---
title: "ml-toolkit"
description: "Data-science skills for framing ML problems, auditing data, engineering features, reducing dimensions, clustering, model selection and validation, classification and regression metrics, tree ensembles, linear and logisti"
canonical: https://agentpluginsdirectory.com/plugins/ml-toolkit
last-updated: 2026-10-12
---

# ml-toolkit
Data-science skills for framing ML problems, auditing data, engineering features, reducing dimensions, clustering, model selection and validation, classification and regression metrics, tree ensembles, linear and logistic models, anomaly detection, and text embeddings.
- Slug: ml-toolkit
- Publisher: Moheet Subudhi
- Repository: https://github.com/moheetsubudhi-isb/business-analytics-skills
- Manifest: plugins/ml-toolkit/plugin.json
- Version: 1.0.0
- License: MIT
- Category (editorial): other
- Skills: 12 (anomaly-detection, classification-metrics-and-threshold, clustering-and-segmentation, dimensionality-reduction, feature-engineering, linear-and-logistic-models, ml-data-audit, ml-problem-framing, model-selection-and-validation, regression-error-metrics, text-and-embedding-features, tree-and-ensemble-models)
- MCP servers: 0
- Stars: 0
- Repository created: 2026-09-13
- Repository last pushed: 2026-10-07
- Publisher type: User
- Listing: https://agentpluginsdirectory.com/plugins/ml-toolkit
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## What ml-toolkit does, in the publisher's words

Data-science skills for framing ML problems, auditing data, engineering features, reducing dimensions, clustering, model selection and validation, classification and regression metrics, tree ensembles, linear and logistic models, anomaly detection, and text embeddings.

Part of Business Analytics Skills, a set of agent skills that make an AI assistant work like a decision scientist and an advisor. Each skill asks what the work is for only when the answer would change the result, follows a real method step by step, runs a check wherever there is logic to verify, and hands back a plain-language brief plus a technical appendix.

- anomaly-detection: Find unusual transactions, sensor readings, log events, users or records, and choose the detection method: z-score or IQR rules, robust statistics, Mahalanobis distance, isolation forest, local….

From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/moheetsubudhi-isb/business-analytics-skills/HEAD/plugins/ml-toolkit/README.md