Skills airflow-dag-patterns

📦

airflow-dag-patterns

Name: airflow-dag-patterns
Author: sickn33

Content revision r2 Safe ⚡ Contains scripts🌐 Network access⚙️ External commands

Build Reliable Apache Airflow DAGs

Airflow pipelines often fail because dependencies, retries, sensors, and tests are designed inconsistently. This skill provides reusable patterns for production DAG design and validation.

Supports: Claude Codex Code(CC)

🥉 78 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "airflow-dag-patterns" from https://skillstore.io/skills/sickn33-airflow-dag-patterns.md and its manifest at https://skillstore.io/api/skills/sickn33-airflow-dag-patterns/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/sickn33-airflow-dag-patterns.md Signed manifest GET /api/skills/sickn33-airflow-dag-patterns/manifest Signed lockfile GET /api/skills/sickn33-airflow-dag-patterns/lockfile

Test it

Using "airflow-dag-patterns". Design a daily customer import that waits for an S3 file and loads a warehouse table.

Expected outcome:

A staged DAG design with an S3 sensor, extract and load tasks, retry settings, timeout guidance, failure alerts, and dependency tests.

Using "airflow-dag-patterns". Review a DAG that passes a complete data frame through XCom.

Expected outcome:

The review flags oversized XCom payloads and recommends storing data externally while passing only a reference between tasks.

Using "airflow-dag-patterns". Plan tests for a branching Airflow pipeline.

Expected outcome:

Verify the DAG imports without errors.
Assert every branch target exists.
Test each quality threshold.
Confirm the join trigger rule accepts one successful branch.
Check the DAG for cycles.

Security Audit

Safe

v5 • 7/23/2026 Open versioned report

All ten static alerts are false positives caused by documentation examples, ordinary Python syntax, or Markdown backticks. No malicious intent, prompt injection, credential access, or data exfiltration was found.

Files scanned

560

Lines analyzed

Review items

False positives ignored

Risk Factors

⚡ Contains scripts (1)

resources/implementation-playbook.md:213

🌐 Network access (2)

resources/implementation-playbook.md:318 resources/implementation-playbook.md:318

⚙️ External commands (2)

SKILL.md:35 SKILL.md:44

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Open versioned report

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). airflow-dag-patterns security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-airflow-dag-patterns/audits/5

BibTeX citation

@techreport{sickn33-sickn33-airflow-dag-patterns-2026,
  author = {sickn33},
  title = {airflow-dag-patterns security audit report (audit version 5)},
  institution = {Skillstore},
  year = {2026},
  number = {5},
  url = {https://skillstore.io/skills/sickn33-airflow-dag-patterns/audits/5},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "airflow-dag-patterns security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
  - name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-airflow-dag-patterns/audits/5"
identifiers:
  - type: other
    value: "skillstore:sickn33-airflow-dag-patterns:audit:5"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

sickn33 Recommended Current

sickn33-airflow-dag-patterns

Skillstore Score 78

Evidence Confidence High

Skillstore usage 8

Updated

2026-08-21

wshobson

wshobson-airflow-dag-patterns

Skillstore Score 77

Evidence Confidence High

Skillstore usage 12

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Create a New Data Pipeline

Design an idempotent DAG with clear dependencies, retries, schedules, and observability.

Standardize Airflow Operations

Define shared sensor, alerting, testing, and deployment patterns across DAG repositories.

Validate Analytics Workflows

Review task order, data quality branches, catchup behavior, and safe retry handling.

Try These Prompts

Design a Basic DAG

Design a daily Airflow DAG for [source] to [target]. Include task dependencies, retries, ownership, start date, and catchup behavior.

Add Sensors and Alerts

Extend my DAG with a [dependency type] sensor and failure alerts. Use efficient sensor settings, timeouts, retry backoff, and clear trigger rules.

Build Dynamic DAGs

Create a dynamic DAG factory for these pipelines: [configurations]. Validate identifiers, schedules, source settings, shared defaults, and deterministic generation.

Review Production Readiness

Review this Airflow design: [design summary]. Identify risks involving idempotency, XCom size, concurrency, backfills, sensors, testing, observability, and deployment.

Best Practices

Keep tasks idempotent, atomic, observable, and safe to retry.
Use reschedule mode and explicit timeouts for long-running sensors.
Test DAG loading, dependencies, branching, schedules, and cycles before deployment.

Avoid

Do not pass large datasets through XCom or keep mutable global task state.
Do not place expensive processing or network calls at DAG import time.
Do not change production schedules or run backfills without impact review and approval.

Frequently Asked Questions

Which Airflow versions does this skill target?

The patterns primarily target Airflow 2 and its TaskFlow API. Confirm operator and provider compatibility with your installed version.

Can it generate complete production DAGs?

It can draft detailed DAG structures and patterns. You must supply environment-specific connections, schemas, credentials, schedules, and business logic.

Does it support dynamic DAG generation?

Yes. It provides a factory pattern that creates deterministic DAGs from pipeline configuration.

Can it help test existing DAGs?

Yes. It covers DagBag import checks, task counts, schedules, dependencies, cycle detection, and isolated task logic.

Does it configure cloud credentials?

No. Use approved Airflow connections or secret backends, and never place credentials directly in DAG source files.

How should backfills be handled?

Review data volume, idempotency, dependencies, concurrency, and duplicate-write risks. Test the backfill in staging before production approval.