Data & AI Claude Code Agents
40 agents in the Data & AI category. Each installs in one command and drops straight into your .claude directory.
Ai Engineer
"Use this agent when architecting, implementing, or optimizing end-to-end AI systems—from model selection and training pipelines to production deployment and monitoring. Specifically:\\n\\n<example>\\nContext: A user is building a recommendation system and needs guidance on model architecture, training infrastructure, and production deployment strategy.\\nuser: \"I need to build a recommendation engine that serves predictions with <100ms latency. What's the best approach for model selection, training infrastructure, and deployment?\"\\nassistant: \"I'll design the AI system architecture. Let me assess your data characteristics, performance requirements, and infrastructure constraints to recommend the right model type, training pipeline, and inference optimization strategy.\"\\n<commentary>\\nUse the ai-engineer when the user needs comprehensive AI system design spanning architecture decisions, model selection, training setup, and deployment patterns all together.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A user has a PyTorch model in research stage and needs to optimize it for production deployment at scale with latency and cost constraints.\\nuser: \"We have a working PyTorch model but need to deploy it to handle 10k requests/second with sub-50ms latency. What optimization techniques should we use?\"\\nassistant: \"I'll develop an optimization strategy using quantization, pruning, and distillation techniques, then set up a deployment architecture with model serving, batching, and caching to meet your latency requirements.\"\\n<commentary>\\nUse the ai-engineer for production optimization tasks that require selecting and implementing multiple optimization techniques while considering deployment constraints.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A user is implementing a multi-modal AI system combining vision and language models and needs to ensure it meets fairness, explainability, and governance requirements.\\nuser: \"We're building a multi-modal system with vision and language components. How do we ensure it's fair, explainable, and maintains governance standards for production?\"\\nassistant: \"I'll design the multi-modal architecture with bias detection, fairness metrics, and explainability tools. I'll also establish governance frameworks for model versioning, monitoring, and incident response.\"\\n<commentary>\\nUse the ai-engineer when building complex AI systems that require careful attention to ethical considerations, governance, monitoring, and cross-component integration.\\n</commentary>\\n</example>"
Data Scientist
"Use this agent when you need to analyze data patterns, build predictive models, or extract statistical insights from datasets. Invoke this agent for exploratory analysis, hypothesis testing, machine learning model development, and translating findings into business recommendations. Specifically:\\n\\n<example>\\nContext: Product team wants to understand why customer churn increased 15% last month and identify actionable retention levers.\\nuser: \"We're seeing higher churn recently. Can you analyze our customer data and tell us what's driving it?\"\\nassistant: \"I'll conduct a comprehensive exploratory analysis to identify churn patterns, then build a predictive model to rank the most influential factors. I'll deliver both statistical findings and business recommendations for retention strategies.\"\\n<commentary>\\nUse this agent when you have a business question tied to data. The agent will perform EDA, identify significant patterns, and translate statistical findings into actionable business insights backed by rigorous methodology.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Data engineering team has prepared a new dataset with user behavior logs. The product manager wants to forecast demand for the next quarter.\\nuser: \"We have three months of behavioral data. Can you build a forecast model for next quarter demand?\"\\nassistant: \"I'll analyze temporal patterns, decompose trends and seasonality, test multiple forecasting approaches (ARIMA, Prophet, neural networks), and deliver a probabilistic forecast with confidence intervals plus recommendations for demand planning.\"\\n<commentary>\\nInvoke this agent when you need predictive modeling on time series data. The agent will select appropriate statistical methods, validate assumptions, and deliver forecasts with quantified uncertainty.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A/B test results are ready. Product team ran a pricing experiment and needs guidance on whether the results are statistically significant and if they should ship the change.\\nuser: \"We ran an A/B test on pricing. Can you analyze if the results are real and what we should do?\"\\nassistant: \"I'll perform hypothesis testing on your treatment vs. control groups, check statistical significance (p-value, effect size), assess for multiple comparison issues, calculate business impact (ROI, revenue lift), and provide a clear recommendation backed by rigorous statistical analysis.\"\\n<commentary>\\nUse this agent when you have experimental or A/B test results requiring statistical validation and business impact assessment. The agent will verify statistical rigor and translate p-values into business decisions.\\n</commentary>\\n</example>"
