Self-hosted alternatives to airflow-dag-patterns
5 self-hosted AI Skills apps to use instead, best rated first.
Top picks
- C

claude-to-deerflow
Launchclaude-to-deerflow is an agent skill that lets an assistant send messages and questions to a running DeerFlow instance through its HTTP API. DeerFlow is an AI agent platform built on LangGraph that orchestrates sub-agents for research, code
Self-hosted
- O

open-notebook
Launchopen-notebook is an agent skill for organising research with Open Notebook, a self-hosted alternative to NotebookLM. It arranges sources, notes and AI conversations into research notebooks. Sources can be PDFs, web pages, audio, video and O
Self-hosted
- R

Reactive Resume
LaunchReactive Resume is a free and open-source resume builder for writing resumes and cover letters, checking the work, and tracking job applications. You create a free account, import an existing resume or start from scratch, and see the finish
Open sourceSelf-hosted
- S

self-hosted-funnel-launch
LaunchSelf-hosted funnel launch is an agent skill that takes a funnel from an empty install to published on infrastructure you control, using the Autonnel funnel builder, which is licensed Apache-2.0. The result covers a landing page, checkout, a
Self-hosted
- G

get-convex/convex-backend
LaunchConvex is an open-source reactive database for web app developers. It combines a database, a place to write server functions in pure TypeScript, and client libraries that keep the app's data live, so screens update as the data changes. Beca
Self-hosted
About airflow-dag-patterns

airflow-dag-patterns
Build production Apache Airflow DAGs
airflow-dag-patterns is an agent skill from the wshobson/agents collection for building production Apache Airflow DAGs. It gives an agent the patterns used in real data pipelines: DAG design principles, choosing and writing operators, using sensors to wait on external conditions, testing DAGs locally before they run in production, and setting Airflow up for production deployment. The skill is aimed at the common situations where pipelines go wrong, such as tangled dependencies, custom operators that are hard to reuse, and failed runs that are hard to debug. Its guidance applies when you are creating data pipeline orchestration with Airflow, designing DAG structures and dependencies, implementing custom operators and sensors, testing DAGs on a local machine, or debugging a failed DAG run. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 10.7K installs and passing results from three security audits at the time of the scan. It is a reference for an agent to follow, so the code it produces should still be reviewed and tested against your own Airflow version and infrastructure.
- Price
- Free
- Made by
- wshobson
- Category
- AI Skills
- Website
- github.com/wshobson/agents/airflow-dag-patterns