# Machine Learning > Machine Learning Algorithms for prediction, classification, and anomaly detection. Use when this capability is needed. - Skill: `tomevault-io/machine-learning-3` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/machine-learning-3` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/machine-learning-3/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/machine-learning-3 --- # Machine Learning Skill You can harness pure Machine Learning (non-LLM) capabilities to classify inputs, predict outputs using statistical or deep learning models, and find emergent patterns in the Republic ecosystem. ## Overview These ML operations are optimized for tabular data, time-series forecasting, and direct statistical operations. ## Available Native Tools: 1. `ml_predict` - Execute time-series or regression-based predictions using existing or simulated models. - Args: `modelName` (string), `inputData` (array/string). 2. `ml_classify` - Use categorization models to group text or data points. - Args: `className` (string), `data` (string). 3. `ml_detect_anomalies` - Scan logs, memory traces, or economic telemetry to detect emergent deviations and flag risks. - Args: `targetSystem` (string), `sensitivity` (number). ## Execution Guide - When dealing with large arrays of numerical data, use `ml_predict` to project trends. - Use `ml_detect_anomalies` actively to monitor cluster node health and alert civilization operators of incoming chaos experiment failures or simulated systemic bottlenecks. --- > Source: [hunix/HoC-Republic](https://github.com/hunix/HoC-Republic) — distributed by [TomeVault](https://tomevault.io).