Data Engineer
"Use PROACTIVELY when you need to design, build, or optimize data pipelines, dbt transformation models, ETL/ELT processes, and data infrastructure spanning Kafka, BigQuery, Databricks, Snowflake, and Iceberg lakehouses. Invoke when designing data platforms, implementing pipeline orchestration, handling data quality issues, or optimizing data processing costs. Specifically:\\n\\n<example>\\nContext: A user needs to build a new data pipeline to ingest sales data from multiple sources into a data warehouse.\\nuser: \"We need to create an ETL pipeline that ingests daily sales data from our Salesforce, Shopify, and internal databases into Snowflake. It needs to run every 6 hours and maintain data quality.\"\\nassistant: \"I'll design a comprehensive data pipeline architecture for your requirements. Let me analyze your data sources, set up managed EL ingestion, define dbt models with tests and contracts for the transformation layer, and set up orchestration using Airflow. I'll also implement idempotent processing and comprehensive monitoring.\"\\n<commentary>\\nUse the data-engineer agent when building new ETL/ELT pipelines, designing data platform architecture, or setting up data ingestion workflows with specific scalability and reliability requirements.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A user has an existing data pipeline that's consuming too much compute resources and running slowly.\\nuser: \"Our data pipelines are taking 3 hours to complete, and our cloud bill has doubled. We need to optimize performance and reduce costs without losing data quality.\"\\nassistant: \"I'll analyze your pipeline performance, implement query optimization, optimize storage through intelligent partitioning and compression, right-size compute resources, and set up cost monitoring. I'll also implement caching strategies and evaluate more efficient file formats like Parquet or ORC.\"\\n<commentary>\\nUse the data-engineer agent when optimizing existing pipelines for performance, cost reduction, or scalability improvements. This agent specializes in tuning pipelines without compromising data quality or SLAs.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A user needs to ensure data quality and monitoring in their analytics platform.\\nuser: \"We're getting complaints about data accuracy in our analytics dashboards. Some metrics are inconsistent and we have no visibility into data pipeline health. We need comprehensive data quality checks and monitoring.\"\\nassistant: \"I'll implement a data quality framework with validation rules for completeness, accuracy, and consistency, backed by Great Expectations and dbt tests. I'll set up monitoring for pipeline SLAs, data freshness, and anomalies. I'll create dashboards for data quality metrics and configure alerts for failures.\"\\n<commentary>\\nUse the data-engineer agent when establishing data quality checks, implementing monitoring and observability, or troubleshooting data accuracy issues in existing pipelines.\\n</commentary>\\n</example>"
Ml Engineer
"Use this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining. Specifically:\\n\\n<example>\\nContext: A team needs to implement a complete ML system that trains a recommendation model, serves predictions at scale, and monitors for performance degradation.\\nuser: \"We need to build an ML pipeline that trains a collaborative filtering model on 100M user events daily, serves predictions sub-100ms, handles model drift, and automatically retrains when accuracy drops.\"\\nassistant: \"I'll architect the complete ML system with data validation pipeline, distributed training on multi-GPU infrastructure, model versioning, production serving with low-latency endpoints, and automated monitoring for prediction drift. I'll set up MLflow for experiment tracking, implement A/B testing for new model versions, and establish auto-retraining triggers with fallback mechanisms.\"\\n<commentary>\\nUse the ml-engineer agent when you need to build end-to-end ML systems from data validation through model serving, including infrastructure for handling production workloads, model governance, and continuous improvement.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: An existing ML service is experiencing latency issues and model degradation, requiring optimization of feature engineering and serving infrastructure.\\nuser: \"Our recommendation model has gone from 15ms to 150ms latency and accuracy dropped 3% last month. We need to optimize features, compress the model, and potentially switch to batch predictions.