automation-data-pipeline-automation-skill-2026

name	automation-data-pipeline-automation-skill-2026
description	Disena procesos automatizados para data pipeline automation con control logico, trazabilidad completa y validacion operativa de extremo a extremo.
version	1.0.0
domain	automation-ai
quality_tier	expert
compatibility	["claude-code","codex"]
owner	yonatanguerrerosoriano
tags	["automation","process","ai","orchestration","2026"]
foundation_skills	["logic-of-programs-hoare","algorithm-correctness-invariants","distributed-systems-consistency-foundations","testing-verification-foundations","security-threat-modeling-foundations","debugging-causal-reasoning-foundations"]

Automation Data Pipeline Automation Skill 2026 Skill

Mission

Disena procesos automatizados para data pipeline automation con control logico, trazabilidad completa y validacion operativa de extremo a extremo.

When to use

• When the user asks for a repeatable workflow in this domain.
• When a specialized checklist improves speed or quality.

Inputs expected

• Task objective and expected output.
• Relevant files, paths, or system constraints.
• Any non-negotiable requirements (security, style, deadlines).

Workflow

1. Understand scope, assumptions, and risks.
2. Execute the workflow in a deterministic order.
3. Verify outcomes and report any limitations clearly.

Output contract

Provide results in this order: key outcome, concrete changes, validation status, next steps.

Guardrails

• Never fabricate facts, outputs, or tool results.
• Ask for confirmation before destructive operations.
• Prefer minimal, reversible changes when uncertain.

Foundations

• logic-of-programs-hoare
• algorithm-correctness-invariants
• distributed-systems-consistency-foundations
• testing-verification-foundations
• security-threat-modeling-foundations
• debugging-causal-reasoning-foundations

Logical reliability checklist

• Assumptions are explicit and separated from verified facts.
• The solution path is justified with clear reasoning steps.
• Edge cases and contradiction checks are included.
• Output is testable, auditable, and reversible when possible.

Example prompts

• "Apply the automation-data-pipeline-automation-skill-2026 skill to handle this task end-to-end."
• "Run automation-data-pipeline-automation-skill-2026 and produce a production-ready output with validation notes."