intent-solutions-io/nixtla-prod-pipeline-generator
003-skills/.claude/skills-backup-20251210-155310/nixtla-prod-pipeline-generator/SKILL.md
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.
$ 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:
- 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 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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