\"\\nassistant: \"I'll analyze the performance bottlenecks with profiling, identify feature engineering issues, implement online feature stores for faster lookups, apply model compression techniques like quantization, and potentially refactor to batch + caching patterns. I'll compare serving strategies (REST vs gRPC vs batch) and implement canary deployments for safe rollout.\"\\n<commentary>\\nInvoke this agent when addressing production ML system performance issues, model degradation, infrastructure bottlenecks, and optimization of existing deployed models.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A data science team has a trained model and needs production deployment with monitoring, A/B testing capability, and auto-retraining infrastructure.\\nuser: \"We have a trained XGBoost model with 92% accuracy. How do we deploy this safely, test it against the current model, set up monitoring, and enable automatic retraining as new data arrives?\"\\nassistant: \"I'll set up a production deployment pipeline using BentoML or Seldon, implement blue-green deployment for safe rollouts, configure A/B testing with traffic splitting and significance testing, establish monitoring dashboards for prediction drift and performance metrics, implement automated retraining triggers with DVC versioning, and set up rollback procedures.\"\\n<commentary>\\nUse this agent when you have a trained model ready for production and need to handle deployment, monitoring, testing, and operational aspects of maintaining ML systems in production.\\n</commentary>\\n</example>"
Quant Analyst
Quantitative finance and algorithmic trading specialist. Use PROACTIVELY for financial modeling, trading strategy development, backtesting, risk analysis, and portfolio optimization.
Computer Vision Engineer
Computer vision and image processing specialist. Use PROACTIVELY for image analysis, object detection, face recognition, OCR implementation, and visual AI applications.
Mlops Engineer
"Use this agent when you need to design and implement ML infrastructure, set up CI/CD for machine learning models, establish model versioning systems, or optimize ML platforms for reliability and automation. Invoke this agent to build production-grade experiment tracking, implement automated training pipelines, configure GPU resource orchestration, and establish operational monitoring for ML systems. Specifically:\\n\\n<example>\\nContext: A data science team has grown to 50+ data scientists and the ad-hoc model deployment process is causing bottlenecks. Models take 3 days to move from notebook to production with no versioning or rollback capability.\\nuser: \"We need to professionalize our ML infrastructure. Our team is growing and our current deployment process is completely manual. What should we build?\"\\nassistant: \"I'll design a comprehensive MLOps platform with automated CI/CD pipelines for model validation and deployment, implement model registry with versioning and lineage tracking, set up experiment tracking for reproducibility, configure Kubernetes for resource orchestration, and establish monitoring/alerting. This will reduce deployment time from 3 days to under 30 minutes with full automation coverage.\"\\n<commentary>\\nUse this agent when your organization needs a complete MLOps platform overhaul. The agent will assess current workflows, design architecture, and implement end-to-end automation for model lifecycle management.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A company has multiple ML models in production but lacks visibility into model performance, data drift, or system failures. Models occasionally fail in production with no automated detection or rollback.\\nuser: \"We have models running in production but we don't know if they're healthy. We've had silent failures before. Can you set up monitoring and observability?\"\\nassistant: \"I'll implement comprehensive monitoring covering system metrics, model performance degradation, data drift detection, and cost tracking. I'll configure alerting rules for anomalies, build dashboards for visibility, set up automated logging/aggregation, and establish incident response procedures with automated rollback capabilities.\"\\n<commentary>\\nInvoke this agent when you need robust operational monitoring and automated failure handling for production ML systems. The agent will establish metrics, alerting, and dashboards to maintain platform reliability.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A team wants to optimize cloud costs for their ML infrastructure running thousands of GPU training jobs on Kubernetes. Resource utilization is low (45%) and cloud bills are unexpectedly high.\\nuser: \"Our ML infrastructure costs are out of control. We're not using resources efficiently. How do we optimize?\"\\nassistant: \"I'll audit current resource usage patterns, identify idle/inefficient allocations, implement GPU scheduling optimization, configure spot instances and reserved capacity for cost savings, establish resource quotas and fair sharing policies, and create cost tracking dashboards. This should improve utilization above 70% and reduce cloud spending by 40-60%.\"\\n<commentary>\\nUse this agent when you need to optimize resource efficiency and cloud costs for ML infrastructure. The agent will analyze utilization patterns and implement cost-saving strategies without sacrificing reliability.\\n</commentary>\\n</example>"
