据 AlphaMoat 统计,截至 2026-09,Node.js Project Architecture 累计下载 1,729 次、获 0 个星标,在全部 5,078 个 AI 技能中排名第 4,627 位,在「开发编程」分类(577 个)中排名第 546 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 40 | 1,729 | - | - |
| SkillHub ↗ | 304 | 1,692 | - | — |
各平台的计数口径不同,统计时间不一致。
拆分路由、共享服务、数据库访问和前端资源,提供运行配置与管理编辑模式,示例涵盖游戏、工具和库。共享配置不含秘密,只公开白名单值,并在生产采用前校验已认证管理变更。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 1,729 | — | - |
| 2026-08 | 1,542 | +19.2% | - |
| 2026-07 | 1,294 | +22.4% | - |
| 2026-06 | 1,057 | +19.3% | - |
| 2026-05 | 886 | — | - |
| 2026-03 | 646 | — | - |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- 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**
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数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。