Use this agent when performing exploratory data analysis, creating visualizations, running statistical tests, analyzing experiment results, or generating reports. For example: profiling a new dataset, creating distribution plots, running hypothesis tests on A/B experiment data, comparing model metrics across…
Use this agent when code changes need review before completion. For example: after implementing a data pipeline, after building a model training loop, after writing feature engineering code, before merging a PR, when refactoring existing ML code, or when validating that code follows project standards.
Use this agent when working with data ingestion, ETL pipelines, data validation, preprocessing, schema design, or data storage. For example: building a data loading pipeline from CSV/Parquet, adding pandera schema validation, creating preprocessing transforms, setting up DVC for data versioning, optimizing data…
Use this agent when working with model architecture, training loops, loss functions, optimizers, hyperparameter tuning, experiment tracking, or model evaluation. For example: building a PyTorch model, writing a training loop with mixed precision, setting up an Optuna hyperparameter sweep, configuring MLflow experiment…
Use this agent when a complex task needs to be broken down, when multiple agents need coordination, or when the best approach for an ML/data science task is unclear. For example: implementing a new model training pipeline, planning a data preprocessing overhaul, triaging a model performance regression, coordinating a…
Load the latest model checkpoint, run evaluation on the test set, and generate a metrics report with confusion matrix. Use this after training to assess model performance or to re-evaluate a specific checkpoint.
Generate a comprehensive summary report of the latest experiment including metrics, plots, and comparison with baseline. Use this after training and evaluation to create a shareable experiment summary.
Run the full data science pipeline: validate raw data, preprocess, engineer features, train model, and evaluate. Use this when you want to execute the end-to-end ML pipeline or re-run it after data or code changes.
Gives the agent up-to-date documentation and code examples for libraries and frameworks, fetched on demand so answers are not stuck at the model's training cutoff. Runs locally from the @upstash/context7-mcp npm package.
Adds a structured step-by-step reasoning tool the agent can call to break a problem into revisable thoughts. Runs locally from the @modelcontextprotocol/server-sequential-thinking npm package.
Gives the agent a persistent knowledge graph it can write facts to and recall across sessions. Runs locally from the @modelcontextprotocol/server-memory npm package.
Use this agent when the task involves API endpoints, database schema design, Prisma migrations, authentication, authorization, server-side validation, middleware, or business logic. For example: creating a new REST endpoint with request validation, adding a Prisma model and migration, implementing JWT auth with…
Use this agent when the task involves Docker, docker-compose, CI/CD pipelines, GitHub Actions, environment variable management, deployment scripts, monitoring, logging infrastructure, or production readiness. For example: writing a multi-stage Dockerfile, setting up a GitHub Actions CI pipeline, configuring…
Use this agent when the task involves React components, pages, hooks, client-side routing, styling with Tailwind CSS, form handling, client-side state management, or accessibility. For example: building a new page with a data table, creating a reusable modal component, adding client-side form validation, fixing a…
Run API integration tests against the running backend, verify endpoints return expected responses and status codes. Use after deploying a preview or starting the dev server.
Use this agent when SwiftUI views, data models, view models, networking, persistence, or business logic need to be implemented. For example: creating a new screen, adding a network API call, setting up SwiftData models, implementing a feature with async/await, writing URLSession networking code, or fixing a data flow…
Use this agent when tests need to be written, debugged, or improved. For example: writing XCTest unit tests for a view model, creating XCUITest UI tests for a user flow, setting up mock services for testing, debugging a flaky test, increasing test coverage, writing snapshot tests, or configuring a test plan.
Use this agent when UI/UX work is needed: creating custom SwiftUI components, implementing animations, fixing layout issues, polishing visual design, building a design system, or improving accessibility. For example: adding a custom tab bar animation, implementing a skeleton loading view, auditing VoiceOver support…
Build and launch the app in the iOS Simulator. Automatically selects an appropriate simulator device, boots it if needed, and installs and launches the app.
The meta-skill: create AI agent teams, individual agents, or custom skills for any project. Use when the user wants to generate a complete .claude/ agent team, create a single agent, or create a single skill.