Docker 容器化全栈技能介绍
Docker 容器化全栈技能覆盖容器管理、镜像构建、Compose 编排、网络配置、数据卷挂载、调试排错和生产环境加固等核心能力，由社区用户开发维护，版本 1.0.1。
Read More项目容器化适配：用 Agent Skill 把仓库落成可部署的 Docker 方案
介绍 SkillHub 社区 Skill「项目容器化适配」（@indiv-zhudev/projcet-containerd）。它用 SKILL.md 约定六阶段流程：分析仓库与配置、容器内打包制品、生成运行镜像与 compose、外置配置模板，并输出 deploy 目录下的脚本与文档。文章说明适用场景、语言覆盖与限制，给出 skillhub CLI 安装命令及 Cursor 等客户端的 skills 目录示例，以及 compose 打包与启动的典型命令。资料来自 SkillHub 开放 API 与官方安装文档。
Read Moreadding-docker: Generate Production-Grade Docker Configurations for Your Application
adding-docker is an Agent Skill in awesome-cursor-skills. When a user asks to containerize an application, it follows the SKILL.md workflow to detect the runtime (e.g., package.json, go.mod), generates a multi-stage Dockerfile and .dockerignore, and adds docker-compose.yml when dependencies such as a database are present. The documentation emphasizes using Alpine images, never baking .env into the image, ignoring unrelated packages in a monorepo, and including a production HEALTHCHECK. Installation is done by placing SKILL.md in .cursor/skills/ so the Agent can discover it automatically.
Read MoreDeepSeek Harness (DSH) Plugin: dsh-podman
`dsh-podman` is a core plugin for DeepSeek Harness (DSH) that provides a containerized, isolated execution environment via Podman by routing shell execution and file access to a container rather than the host. It supports ephemeral project containers, multi-image builds, privilege escalation, daemon execution, and terminal operations inside containers. The plugin is intended for agent development that requires environment isolation, is maintained by Exagone313, and is distributed under the MIT License.
Read MoreLightweight Deployment of Flask: Rapid Online through Docker Containerization
This article introduces the method of containerizing and deploying Flask applications using Docker to address deployment issues caused by differences between development and production environments. The core advantages of Docker include environment consistency, strong isolation, light weight, and rapid deployment. The quick start process consists of four steps: first, prepare the Flask application (including app.py and requirements.txt); second, write a Dockerfile using the Python 3.9-slim base image, set the working directory, install dependencies, copy files, and configure the startup command; third, execute `docker build -t myflaskapp .` to build the image; finally, run the container with `docker run -p 5000:5000 myflaskapp` to start the application. Advanced techniques include multi-stage builds to reduce image size, data persistence through data volumes, and managing sensitive information with environment variables. The article also addresses common issues such as viewing logs and redeploying after code modifications. Docker containerization enables Flask applications to achieve "build once, run anywhere," significantly improving deployment efficiency and stability.
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