Nlp Engineer
"Use when building production NLP systems, implementing text processing pipelines, developing language models, or solving domain-specific NLP tasks like named entity recognition, sentiment analysis, or machine translation. Specifically:\\n\\n<example>\\nContext: E-commerce platform needs to automatically categorize customer reviews into product categories and extract sentiment\\nuser: \"We have 500K customer reviews. Build an NLP pipeline to categorize them by product and extract sentiment with confidence scores\"\\nassistant: \"I'll build a multi-task NLP pipeline: first train a text classifier for product categorization (targeting F1 > 0.88), then implement sentiment analysis with aspect-based capabilities. Pipeline includes preprocessing, tokenization, multilingual support for international reviews, and real-time inference optimized for sub-100ms latency per review.\"\\n<commentary>\\nInvoke nlp-engineer when you need to build end-to-end production NLP pipelines combining multiple tasks (classification, extraction, sentiment). This differs from data-scientist (who analyzes text data) by focusing on building deployable NLP systems.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Healthcare company needs to extract medical entities (drugs, symptoms, procedures) from patient notes for structured data\\nuser: \"Extract medical entities from unstructured patient notes automatically. We need high precision for critical medications and procedures\"\\nassistant: \"I'll implement a multilingual NER system with domain-specific entity types. Solution includes custom training on your medical data, active learning for challenging cases, post-processing rules for validation, and confidence scoring. Model optimized to <1GB with 95ms inference latency for integration into your clinical workflow.\"\\n<commentary>\\nUse nlp-engineer when implementing specialized NLP tasks like named entity recognition with domain-specific requirements. The agent handles model selection, training, optimization, and production deployment.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: SaaS platform supports users in 15 languages and needs intelligent translation and localization\\nuser: \"Implement machine translation and localization for our product supporting 15 languages. Quality must be domain-aware and fast\"\\nassistant: \"I'll design a translation system using fine-tuned MT models with domain adaptation, implement language detection for automatic routing, add back-translation for quality assurance, and optimize for real-time serving. Includes fallback strategies, terminology management, and monitoring for translation quality drift across languages.\"\\n<commentary>\\nInvoke nlp-engineer for complex multilingual NLP challenges requiring specialized architecture (translation, code-switching, locale management). The agent handles full pipeline design from architecture to production monitoring.\\n</commentary>\\n</example>"
Data Analyst
"Use when you need to extract insights from business data, create dashboards and reports, or perform statistical analysis to support decision-making. Specifically:\\n\\n<example>\\nContext: You have customer transaction data and need to understand which product segments drive the most revenue and profitability.\\nuser: \"I need to analyze our sales data to identify high-margin product categories and customer segments. We have SQL access to our warehouse and want actionable insights.\"\\nassistant: \"I'll analyze your sales data using SQL to profile revenue and margins by product and customer segment, then create visualizations showing the top performers and opportunities. This will help stakeholders prioritize which segments to focus on.\"\\n<commentary>\\nUse the data-analyst agent when you need to investigate business metrics, segment customers, or identify trends using queries and statistical methods. The agent will extract data, perform analysis, and present findings clearly to non-technical stakeholders.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Your company tracks KPIs across multiple departments and needs a consolidated dashboard to monitor business health.\\nuser: \"Can you build a dashboard that shows our key performance indicators? We need to track monthly revenue, user retention, support ticket volume, and conversion rates. It should update daily.\"\\nassistant: \"I'll develop a comprehensive BI dashboard connecting to your data sources, calculating the key metrics you need, and designing clear visualizations with interactive filters so stakeholders can drill down into the data they care about.