# nixtla-prod-pipeline-generator

> Transforms forecasting experiments into production-ready inference pipelines with Airflow, Prefect, or cron orchestration. Generates ETL tasks, monitoring, error handling, and deployment configs. Activates when user needs to deploy forecasts to production, schedule batch inference, operationalize models, or create production pipelines.

- Author: jeremylongshore
- Repository: jeremylongshore/claude-code-plugins-nixtla
- Version: 20260109183454
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-08
- Source: https://github.com/jeremylongshore/claude-code-plugins-nixtla
- Web: https://mule.run/skillshub/@@jeremylongshore/claude-code-plugins-nixtla~nixtla-prod-pipeline-generator:20260109183454

---

---
name: nixtla-prod-pipeline-generator
description: Transforms forecasting experiments into production-ready inference pipelines with Airflow, Prefect, or cron orchestration. Generates ETL tasks, monitoring, error handling, and deployment configs. Activates when user needs to deploy forecasts to production, schedule batch inference, operationalize models, or create production pipelines.
allowed-tools: "Read,Write,Glob,Grep,Edit"
version: "1.0.0"
license: MIT
---

# Nixtla Production Pipeline Generator

Transform validated forecasting experiments into production-ready inference pipelines with proper orchestration, monitoring, and error handling.

## Overview

This skill productionizes Nixtla forecasting workflows by generating complete deployment artifacts:

- **Airflow DAGs**: Enterprise orchestration with dependencies and monitoring
- **Prefect Flows**: Modern Python-native pipelines with better local testing
- **Cron Scripts**: Simple single-machine batch processing

All pipelines implement: Extract -> Transform -> Forecast -> Load -> Monitor

## Prerequisites

**Required**:
- Python 3.8+
- Completed experiment in `forecasting/config.yml`
- One of: Airflow, Prefect, or cron access

**Environment Variables**:
- `NIXTLA_API_KEY`: TimeGPT API key (if using TimeGPT)
- `FORECAST_DATA_SOURCE`: Production data connection string
- `FORECAST_DESTINATION`: Output destination for forecasts

**Installation**:
```bash
pip install nixtla pandas statsforecast  # Core
pip install apache-airflow  # For Airflow
pip install prefect  # For Prefect
```

## Instructions

### Step 1: Read Experiment Config

Load experiment from `forecasting/config.yml`:
```bash
python {baseDir}/scripts/read_experiment.py --config forecasting/config.yml
```

### Step 2: Select Orchestration Platform

Choose based on requirements:
- **Airflow**: Enterprise, complex dependencies, extensive monitoring
- **Prefect**: Python-native, better local testing, modern error handling
- **Cron**: Simple single-machine, no dependencies, quick setup

### Step 3: Generate Pipeline

```bash
python {baseDir}/scripts/generate_pipeline.py \
    --config forecasting/config.yml \
    --platform airflow \
    --output pipelines/
```

### Step 4: Add Monitoring

```bash
python {baseDir}/scripts/add_monitoring.py \
    --pipeline pipelines/forecast_dag.py \
    --metrics smape,mase
```

### Step 5: Deploy

Follow generated `pipelines/README.md` for deployment instructions.

## Output

- **pipelines/forecast_dag.py**: Main pipeline file (Airflow/Prefect/Cron)
- **pipelines/monitoring.py**: Quality checks and fallback logic
- **pipelines/README.md**: Deployment instructions
- **pipelines/requirements.txt**: Dependencies

## Error Handling

1. **Error**: `Config file not found`
   **Solution**: Run `nixtla-experiment-architect` first to create config

2. **Error**: `NIXTLA_API_KEY not set`
   **Solution**: Export your TimeGPT API key or use StatsForecast baselines

3. **Error**: `Database connection failed`
   **Solution**: Verify `FORECAST_DATA_SOURCE` connection string

4. **Error**: `Forecast quality check failed`
   **Solution**: Pipeline auto-falls back to baseline models

## Examples

### Example 1: Airflow DAG

```bash
python {baseDir}/scripts/generate_pipeline.py \
    --config forecasting/config.yml \
    --platform airflow \
    --schedule "0 6 * * *" \
    --output pipelines/
```

**Output**:
```
Generated: pipelines/forecast_dag.py
Schedule: Daily at 6am
Tasks: extract -> transform -> forecast -> load -> monitor
```

### Example 2: Simple Cron Script

```bash
python {baseDir}/scripts/generate_pipeline.py \
    --config forecasting/config.yml \
    --platform cron \
    --output pipelines/
```

## Resources

- Scripts: `{baseDir}/scripts/`
- Templates: `{baseDir}/assets/templates/`
- Nixtla Docs: https://nixtla.github.io/

**Related Skills**:
- `nixtla-experiment-architect`: Creates experiments to productionize
- `nixtla-timegpt-finetune-lab`: Fine-tuned models for pipelines
- `nixtla-usage-optimizer`: Cost-effective routing strategies