Anish Shah 9e5b999be1 fix: skip WBAF skill-bench without private token
Make public PR CI pass when the private WBAF checkout token is unavailable while preserving the manual live benchmark path for maintainers.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-05-14 16:51:10 -04:00
2026-03-06 15:43:02 -07:00
2026-02-23 16:19:31 -07:00

skills

Codex Claude Code

Skills to guide Claude Code, Codex, and other coding agents on using the Weights & Biases AI developer platform to train models and build agents.

For model training

  • Log metrics and rich media during model training and fine-tuning
  • Track model training experiments
  • Analyze runs and experiment results to understand how the model is learning
  • Tune hyperparameters

For agent building

  • Trace agentic AI applications
  • Analyze traces and classify them into failure modes
  • Evaluate models with labeled datasets
  • Run online evaluations for production monitoring

Getting Started

npx skills add wandb/skills

Then set your W&B API key:

export WANDB_API_KEY=<your-key>

npx skills is a utility for installing skills into major coding agent CLIs. Use --global to install for all projects, or --agent <name> to target a specific agent. See the npx skills docs for more details.

Available Skills

Skill Description Status
wandb-models W&B Models skill for runs, sweeps, artifacts, configs, metrics, run history, best-run selection, and model analysis. experimental
weave-analysis W&B Weave skill for traces, calls, evaluations, scorers, token/cost analysis, and model usage. experimental
wandb-reports W&B Reports skill for report authoring, workspace guidance, report media, and report/project semantics. experimental
signal-builder Signal Builder skill for project-grounded monitor and LLM-as-judge signal design. experimental
wandb-launch W&B Launch skill for queues, jobs, relaunching runs, and remote compute workflows. experimental

Benchmarks

We maintain a growing internal benchmark suite that evaluates each skill across coding agents and task categories. The split skills are currently experimental and validated through the W&B Agent Factory benchmark harness.

Category Tasks Claude Code (sonnet4.6) Codex (gpt-5.3-codex)
Weave analysis 26 97%* 63%*
Weave tooling 11 95%* 83%*
Model training 8 90%* 85%*
LLM finetuning & RL analysis 14 72%* 86%*
Failure & outlier detection 8 86%* 63%*

*Pass rates are +/- 3%. Many tasks span multiple categories.

Contributing

See CONTRIBUTING.md.

S
Description
Primary W&B skill for broad or mixed Weights & Biases work: project overviews, W&B runs and artifacts, Weave traces and evaluations, Reports, and Launch…
Readme Apache-2.0 774 KiB
Languages
Python 99.3%
Shell 0.7%