\"\\n<commentary>\\nInvoke the data-analyst agent when you need to build BI dashboards, set up metric definitions, or create self-service reporting tools. The agent specializes in translating business requirements into clear, maintainable analytics infrastructure.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Your team suspects customer behavior has changed significantly in the past quarter and needs statistical evidence to support a strategic pivot.\\nuser: \"We think our user churn rate has increased recently. Can you analyze retention trends and determine if the change is statistically significant? We need to understand what's driving it.\"\\nassistant: \"I'll perform time series analysis on your retention data, conduct statistical hypothesis testing to confirm the change is significant, segment users to identify which groups are most affected, and provide visualizations with clear takeaways for leadership.\"\\n<commentary>\\nUse the data-analyst agent when you need statistical rigor to validate hypotheses, detect anomalies, or perform cohort analysis. The agent applies appropriate statistical methods and communicates findings in business terms.\\n</commentary>\\n</example>"
Prd
Generate a comprehensive Product Requirements Document (PRD) in Markdown, detailing user stories, acceptance criteria, technical considerations, and metrics. Optionally create GitHub issues upon user confirmation.
Postgresql Dba
Work with PostgreSQL databases using the PostgreSQL extension.
Task Planner
Task planner for creating actionable implementation plans - Brought to you by microsoft/edge-ai
Prompt Engineer
A specialized chat mode for analyzing and improving prompts. Every user input is treated as a prompt to be improved. It evaluates the prompt against a systematic framework of prompt engineering best practices, then generates a new improved prompt. Use this agent when you need to turn vague or incomplete instructions into precise, production-ready system prompts.
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai
Software Engineer Agent V1
Expert-level software engineering agent. Deliver production-ready, maintainable code. Execute systematically and specification-driven. Document comprehensively. Operate autonomously and adaptively.
Ms Sql Dba
Work with Microsoft SQL Server databases using the MS SQL extension.
Power Bi Data Modeling Expert
Expert Power BI data modeling guidance using star schema principles, relationship design, and Microsoft best practices for optimal model performance and usability.
Machine Learning Engineer
"Use this agent when you need to deploy, optimize, or serve machine learning models at scale in production environments. Specifically:\\n\\n<example>\\nContext: User has a trained ML model and needs to deploy it to handle real-time inference requests with minimal latency.\\nuser: \"I have a PyTorch model that needs to serve 1000+ requests per second. What's the best way to deploy this?\"\\nassistant: \"I'll use the machine-learning-engineer agent to analyze your model, optimize it for inference, and design a serving infrastructure that meets your latency and throughput requirements.\"\\n<commentary>\\nWhen users need production model deployment with strict performance requirements (latency, throughput, or scalability), use the machine-learning-engineer agent to design and implement the serving infrastructure.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User has multiple ML models running in production but they're consuming too much resources and causing slow responses.\\nuser: \"Our model serving is costing way too much in GPU resources, and inference latency is 500ms. Can we optimize this?\"\\nassistant: \"I'll invoke the machine-learning-engineer agent to analyze your current deployment, apply model optimization techniques like quantization and pruning, and reconfigure your serving infrastructure for better efficiency.\"\\n<commentary>\\nUse the machine-learning-engineer agent when optimizing existing model deployments for performance, reducing resource costs, or improving inference latency in production systems.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User is building an edge deployment system for ML models that must run on resource-constrained devices.\\nuser: \"We need to run our recommendation model on mobile devices. How do we compress and optimize it?\"\\nassistant: \"I'll use the machine-learning-engineer agent to implement model compression, select optimal hardware configurations, and set up update mechanisms for edge deployment.\"\\n<commentary>\\nWhen deploying models to edge devices with constraints on memory, compute, or power, use the machine-learning-engineer agent to handle model compression, hardware optimization, and offline capability.\\n</commentary>\\n</example>"
Task Researcher
Task research specialist for comprehensive project analysis - Brought to you by microsoft/edge-ai
Blueprint Mode
Executes structured workflows (Debug, Express, Main, Loop) with strict correctness and maintainability. Enforces an improved tool usage policy, never assumes facts, prioritizes reproducible solutions, self-correction, and edge-case handling.
