intent-solutions-io/nixtla-prod-pipeline-generator
003-skills/.claude/skills/nixtla-prod-pipeline-generator/SKILL.md
Transform forecasting experiments into Airflow/Prefect pipelines with monitoring. Use when deploying forecasts to production. Trigger with 'generate pipeline' or 'create Airflow DAG'.
$ install --global
skillsauthnpx skillsauth add intent-solutions-io/plugins-nixtla nixtla-prod-pipeline-generatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- nixtla-prod-pipeline-generator
- description:
- Transform forecasting experiments into Airflow/Prefect pipelines with monitoring. Use when deploying forecasts to production. Trigger with 'generate pipeline' or 'create Airflow DAG'.
- allowed-tools:
- Read,Write,Glob,Grep,Edit
- version:
- 1.0.0
- author:
- Jeremy Longshore <[email protected]>
- 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 stringFORECAST_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
-
Error:
Config file not foundSolution: Runnixtla-experiment-architectfirst to create config -
Error:
NIXTLA_API_KEY not setSolution: Export your TimeGPT API key or use StatsForecast baselines -
Error:
Database connection failedSolution: VerifyFORECAST_DATA_SOURCEconnection string -
Error:
Forecast quality check failedSolution: 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 productionizenixtla-timegpt-finetune-lab: Fine-tuned models for pipelinesnixtla-usage-optimizer: Cost-effective routing strategies
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