apache-airflow-dags

Generate Apache Airflow DAGs with skeleton-first, contract-driven TaskFlow pipelines.

3|1|Updated Apr 12, 2026

Search Tags:#idempotency #pipeline architecture #data contracts #apache airflow #taskflow api #dag authoring #version safety

Ready to run

npx skills add https://github.com/Cogni-AI-OU/cogni-ai-agent-skills --skill apache-airflow-dags

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: apache-airflow-dags
Source: https://github.com/Cogni-AI-OU/cogni-ai-agent-skills/tree/main/apache-airflow-dags
Command: npx skills add https://github.com/Cogni-AI-OU/cogni-ai-agent-skills --skill apache-airflow-dags

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you author Apache Airflow Dags without brittle, version-specific code generation mistakes by enforcing a skeleton-first workflow and explicit data contracts.

Core Features & Use Cases

Skeleton strategy for maintainability: Draft a natural-language plan, generate an operator/DAG skeleton with placeholders, then iteratively replace placeholders with TaskFlow business logic.
Contract-driven DAG design: Define explicit input/output schemas and idempotency expectations (e.g., MERGE/UPSERT) to prevent hallucinated or ambiguous transformations.
Airflow version & execution-boundary guardrails: Force correct use of TaskFlow API patterns and respect execution boundaries (scheduler never runs user code; workers use the Execution API).

Quick Start

Ask for an Airflow Dag spec that includes the target Airflow version, executor type, schedule, and a precise data contract, and the Dag should be produced using the skeleton strategy and TaskFlow decorators.

Frequently Asked Questions about apache-airflow-dags

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I write Apache Airflow DAGs that prevent version drift and maintainability issues?▼

To write safe Apache Airflow DAGs, use a skeleton-first workflow that drafts a plan, generates structural placeholders, and iteratively applies TaskFlow business logic to enforce data contracts and prevent version drift.

What is the best way to enforce data contracts and idempotency in Airflow TaskFlow pipelines?▼

Enforcing data contracts in TaskFlow pipelines requires defining explicit input and output schemas alongside idempotency expectations like MERGE or UPSERT operations, preventing ambiguous transformations and ensuring deterministic pipeline architecture.

How do I structure Airflow DAGs to respect scheduler and worker execution boundaries?▼

Structuring Airflow DAGs correctly requires respecting execution boundaries by ensuring the scheduler never runs user code while workers utilize the Execution API, validated through provider availability checks and TaskFlow API patterns.

Can I refactor existing Airflow DAGs using a skeleton-first, contract-driven approach?▼

Yes, you can refactor existing Airflow DAGs by applying a skeleton-first strategy that replaces brittle code with deterministic structure placeholders, anchoring correctness with official documentation snippets and explicit data contracts.

Why does my Airflow DAG generation produce brittle, version-specific code mistakes?▼

Airflow DAG generation produces brittle code when lacking explicit environment context and deterministic structure placeholders, which causes version drift and maintainability issues without contract-driven design boundaries.