/cortex-model

CortexML & AI

Builds an end-to-end ML pipeline: data ingestion with validation, feature engineering with a feature store, model training with cross-validation and hyperparameter tuning, evaluation against a held-out test set, and deployment to a serving endpoint with monitoring. Chooses the right model type for the problem.

Install

This skill

Install Cortex Model

1. Add to marketplace

$ claude plugin marketplace add tonone-ai/tonone

2. Install this skill

$ claude plugin install cortex-model@tonone-ai

The agent

Install Cortex

1. Add to marketplace

$ claude plugin marketplace add tonone-ai/tonone

2. Install Cortex

$ claude plugin install cortex@tonone-ai

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Full installation guide

Invoke this skill

Command|$ /cortex-model

When to use

When building a prediction, classification, or regression model from labeled data for the first time and want a complete pipeline rather than a notebook. When an existing model needs proper versioning, evaluation infrastructure, and a serving layer rather than running as a script.

Deep dive

AI End-to-End ML Pipeline Builder

Most ML projects ship as notebooks. /cortex-model produces a real pipeline: ingestion, feature store, training with CV, evaluation, deploy with monitoring.

Read the article

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CortexML

/cortex-prompt

Designs, versions, and evaluates prompts for LLM-powered features. Builds evaluation suites that test prompts against a representative set of inputs so quality regressions are caught before they reach users. Applies systematic prompt engineering techniques and documents what each version changes and why.

When building AI features where prompt quality dir…

CortexML

/cortex-integrate

Integrates an LLM into a production service: API client with retry and timeout handling, streaming response support, semantic caching to avoid redundant API calls, fallback provider configuration, and cost controls including token budget enforcement and request rate limiting per user.

When adding an AI feature to an existing product f…

CortexML

/cortex-eval

Evaluates a deployed model or LLM integration for performance issues: checks for accuracy degradation against a reference dataset, data distribution drift that may explain behavior changes, latency regression compared to baseline, and cost increases from token usage changes. Produces a health report with recommended actions.

When model outputs seem worse after a retraining r…

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