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Standards for generating Model Cards from data analysis notebooks. Aligned with Hugging Face and ISO transparency standards.

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SKILL.md

name data-model-reporter
description Standards for generating Model Cards from data analysis notebooks. Aligned with Hugging Face and ISO transparency standards.

Skill: Model Card Reporter

This skill defines the standard for generating Model Cards from data analysis notebooks. It aligns with Hugging Face and Google standards to ensure transparency, reproducibility, and ethical reporting.

💎 1. Core Principles

  1. Standard Alignment:
    • Follows the Hugging Face Model Card structure (YAML Metadata + Markdown Sections).
    • Must include "Ethical Considerations" and "Limitations".
  2. Evidence-Based:
    • All metrics (Accuracy, F1, etc.) must be extracted directly from the notebook execution results.
    • No hallucinated metrics.
  3. Neutral Tone:
    • Use objective language. Avoid marketing buzzwords like "Superb", "Perfect".
    • Acknowledge biases and limitations honestly.

🏗️ 2. Report Structure

The output must follow report-template.md.

Metadata (YAML Frontmatter)

Essential for machine readability (Hugging Face Hub compatibility).

  • language: (e.g., en, ko)
  • library_name: (e.g., sklearn, pytorch)
  • tags: (e.g., tabular-classification, finance)
  • metrics: (e.g., accuracy, f1)

Section 1: Model Details

  • Architecture: Algorithm used (e.g., Random Forest, BERT).
  • Framework: Version info (e.g., Scikit-Learn 1.0.2).
  • Author: Developer or Team name.

Section 2: Intended Use

  • Primary Use: What specific problem does this solve?
  • Out of Scope: When should this model NOT be used? (Crucial for safety).

Section 3: Factors & Metrics

  • Factors: Input features used. Highlight key drivers (SHAP values, feature importance).
  • Metrics: Quantitative performance on Test/Validation sets.

Section 4: Ethical Considerations (Critical)

  • Bias: Are there protected groups (gender, race) that might be unfairly treated?
  • Fairness: Disparate impact analysis results.

🏆 3. Quality Standards

  1. Metric Integrity:
    • REPORTED metrics MUST MATCH valid execution outputs.
    • If code failed to run, do NOT guess the number. Mark as "N/A".
  2. Disclosure:
    • Always disclose the 'Out of Scope' use cases to prevent misuse.
    • Always mention the framework version for reproducibility.

✅ 4. Checklist

  • Extraction: Did you find the model object and training metrics?
  • Completeness: Are all 5 sections of the template filled?
  • Safety Check: Is 'Out of Scope' clearly defined?
  • Verification: Did you explicitly warn the user to review the 'Ethical Considerations'?