vimagick/dockerfiles

:whale: A curated list of delicious docker recipes 🇺🇦🇮🇱 (Let's Fight Against Dictatorship)

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Summary Information

Updated 1 minute ago
Added to GitGenius on September 21st, 2026
Created on December 1st, 2014
Open Issues & Pull Requests: 63 (+0)
GitHub issues: Enabled
Number of forks: 781
Total Stargazers: 3,210 (+0)
Total Subscribers: 96 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 185.2 days
Mean response time: 898.3 days
90th percentile: 2378.2 days
Tracked items: 16

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Issue Activity (beta)

Open issues: 1
New in 7 days: 0
Closed in 7 days: 10
Avg open age: 404 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 10
Comments in 7 days: 10
Events in 7 days: 21

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Detailed Description

Dockerfiles is a curated collection of Docker recipes and configurations for containerizing applications.

The project addresses the need for ready-to-use Docker setups by providing a collection of Dockerfile and docker-compose configurations for various services and applications. Rather than building container images from scratch, developers can reference or adapt the recipes in this repository to quickly containerize their own projects. The approach is straightforward: each recipe demonstrates how to package a specific application or service into a Docker container, with accompanying compose files for multi-container orchestration where relevant.

This collection suits developers who want to learn Docker best practices through working examples or who need to quickly spin up containers for common services. It works well for self-hosted deployments where you want reference implementations before customizing them for your own infrastructure. The repository functions as a learning resource and a starting point rather than a production-ready framework, so adopters should expect to review and adapt recipes to their specific requirements rather than use them as-is.

The project receives sporadic updates with recipes added or modified at irregular intervals. Maintenance activity is distributed across multiple areas of the codebase rather than concentrated in any single component, suggesting a broad but inconsistent engagement with the collection.