@claudeautopm/plugin-data

@claudeautopm/plugin-data

Data engineering, machine learning pipelines, and workflow orchestration specialists.

Installation

# Install the plugin package
npm install -g @claudeautopm/plugin-data

# Install plugin agents to your project
autopm plugin install data

Agents Included

Workflow Orchestration

• airflow-orchestration-expert - Apache Airflow DAG development
  • DAG design and best practices
  • Task dependencies and scheduling
  • Sensor and operator development
  • XCom for inter-task communication
  • Connection and variable management
  • Monitoring and alerting

ML Pipeline Development

• kedro-pipeline-expert - Kedro ML pipeline framework
  • Pipeline architecture
  • Data catalog management
  • Node and pipeline creation
  • Parameters and configuration
  • Testing and debugging
  • Production deployment

AI Workflow Automation

• langgraph-workflow-expert - LangGraph AI workflow orchestration
  • Graph-based workflow design
  • State management
  • Agent coordination patterns
  • LLM integration
  • Error handling and retries
  • Streaming and async workflows

Usage

In Claude Code

After installation, agents are available in your project:

<!-- CLAUDE.md -->
## Active Team Agents

<!-- Load data engineering agents -->
- @include .claude/agents/data/airflow-orchestration-expert.md
- @include .claude/agents/data/kedro-pipeline-expert.md

Or use autopm team load to automatically include agents:

# Load data engineering team
autopm team load data

# Or include in fullstack team
autopm team load fullstack

Direct Invocation

# Invoke agent directly from CLI
autopm agent invoke airflow-orchestration-expert "Design ETL DAG for data warehouse"

Agent Capabilities

Data Pipeline Orchestration

• Complex DAG design and scheduling
• Task dependency management
• Dynamic pipeline generation
• Resource allocation and optimization

ML Workflow Management

• End-to-end ML pipeline design
• Data versioning and lineage
• Experiment tracking
• Model deployment automation

AI Agent Orchestration

• Multi-agent coordination
• LLM workflow automation
• State machine design
• Tool integration patterns

Data Engineering

• ETL/ELT pipeline development
• Data quality validation
• Incremental processing
• Error handling and recovery

Examples

Airflow ETL Pipeline

@airflow-orchestration-expert

Create Airflow DAG for daily ETL:

Requirements:
- Extract from PostgreSQL source
- Transform data with pandas
- Load to BigQuery warehouse
- Data quality checks
- Email alerts on failure
- Retry logic with backoff

Schedule:
- Run daily at 2 AM UTC
- Handle time zones
- SLA monitoring

Include:
1. DAG definition
2. Custom operators
3. Data quality sensors
4. Alert configuration
5. Testing strategy

Kedro ML Pipeline

@kedro-pipeline-expert

Build ML pipeline for churn prediction:

Pipeline stages:
1. Data ingestion (multiple sources)
2. Feature engineering
3. Model training (XGBoost, LightGBM)
4. Model evaluation
5. Model deployment

Requirements:
- Modular pipeline design
- Data catalog for versioning
- Parameter management
- Cross-validation
- Model registry integration

Include:
1. Pipeline structure
2. Node implementations
3. Data catalog YAML
4. Parameters YAML
5. Testing suite

LangGraph AI Workflow

@langgraph-workflow-expert

Design multi-agent research workflow:

Agents:
- Research Agent (web search)
- Analysis Agent (data processing)
- Writer Agent (report generation)
- Reviewer Agent (quality check)

Workflow:
1. Research gathers information
2. Analysis processes findings
3. Writer creates draft
4. Reviewer validates quality
5. Loop back if quality < threshold

Requirements:
- State persistence
- Error recovery
- Streaming output
- Token usage tracking

Include:
1. Graph definition
2. Agent nodes
3. State management
4. Edge conditions
5. Testing examples

Complex Airflow Architecture

@airflow-orchestration-expert

Design multi-tenant data platform:

Requirements:
- 10+ data sources (APIs, databases, files)
- Dynamic DAG generation per tenant
- Parallel processing with pools
- Resource quotas per tenant
- Cost tracking and optimization
- Disaster recovery

Features:
- DAG factory pattern
- Custom operators for common tasks
- Centralized logging
- Metric collection
- Auto-scaling workers

Include:
1. Architecture diagram
2. DAG factory implementation
3. Custom operator library
4. Configuration management
5. Monitoring setup

Kedro Production Deployment

@kedro-pipeline-expert

Productionize Kedro pipeline:

Requirements:
- Docker containerization
- Kubernetes deployment
- CI/CD with GitHub Actions
- Model registry (MLflow)
- Monitoring and logging
- A/B testing support

Pipeline:
- Training pipeline (weekly)
- Inference pipeline (real-time)
- Evaluation pipeline (daily)

Include:
1. Dockerfile and docker-compose
2. Kubernetes manifests
3. CI/CD workflows
4. Deployment scripts
5. Monitoring dashboards

Configuration

Environment Variables

Some agents benefit from environment variables:

# Airflow
export AIRFLOW_HOME=/opt/airflow
export AIRFLOW__CORE__EXECUTOR=CeleryExecutor
export AIRFLOW__CORE__SQL_ALCHEMY_CONN=postgresql://...

# Kedro
export KEDRO_ENV=production
export KEDRO_LOGGING_CONFIG=conf/base/logging.yml

# LangGraph
export OPENAI_API_KEY=your-key
export LANGSMITH_API_KEY=your-key
export LANGSMITH_PROJECT=my-project

Agent Customization

You can customize agent behavior in .claude/config.yaml:

plugins:
  data:
    airflow:
      default_executor: CeleryExecutor
      default_retries: 3
      schedule_interval: '@daily'
    kedro:
      default_runner: SequentialRunner
      log_level: INFO
      data_catalog_type: local
    langgraph:
      llm_provider: openai
      model: gpt-4
      enable_tracing: true

Documentation

• Airflow Orchestration Expert Guide
• Kedro Pipeline Expert Guide
• LangGraph Workflow Expert Guide

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT ClaudeAutoPM Team

Links

• ClaudeAutoPM
• Plugin Documentation
• npm Package
• Issues