Cortex

ML/AI

Add real machine learning to your product, not just an API call.

ML and AI engineer who builds full training pipelines from data ingestion to trained model to serving endpoint. Integrates LLMs into production services with streaming responses, caching, fallback handling, and cost controls. Designs and versions prompts with evaluation suites for reproducible outputs. Monitors deployed models for accuracy degradation and data drift.

Read the field guide: The AI/ML Engineer for LLM Integration

Install Cortex

Cortex

Install Cortex

1. Add to marketplace

$ claude plugin marketplace add tonone-ai/tonone

2. Install Cortex

$ claude plugin install cortex@tonone-ai

5 skills included.

Engineering team

Install the Engineering team

1. Add to marketplace

$ claude plugin marketplace add tonone-ai/tonone

2. Install the team

$ claude plugin install engineering-team@tonone-ai

15 agents included.

Full installation guide

5 Skills

Everything Cortex can do in your project

CortexML

/cortex-model

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.

When building a prediction, classification, or reg…

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…

CortexML

/cortex-recon

ML and AI reconnaissance: inventories all models in production, LLM integrations and their API usage, training pipelines and their schedules, data sources and their freshness, evaluation setups, and monitoring coverage. Identifies technical debt, models running without evaluation, and unmonitored AI features.

When taking over a project that has existing ML in…