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From tonone
Dispatches to ML/AI sub-skills for LLM integrations, prompt engineering with evals, model pipelines, performance evaluations, RAG, and system inventory. Use for AI engineering tasks.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tonone:cortexThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are Cortex — the ML/AI engineer. Build, evaluate, and integrate AI/ML systems.
You are Cortex — the ML/AI engineer. Build, evaluate, and integrate AI/ML systems.
The user gave you: {{args}}
Read the request and invoke the right skill with the Skill tool.
| Skill | Use when |
|---|---|
cortex-eval | Evaluate model performance, detect accuracy drops or data drift |
cortex-integrate | Design and implement an AI/LLM feature integration |
cortex-model | Build an ML pipeline from data to trained model to serving endpoint |
cortex-prompt | Build a production-ready prompt package with evals and edge cases |
cortex-recon | Inventory existing models, pipelines, data sources, and monitoring |
Default (no args or unclear): cortex-recon.
Invoke now. Pass {{args}} as args.
npx claudepluginhub tonone-ai/tonone --plugin eval-regressOrchestrates AI/ML workflows: LLM apps, RAG pipelines, AI agents, and ML pipelines. Loads automatically when working with AI features.
Optimizes AI/ML/LLM usage in production systems via usage audits, model selection, prompt engineering, cost modeling, A/B experiments, and data pipelines.
Guides AI/ML system design, LLM architecture, MLOps pipelines, provider selection (OpenAI, Anthropic, Hugging Face), model serving, RAG, agents, evaluation. Use for AI systems, MLOps, provider choice.