skills: Official Agent Skills for Weights & Biases Models and Weave

by wandb · Agent Tool · ★ 69

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install skills

npx skills add wandb/skills

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars69
Forks3
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score66/100
Open Issues14
Last Updated2026-10-12
Created2026-02-23
Platformspython
Est. Tokens~14k

About skills

skills -yellow) -brightgreen) 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...

Compatible Skills

These tools work well together with skills for enhanced workflows:

  • mathodology — semantic(0.21)+complementary+shared_fw(vercel)+same_lang+similar_pop+shared_platform (65%)
  • claude-prompt-engineering-guide — semantic(0.18)+complementary+shared_fw(vercel)+same_lang+similar_pop+shared_platform (64%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Agent Tool tools