AI/ML Product Agent Team for Claude Code

The AI/ML Product Agent Team for Claude Code bundles four purpose‑built Claude Code agents that work together to cover the entire machine‑learning product lifecycle. By assigning model construction, prompt design, data preparation, and evaluation to dedicated agents, you can move from concept to production with fewer manual hand‑offs and clearer separation of concerns.

What You Get

• ML Engineer Agent – designs model architectures, writes training pipelines, performs data preprocessing, and runs hyper‑parameter searches tailored to your use case.
• Prompt Engineer Agent – creates and iterates prompts, applies systematic A/B testing, and refines chain‑of‑thought patterns to improve LLM responses.
• Data Scientist Agent – conducts exploratory data analysis, carries out feature engineering, runs statistical tests, and builds interactive visualization dashboards.
• Eval Specialist Agent – assembles evaluation frameworks, constructs benchmark suites, and records model performance metrics over time.

How to Use

Extract the provided pack into the .claude/ directory of your project. All agents read from a shared context that includes references to your ML stack, data sources, and any existing configuration files. Launch Claude Code, describe the AI task you want to address, and the agents begin collaborating automatically. The ML Engineer Agent generates or updates model code while the Eval Specialist Agent runs validation in parallel. When prompt quality is a priority, the Prompt Engineer Agent takes over to refine the wording and test variations. The Data Scientist Agent can be invoked at any stage to explore new data signals or to produce dashboards for stakeholder review. Because each agent maintains its own focus, you can run multiple aspects of the pipeline concurrently without manually coordinating scripts.

Who This Is For

AI product teams that need to integrate large language models or custom classifiers into applications and want a consistent workflow for model development, testing, and deployment. Data scientists seeking to reduce the time spent on repetitive preprocessing and evaluation tasks. Engineers responsible for maintaining end‑to‑end pipelines who prefer to delegate specialized steps to autonomous agents while keeping overall control. The team is also suitable for research groups that require systematic prompt experimentation alongside traditional model training.