據 AlphaMoat 統計,截至 2026-09,Node.js Project Architecture 累計下載 1,729 次、獲 0 個星標,在全部 5,078 個 AI 技能中排名第 4,627 位,在「開發程式設計」分類(577 個)中排名第 546 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 40 | 1729 | - | - |
| SkillHub ↗ | 304 | 1692 | - | — |
各平台的計數口徑不同,統計時間不一致。
拆分路由、共享服務、資料庫訪問和前端資源,提供執行配置與管理編輯模式,示例涵蓋遊戲、工具和庫。共享配置不含秘密,只公開白名單值,並在生產採用前校驗已認證管理變更。
資料釋出於 2026-10-27
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 1729 | — | - |
| 2026-08 | 1542 | +19.2% | - |
| 2026-07 | 1294 | +22.4% | - |
| 2026-06 | 1057 | +19.3% | - |
| 2026-05 | 886 | — | - |
| 2026-03 | 646 | — | - |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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**
用 gh 命令列工具檢視 GitHub 上的 PR 檢查、工作流執行與 API 查詢的技能。
聚合 16 個搜尋引擎、無需 API Key 的檢索技能。
用命令列直接列出、配置、授權並呼叫 MCP 服務與工具。
生成有效的 Mermaid 圖表,如流程圖、時序圖、思維導圖、ER 圖、使用者旅程等。
用受管 OAuth 訪問 YouTube Data API,檢索影片並管理頻道內容。
透過受管的 OAuth 連線訪問 GitHub REST API。
覆蓋規劃、實現、驗證與測試的編碼工作流技能。
整理 Docker 常用命令與容器工作流的技能。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。