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weights-and-biases

weights-and-biasesSafety 95Repository

Machine learning experiment tracking and model management.

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1.2k downloads
Updated 2/11/2026

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SKILL.md

Weights & Biases

Category: Performance EU AI Act Reference: Art. 12, Art. 15 Source: 3rd-party (W&B)

When to Use This Skill

Use this skill when:

  • Weights & Biases tasks - Working on machine learning experiment tracking and model management
  • Compliance assessment - Evaluating AI systems against Art. 12, Art. 15 requirements
  • Risk mitigation - Implementing controls for performance risks

Overview

Machine learning experiment tracking and model management. Log metrics, visualize results, manage datasets, and collaborate on ML projects.

Key Requirements

Regulatory Context (Art. 12, Art. 15)

This tool supports compliance with the following EU AI Act requirements:

  1. Assessment - Evaluate current state against Art. 12, Art. 15 requirements
  2. Implementation - Deploy appropriate controls and measures
  3. Documentation - Record findings, decisions, and evidence
  4. Monitoring - Establish ongoing monitoring and review processes

Assessment Checklist

  • Identify applicable Art. 12, Art. 15 requirements
  • Assess current compliance status
  • Document gaps and findings
  • Develop remediation plan
  • Implement required controls
  • Verify effectiveness of measures
  • Maintain ongoing documentation

Integration Notes

Source Type: 3rd-party (W&B) Category: Performance

Install

Download ZIP
Requires askill CLI v1.0+

AI Quality Score

35/100Analyzed 2/25/2026

Generic compliance template masquerading as a W&B skill. Contains regulatory references but no actual Weights & Biases content - no commands, code snippets, or practical implementation guidance. The "Assessment Checklist" and "Key Requirements" sections are boilerplate and could apply to any tool. Tags and metadata suggest generic observability/CI-CD context rather than W&B-specific guidance.

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Metadata

Licenseunknown
Version-
Updated2/11/2026
PublisherDTMC-marketplace

Tags

ci-cdobservability