SOLID Docker/Compose 護欄：小型 Dockerfile、實務上非 root、映像中無機密資訊、固定基礎映像標籤、清晰的多階段建置。用於編輯 Dockerfile 或 Compose 檔案時。
此技能專注於在開發者編輯 Dockerfile 或 docker-compose 檔案時，提供一套強健的 SOLID 原則護欄。其主要目標是強制執行容器化最佳實踐，包括保持 Dockerfile 簡潔、使用多階段建置縮小執行階段映像、固定基礎映像標籤以避免意外更新，以及確保映像中不包含機密資料。技能涵蓋多種平台與設定，透過明確的檢查清單幫助開發者避免常見陷阱。支援環境變數文件化而非硬編碼，並鼓勵在可行情況下以非 root 使用者身分執行容器，從而提升安全性和可攜性。
npx skills add https://github.com/Nutz95/CursorCodingSkills/tree/HEAD/skills/solid-dockerInstalled? Explore more 影像 skills: steipete/songsee, affaan-m/frontend-design, mattpocock/improve-codebase-architecture · View all 6 →
latest）.dockerignore 排除 .venv、data/、.git 及本機設定檔This repo contains 3 individual skills — each has its own dedicated page.
>- SOLID Batch (.bat/.cmd) guardrails: thin wrappers around PowerShell/Python, setlocal, explicit errorlevel checks, no secrets. Use when editing Windows launchers or portable package entrypoints.
>- SOLID C++ / ESP32 firmware guardrails: file/class size, no nested classes, strict DI at composition roots, no UNIT_TEST / test ifdefs in production headers (and avoid them in .cpp), no hardcoded secrets, one type per public header, public API /// comments, dual-core radio pinning (PRO_CPU 0 / APP_CPU 1), FreeRTOS-safe patterns. Use when editing ESP-IDF, Arduino-ESP32, PlatformIO firmware, or ESP-NOW / BLE / Wi-Fi tasks.
>- SOLID CUDA 13 / GPU pipeline guardrails for Python+CUDA apps (e.g. NVDEC): concrete typing at boundaries, small modules, explicit device/resource lifetimes, no silent Any leakage. Use when editing CUDA 13, PyNvCodec, GPU decode/inference code.
Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.
Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
Add documentation for a new AI provider — usage docs, env vars, Docker config, image resources.
Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.