ml-analyze

Name: ml-analyze
Availability: InStock
Author: mila

Analyze CSV, JSON, Parquet, or text datasets and generate plain-language reports.

Updated Apr 6, 2026

Search Tags:#report generation #data analysis #pattern detection #anomaly detection #end-to-end analysis

Ready to run

npx skills add https://github.com/milasaurus/compound-ml --skill ml-analyze

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ml-analyze
Source: https://github.com/milasaurus/compound-ml/tree/main/plugins/compound-ml/skills/ml-analyze
Command: npx skills add https://github.com/milasaurus/compound-ml --skill ml-analyze

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, sklearn, umap, hdbscan, sentence-transformers, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a thorough end-to-end analysis of datasets, uncovering patterns, anomalies, and generating plain-language reports to help users make informed decisions.

Core Features & Use Cases

End-to-End Analysis: Profile, cluster, detect anomalies, and generate reports.
Natural Language Objective: Users can describe their analysis goals in plain language.
Use Case: For instance, a user can say "analyze this dataset for patterns" and receive a detailed report with insights.

Quick Start

Run an analysis on the dataset 'sales_data.csv' to understand customer buying patterns.

Frequently Asked Questions about ml-analyze

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

FAQPage Schema

How do I run end-to-end data analysis and generate a plain-language report from a CSV dataset?▼

End-to-end data analysis automatically profiles your CSV, detects patterns and anomalies using clustering, then generates a plain-language report with actionable insights. You simply provide the dataset and describe your analysis objective in natural language.

Can I use natural language to specify my data analysis objectives instead of writing code?▼

Yes, data analysis objectives can be specified in natural language. You can describe your goals in plain text, such as asking to find customer buying patterns, and the analysis methods are automatically selected and executed based on that objective.

What file formats are supported for pattern detection and anomaly detection?▼

Pattern detection and anomaly detection support CSV, JSON, Parquet, and text file datasets. The analysis methods are automatically selected based on the specific data type provided in these supported formats.

Do I need Python libraries installed to perform anomaly detection and clustering on my dataset?▼

Yes, Python libraries are required for data manipulation, clustering, and anomaly detection. The analysis relies on dependencies including pandas, sklearn, umap, hdbscan, sentence-transformers, and matplotlib to process data and generate visualizations.

What is the best way to detect anomalies and uncover patterns in a large Parquet dataset?▼

The best way to detect anomalies and uncover patterns in a Parquet dataset is using automated clustering and anomaly detection algorithms. This approach profiles the data, applies hdbscan and umap for pattern recognition, and outputs a detailed report.