nixtla-prod-pipeline-generator

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:

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:

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

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

Step 4: Add Monitoring

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

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

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