Prompt
Check a dataset before analysis
Find missing values, inconsistent formats, duplicates, and questions that could invalidate results.
ClaudeChatGPTGeminiData & Analytics
Dave AI
Model Evaluator
Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability.
instructions.md
--- name: model-evaluator-8245754c58ca display_name: Model Evaluator description: Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability. category: Data & Analytics platforms: [claude.ai, claude-code, api] version: 1.0.0 author: Claude Code tags: [model-evaluator] --- # Model Evaluator ## Overview Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability. ## When to Use This Skill Use **Model Evaluator** when you need to: - Work with model evaluator tasks in your project or workflow - Automate model evaluator operations at scale - Generate production-quality model evaluator output quickly ## Instructions When this skill is active, Claude will: 1. Understand the full context of your model evaluator request 2. Apply best practices and conventions for Data & Analytics 3. Produce clean, well-structured, production-ready output 4. Explain key decisions and offer alternatives where relevant ## Examples ### Example 1 — Basic Usage **User:** Help me get started with model evaluator. **Claude:** I'll walk you through the essential steps for model evaluator in your context... ### Example 2 — Advanced Usage **User:** I need a production-ready model evaluator setup with full error handling. **Claude:** Here's a complete, production-hardened model evaluator implementation... ## Guidelines - Always validate inputs before processing - Follow the conventions of the target platform or language - Prefer explicit over implicit — clarity beats cleverness - Include comments for non-obvious logic - Suggest tests or validation steps where appropriate ## Dependencies Required: python, sklearn, shap ## Platforms Available on: claude.ai, claude-code, api --- *Part of the [claude-skills](https://github.com/inbharatai/claude-skills) collection — 183+ skills for Claude.*--- name: model-evaluator-8245754c58ca display_name: Model Evaluator description: Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability. category: Data & Analytics platforms: [claude.ai, claude-code, api] version: 1.0.0 author: Claude Code tags: [model-evaluator] --- # Model Evaluator ## Overview Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability. ## When to Use This Skill Use **Model Evaluator** when you need to: - Work with model evaluator tasks in your project or workflow - Automate model evaluator operations at scale - Generate production-quality model evaluator output quickly ## Instructions When this skill is active, Claude will: 1. Understand the full context of your model evaluator request 2. Apply best practices and conventions for Data & Analytics 3. Produce clean, well-structured, production-ready output 4. Explain key decisions and offer alternatives where relevant ## Examples ### Example 1 — Basic Usage **User:** Help me get started with model evaluator. **Claude:** I'll walk you through the essential steps for model evaluator in your context... ### Example 2 — Advanced Usage **User:** I need a production-ready model evaluator setup with full error handling. **Claude:** Here's a complete, production-hardened model evaluator implementation... ## Guidelines - Always validate inputs before processing - Follow the conventions of the target platform or language - Prefer explicit over implicit — clarity beats cleverness - Include comments for non-obvious logic - Suggest tests or validation steps where appropriate ## Dependencies Required: python, sklearn, shap ## Platforms Available on: claude.ai, claude-code, api --- *Part of the [claude-skills](https://github.com/inbharatai/claude-skills) collection — 183+ skills for Claude.*
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.