content-creator
88Create Brand-Consistent Marketing Content
Marketing teams need content that stays consistent across channels. This skill helps plan, write, analyze, and optimize content for brand voice and SEO.
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.
Copy this request to your Agent. It includes the canonical Skill page and manifest.
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.
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
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:
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.
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
https://skillstore.io/skills/sickn33-airflow-dag-patterns/audits/5?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/sickn33-airflow-dag-patterns?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/sickn33-airflow-dag-patterns?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/sickn33-airflow-dag-patterns/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/sickn33-airflow-dag-patterns.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>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@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}
}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"
Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
sickn33-airflow-dag-patterns
2026-09-09
wshobson-airflow-dag-patterns
2026-09-09
Design an idempotent DAG with clear dependencies, retries, schedules, and observability.
Define shared sensor, alerting, testing, and deployment patterns across DAG repositories.
Review task order, data quality branches, catchup behavior, and safe retry handling.
Design a daily Airflow DAG for [source] to [target]. Include task dependencies, retries, ownership, start date, and catchup behavior.
Extend my DAG with a [dependency type] sensor and failure alerts. Use efficient sensor settings, timeouts, retry backoff, and clear trigger rules.
Create a dynamic DAG factory for these pipelines: [configurations]. Validate identifiers, schedules, source settings, shared defaults, and deterministic generation.
Review this Airflow design: [design summary]. Identify risks involving idempotency, XCom size, concurrency, backfills, sensors, testing, observability, and deployment.
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/airflow-dag-patternsRef
ebdfe608f5de2b66ff37ab4af12af8ac4f5e8006
Maintenance freshness
7/26/2026
Usage
5 downloads ยท 132 views
File structure
Create Brand-Consistent Marketing Content
Marketing teams need content that stays consistent across channels. This skill helps plan, write, analyze, and optimize content for brand voice and SEO.
Improve LLM Prompts With Proven Patterns
Inconsistent prompts waste time and make AI outputs hard to trust. This skill guides prompt design with reusable patterns, examples, evaluation steps, and optimization workflows.
Review Architecture Workflows and Project Paths
Architecture reviews need a consistent starting point and clear project boundaries. This skill provides workspace-scoped tool templates, command examples, and generic design references.
Review Fullstack Project Foundations
Fullstack project setup can require many tools and checks. This skill provides basic local wrappers, common commands, and general engineering guidance.
Optimize AI Prompts With Prompt Engineer
Writing clear prompts is hard when goals are vague or complex. This skill turns rough requests into structured prompts using proven prompt frameworks.
Prioritize Product Work With Research Insights
Product teams need faster ways to rank features, synthesize interviews, and document decisions. This skill provides RICE scoring, interview analysis, and PRD templates for structured planning.
Design Reliable Data Engineering Systems
by davila7
Data teams need clear decisions for reliable pipelines, models, and operations. This skill provides architecture guidance, production checklists, and starter CLI scaffolds.
Coordinate Data Pipelines and Quality Checks
by Brownbull
Data pipeline work can become inconsistent when ingestion, features, quality, and reporting are planned separately. This skill organizes data tasks around pipeline context, quality gates, lineage, and handoffs.
Create Bauplan Data Pipelines
by BauplanLabs
Bauplan projects need clear DAG structure, branch safety, and consistent SQL or Python model patterns. This skill guides Claude, Codex, and Claude Code through pipeline files, validation, and safe run workflows.
Build Reliable Data Pipelines and Warehouses
by zl2023github
Data teams need dependable pipelines across fragmented platforms. This skill designs implementation-ready batch, streaming, modeling, orchestration, and quality workflows.
Build Declarative Data Pipelines with mloda
by mloda-ai
AI agents often generate fragile data-fetching and transformation code. This skill uses mloda feature requests, plugins, and lineage to build deterministic data workflows.
Build CocoIndex Data Pipelines
by 89jobrien
AI data projects need reliable indexing flows, custom transforms, and operational commands. This skill guides CocoIndex design, implementation, testing, and troubleshooting.