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agentic-labeling

Automate dataset clustering, feature engineering, and cluster interpretation with multi-agent architecture.

Updated Feb 25, 2026
Search Tags:#feature engineering#multi-agent systems#data interpretation#clustering#auto-labeling
One-click install
npx skills add https://github.com/ginaecho/agentic-labeling --skill agentic-labeling
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-labeling
Source: https://github.com/ginaecho/agentic-labeling/tree/main
Command: npx skills add https://github.com/ginaecho/agentic-labeling --skill agentic-labeling

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-learn, xgboost, numpy, pandas, shap, anthropic, rlms, matplotlib, seaborn, plotly, umap-learn, scipy, pyarrow, pyyaml, kaggle, jupyter, ipykernel, flask, requests, scikit-fuzzy, lightgbm, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the entire process of clustering datasets, engineering features, and interpreting the results, providing human-readable cluster personas.

Core Features & Use Cases

  • Automated Clustering: Automatically clusters datasets using various algorithms and optimizes the number of clusters.
  • Feature Engineering: Automatically engineers features from raw data.
  • Cluster Interpretation: Generates human-readable persona names, taglines, descriptions, and traits from cluster profiles.
  • Use Case: Imagine you have a dataset of customer behavior. Use this Skill to automatically cluster customers into segments and generate personas for each segment.

Quick Start

Run the agentic-labeling skill with the data path to your dataset, e.g., agentic-labeling --data path/to/your_dataset.csv

Frequently Asked Questions about agentic-labeling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate clustering and feature engineering for customer segmentation?▼

You can automate clustering and feature engineering by running a multi-agent system that processes raw datasets, optimizes cluster counts, and outputs human-readable customer personas using scikit-learn and pandas.

What is multi-agent data interpretation for market segmentation?▼

Multi-agent data interpretation uses automated agents to analyze clustered dataset profiles and generate human-readable persona names, taglines, descriptions, and traits for market segmentation tasks.

Do I need pandas and scikit-learn to auto-label dataset clusters?▼

Yes, auto-labeling dataset clusters requires pandas and scikit-learn, alongside dependencies like numpy, shap, and umap-learn, to execute feature engineering and multi-agent interpretation processes.

Can I use a multi-agent architecture to generate human-readable cluster personas?▼

Yes, a multi-agent architecture can generate human-readable cluster personas by automatically profiling clustered data to produce descriptive names, taglines, and traits for each identified segment.

What's the best way to interpret clustering results from raw datasets?▼

The best way to interpret clustering results is using an automated multi-agent system that transforms raw data into engineered features and generates descriptive personas for each cluster profile.

Does agentic-labeling work with CSV files for customer profiling?▼

Yes, agentic-labeling works with CSV files for customer profiling by accepting a file path argument to automatically process datasets, engineer features, and interpret cluster segments.

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