients: train/grad_norm, grad_norm, gradient_norm Steps: train/global_step, global_step, step, _step Eval: eval/loss, eval_loss, eval/accuracy, eval_acc
Health Thresholds
Gradients > 10: Exploding (critical) Gradients > 5: Spiky (warning) Gradients < 0.0001: Vanishing (warning) Heartbeat > 30min: Stalled (critical) Heartbeat > 10min: Slow (warning)
Integration Notes
For morning briefings, use watch_runs.py --json and parse the output. For detailed analysis of a specific run, use characterize_run.py. For A/B testing or hyperparameter comparisons, use compare_runs.py.
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Scripts1 Docs
- SKILL.md
- scripts/characterize_run.py
- scripts/check_runs.py
- scripts/compare_runs.py
- scripts/run_details.py
- scripts/watch_runs.py