How do I install the Data Model Reporter skill?

Run npx skillmds add majiayu000/data-model-reporter in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Data Model Reporter safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Data Model Reporter?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Data Model Reporter free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Data Model Reporter?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Data Model Reporter by majiayu000 — Agent Skill

Name: Data Model Reporter
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

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

Standard Alignment:
- Follows the Hugging Face Model Card structure (YAML Metadata + Markdown Sections).
- Must include "Ethical Considerations" and "Limitations".
Evidence-Based:
- All metrics (Accuracy, F1, etc.) must be extracted directly from the notebook execution results.
- No hallucinated metrics.
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

Metric Integrity:
- REPORTED metrics MUST MATCH valid execution outputs.
- If code failed to run, do NOT guess the number. Mark as "N/A".
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'?

---name: data-model-reporterdescription: 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'?

Frequently asked questions