Microsoft Agent Framework Dotnet
Create, update, refactor, explain or work with code using the .NET version of Microsoft Agent Framework.
Neon Optimization Analyzer
Identify and fix slow Postgres queries automatically using Neon's branching workflow. Analyzes execution plans, tests optimizations in isolated database branches, and provides clear before/after performance metrics with actionable code fixes.
Power Bi Dax Expert
Expert Power BI DAX guidance using Microsoft best practices for performance, readability, and maintainability of DAX formulas and calculations.
Power Platform Expert
Power Platform expert providing guidance on Code Apps, canvas apps, Dataverse, connectors, and Power Platform best practices
Se System Architecture Reviewer
System architecture review specialist with Well-Architected frameworks, design validation, and scalability analysis for AI and distributed systems
Neon Migration Specialist
Safe Postgres migrations with zero-downtime using Neon's branching workflow. Test schema changes in isolated database branches, validate thoroughly, then apply to production—all automated with support for Prisma, Drizzle, or your favorite ORM.
Simple App Idea Generator
Brainstorm and develop new application ideas through fun, interactive questioning until ready for specification creation.
Tdd Green
Implement minimal code to satisfy GitHub issue requirements and make failing tests pass without over-engineering.
Blueprint Mode Codex
Executes structured workflows with strict correctness and maintainability. Enforces a minimal tool usage policy, never assumes facts, prioritizes reproducible solutions, self-correction, and edge-case handling.
Demonstrate Understanding
Validate user understanding of code, design patterns, and implementation details through guided questioning.
Monday Bug Fixer
Elite bug-fixing agent that enriches task context from Monday.com platform data. Gathers related items, docs, comments, epics, and requirements to deliver production-quality fixes with comprehensive PRs.
Se Product Manager Advisor
Product management guidance for creating GitHub issues, aligning business value with user needs, and making data-driven product decisions
Adr Generator
Expert agent for creating comprehensive Architectural Decision Records (ADRs) with structured formatting optimized for AI consumption and human readability.
Tdd Red
Guide test-first development by writing failing tests that describe desired behaviour from GitHub issue context before implementation exists.
Code Tour
Expert agent for creating and maintaining VSCode CodeTour files with comprehensive schema support and best practices
Dotnet Maui
Support development of .NET MAUI cross-platform apps with controls, XAML, handlers, and performance best practices.
Semantic Kernel Dotnet
Create, update, refactor, explain or work with code using the .NET version of Semantic Kernel.
Clojure Interactive Programming
Expert Clojure pair programmer with REPL-first methodology, architectural oversight, and interactive problem-solving. Enforces quality standards, prevents workarounds, and develops solutions incrementally through live REPL evaluation before file modifications.
Amplitude Experiment Implementation
This custom agent uses Amplitude's MCP tools to deploy new experiments inside of Amplitude, enabling seamless variant testing capabilities and rollout of product features.
Hlbpa
Your perfect AI chat mode for high-level architectural documentation and review. Perfect for targeted updates after a story or researching that legacy system when nobody remembers what it's supposed to be doing.
Other Agents Categories
Catalog data and component content are sourced from the open-source davila7/claude-code-templates project (MIT license). ToolZip curates the listing and writes original descriptions; every component links back to its original source. Claude Code is a product of Anthropic. ToolZip is an independent catalog and is not affiliated with or endorsed by Anthropic.