Python Data Pipeline Designer
- 231 installs
- 2 repo stars
- Updated January 25, 2026
- jorgealves/agent_skills
Design and implement data pipeline architectures using Python with guidance for optimization.
About
Real engineering skills for data pipeline design covering orchestration, error handling, and composability. Provides patterns for production-grade pipeline implementation.
- Small, composable pipeline patterns
- Real engineering experience-based guidance
Python Data Pipeline Designer by the numbers
- 231 all-time installs (skills.sh)
- +8 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #623 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
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| Installs | 231 |
|---|---|
| repo stars | ★ 2 |
| Last updated | January 25, 2026 |
| Repository | jorgealves/agent_skills ↗ |
What it does
Design and implement data pipeline architectures using Python with guidance for optimization.
Files
Python Data Pipeline Designer
Purpose and Intent
Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.
When to Use
- Project Setup: When initializing a new Python project.
- Continuous Integration: As part of automated build and test pipelines.
- Legacy Refactoring: When updating older Python codebases to modern standards.
When NOT to Use
- Non-Python Projects: This tool is specialized for the Python ecosystem.
Error Conditions and Edge Cases
- Missing Requirements: If the project lacks a requirements.txt or pyproject.toml.
- Incompatible Versions: If the project uses a Python version not supported by the tools.
Security and Data-Handling Considerations
- All analysis is performed locally.
- No source code or credentials are ever transmitted externally.
name: python-data-pipeline-designer
version: 1.0.0
description: Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.
inputs:
project_path:
type: string
description: Path to the Python project directory.
required: true
outputs:
report:
type: string
description: A detailed report of the analysis or actions performed.
capabilities:
- Automated analysis of Python project structure.
- Integration with standard Python tooling.
constraints:
- Requires a valid Python environment.
security:
- Operates locally on source files.
examples:
- input:
project_path: "."
output:
report: "Analysis complete. No issues found."
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