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An instruction-only architecture guide whose requirements and actions match its stated purpose, but it prescribes patterns (hot-reload admin endpoints and storing admin credentials in config.json) that can be insecure if implemented or deployed without care.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Node.js Project Architecture」。简介：Node.js project architecture standards for AI-assisted development. Enforces fi…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/abczsl520/nodejs-project-arch/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: nodejs-project-arch
description: Node.js project architecture standards for AI-assisted development. Enforces file splitting (<400 lines), config externalization, route modularization, and admin dashboards. Use when creating new Node.js projects, refactoring large single-file codebases, or when AI context window is being consumed by oversized files. Covers H5 games (Canvas/Phaser/Matter.js), data tools (crawlers/scrapers), content platforms, monitoring dashboards, API services, and SDK libraries.
---

# Node.js Project Architecture for AI-Friendly Development

Architecture standards that keep files small enough for AI agents to read/edit without blowing the context window.

## Core Rules

- Single file max **400 lines**, `index.html` max **200 lines**, `server.js` entry max **100 lines**
- All tunable values in `config.json`, loaded at runtime, editable via admin dashboard
- Backend: `routes/` by domain, `services/` for shared logic, `db.js` for database
- Frontend: HTML skeleton only, JS/CSS in separate files
- Every project gets `admin.html` + `routes/admin.js` for config hot-reload

## Project Type Selection

Determine project type, then read the corresponding reference:

| Type | Signals | Reference |
|------|---------|-----------|
| **H5 Game** | Canvas, Phaser, Matter.js, game loop, sprites | [references/game.md](references/game.md) |
| **Data Tool** | Crawler, scraper, scheduler, data sync, analytics | [references/tool.md](references/tool.md) |
| **Content/Utility** | Generator, library, publisher, file processing | [references/tool.md](references/tool.md) |
| **Dashboard/Monitor** | Charts, real-time, alerts, metrics | [references/tool.md](references/tool.md) |
| **API Service** | REST endpoints, middleware, microservice | [references/tool.md](references/tool.md) |
| **SDK/Library** | Shared module, build step, multi-consumer | [references/sdk.md](references/sdk.md) |

## Quick Start (All Types)

1. Identify project type from table above
2. Read the corresponding reference file
3. Create directory structure per the reference
4. Extract hardcoded values → `config.json`
5. Split large files by function (each <400 lines)
6. Add `routes/admin.js` + `admin.html`
7. Frontend: `config.js` fetches `/api/config` at startup, code reads `GAME_CONFIG.xxx` or `APP_CONFIG.xxx`
8. Test locally → backup → deploy

## config.json Pattern (Universal)

```javascript
// Server: load and serve config
const config = JSON.parse(fs.readFileSync('./config.json', 'utf8'));
app.get('/api/config', (req, res) => {
  const safe = { ...config };
  delete safe.admin; // strip secrets
  res.json(safe);
});

// Admin: hot-reload
app.post('/admin/config', requireAdmin, (req, res) => {
  fs.writeFileSync('./config.json.bak', fs.readFileSync('./config.json'));
  fs.writeFileSync('./config.json', JSON.stringify(req.body, null, 2));
  Object.assign(config, req.body);
  res.json({ ok: true });
});
```

## Admin Dashboard Pattern (Universal)

`admin.html` auto-generates form from config structure:
- Password login (`x-admin-password` header)
- Visual config editor with save + hot-reload
- Stats overview (users/data/uptime)
- Config backup history + restore

## Why This Matters

Large single files consume massive context tokens when AI reads them:
- 3000-line file → ~40K tokens per read (20% of 200K window)
- 200-line module → ~2.7K tokens per read (1.3% of window)
- **Result: 10-15 productive rounds vs 3-5 before context compression**