hugging-face-evaluation-manager

slug	hf_evaluation_manager
name	Hugging Face Evaluation Manager
summary	Manage and track model evaluation results for agricultural ML models and farm prediction systems
description	Adapted from Hugging Face's evaluation manager skill for FF-Terminal, this skill provides comprehensive tools for managing evaluation results of machine learning models used in agricultural contexts. It supports tracking crop prediction models, yield forecasting systems, disease detection models, and other agricultural ML applications. Includes integration with model cards, benchmark comparisons, and performance tracking over time.
version	0.1.0
tags	["huggingface","evaluation","metrics","benchmarking","agriculture","machine-learning","model-performance","farming"]
triggers	["evaluation","model evaluation","metrics","benchmark","performance","model card","agricultural model","crop model","disease detection","yield prediction"]
priority	default
assets	[]
recommended_tools	["analyze_data","write_file","run_command","tavily_search","generate_image_gemini"]

Hugging Face Evaluation Manager for FF-Terminal

Overview

This skill enables you to manage, track, and analyze evaluation results for machine learning models used in agricultural applications. It provides tools for adding structured evaluation data to model cards, comparing performance across different models, and maintaining comprehensive evaluation histories.

When to Use This Skill

Use this skill when you need to:

• Track evaluation results for crop yield prediction models
• Compare performance of different disease detection models
• Monitor model accuracy for weather forecasting systems
• Add evaluation metrics to agricultural ML model cards
• Benchmark models against industry standards
• Track model performance over growing seasons
• Evaluate precision agriculture algorithms

Key Capabilities

1. Agricultural Model Types Supported

• Crop Yield Prediction: Regression models for yield forecasting
• Disease Detection: Classification models for plant disease identification
• Weather Prediction: Time series models for weather and climate forecasting
• Soil Analysis: Models for soil property prediction and recommendations
• Pest Detection: Computer vision models for pest identification
• Irrigation Optimization: Models for water usage optimization
• Harvest Timing: Models for optimal harvest prediction

2. Evaluation Metrics

• Regression Metrics: MAE, MSE, RMSE, R² for continuous predictions
• Classification Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC
• Time Series Metrics: MAPE, SMAPE, Directional Accuracy
• Custom Agricultural Metrics: Yield prediction accuracy, disease detection rates
• Economic Metrics: Cost-benefit analysis, ROI calculations
• Environmental Metrics: Resource efficiency, sustainability scores

3. Data Sources for Evaluation

• Historical Farm Data: Past seasons' performance data
• Cross-Validation: K-fold validation on agricultural datasets
• Field Trials: Real-world testing on actual farms
• Simulation Results: Controlled environment testing