AgentSkillsCN

kouchou-ai-development

kouchou-ai仓库的本地开发环境搭建、构建与Lint命令、环境配置，以及部署辅助工具。当您需要启动服务、构建镜像、运行Lint或格式化代码，或与Azure/静态构建协作时，此技能将助您事半功倍。

SKILL.md
--- frontmatter
name: kouchou-ai-development
description: "Local development setup, build and lint commands, environment configuration, and deployment helpers for the kouchou-ai repo. Use when starting services, building images, running lint/format, or working with Azure/static builds."

Kouchou-AI Development

Overview

Use this skill for setup, build, and operational commands.

Local development setup

  • •Copy .env.example to .env before starting services.
  • •Start all services with docker compose up.
  • •Initialize frontend dependencies with make client-setup.
  • •Run the public viewer, admin, and dummy server with make client-dev -j 3.

Build and static exports

  • •Build all Docker images with make build.
  • •Generate static exports with make client-build-static.
  • •Build individual frontends with pnpm run build in apps/public-viewer/ or apps/admin/.

Linting and formatting

  • •Run root lint/format with pnpm run lint and pnpm run format.
  • •Run frontend linting with pnpm run lint inside each frontend app.
  • •Run backend linting with rye run ruff check . inside apps/api/.

Server development

  • •Run the API locally with rye run uvicorn src.main:app --reload --port 8000 in apps/api/.
  • •Use make lint/check and make lint/format in apps/api/.
  • •Use make lint/api-check and make lint/api-format for Docker-based linting.

Environment configuration

  • •Keep .env files scoped per service directory and reference .env.example for defaults.
  • •Restart and rebuild Docker images if you change environment variables that are baked at build time.

Pull Request workflow

  • •Follow .github/PULL_REQUEST_TEMPLATE.md when creating a PR.

Documentation conventions

  • •Add language identifiers to fenced code blocks in docs (for example, bash or text).

Azure deployment helpers

  • •Use make azure-setup-all for full Azure setup.
  • •Use make azure-build, make azure-push, make azure-deploy, and make azure-info for individual steps.

Local LLM notes

  • •Enable Ollama with docker compose --profile ollama up -d when GPU support is available.
  • •Plan for 8GB+ GPU memory for local LLM usage.