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Execute qualquer Skill no Manus com um clique

python-data-pipeline-designer

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Atualizado25 de janeiro de 2026 às 18:01

Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

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Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

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jorgealves

jorgealves/agent_skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Cientistas de dadosInformática e Matemática·SOC 15-2051

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SKILL.md

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name	python-data-pipeline-designer
description	Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

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.

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name	python-data-pipeline-designer
description	Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

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.