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weights-and-biases Skill

AI Agent SkillPythonOpen source

W&B: log ML experiments, sweeps, model registry, dashboards. Published by NousResearch in hermes-agent.

What is weights-and-biases Skill?

W&B: log ML experiments, sweeps, model registry, dashboards. Published by NousResearch in hermes-agent. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.

Trust signal
95/100
Maintenance signal
90/100
Adoption signal
100/100

Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.

Key capabilities

  • Includes SKILL.md support
  • Reusable instructions support
  • Developers using hermes-agent

Technical details

Copy skill directory
  • Install or run with Copy skill directory

When to use weights-and-biases Skill

  • Use it for developers using hermes-agent.

Built with

PythonCopy skill directory

Editorial notes

Source

  • Creator: NousResearch
  • Repository: NousResearch/hermes-agent
  • Skill file: skills/mlops/evaluation/weights-and-biases/SKILL.md

What it does

W&B: log ML experiments, sweeps, model registry, dashboards.

Skill instructions

Weights & Biases: ML Experiment Tracking & MLOps When to Use This Skill Use Weights & Biases (W&B) when you need to: - Track ML experiments with automatic metric logging - Visualize training in real-time dashboards - Compare runs across hyperparameters and configurations - Optimize hyperparameters with automated sweeps - Manage model registry with versioning and lineage - Collaborate on ML projects with team workspaces - Track artifacts (datasets, models, code) with lineage Users: 200,000+ ML practitioners | GitHub Stars: 10.5k+ | Integrations: 100+ Installation bash Install W&B pip install wandb Login (creates API key) wandb login Or set API key programmatically export WANDBAPIKEY=yourapikeyhere Quick Start Basic Experiment Tracking python import wandb Initialize a run run = wandb.init( project="my-project", config={ "learningrate": 0.001, "epochs": 10, "batchsize": 32, "architecture": "ResNet50" } ) Training loop for epoch in range(run.config.epochs): Your training code trainloss = t

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Frequently asked questions

What is weights-and-biases?

weights-and-biases is a open-source AI agent skill with Copy skill directory. W&B: log ML experiments, sweeps, model registry, dashboards.

Who is weights-and-biases best for?

weights-and-biases is best for reusing agent instructions, scripts, and references.

How do I install weights-and-biases?

Install or run weights-and-biases using Copy skill directory. Check weights-and-biases for the latest setup command.

Is weights-and-biases actively maintained?

weights-and-biases may need a closer maintenance check before production use.

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Stars
214,436
Forks
39,858
Last commit
9 days ago
Repository age
1 year
License
MIT

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