Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Zenobia0000/cursor-agentic-coding-templateWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/zenobia0000/cursor-agentic-coding-template/overview)<a href="https://agentmods.dev/rules/zenobia0000/cursor-agentic-coding-template/overview"><img src="https://agentmods.dev/badge/rules/zenobia0000/cursor-agentic-coding-template/overview/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/zenobia0000/cursor-agentic-coding-template/overview"><img src="https://agentmods.dev/badge/rules/zenobia0000/cursor-agentic-coding-template/overview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.01667 |
| Opus 5.5 | $0.00010 | $0.00667 |
| Sonnet 5.5 | $0.00005 | $0.00333 |
| Haiku 4.5 | $0.00002 | $0.00167 |
Grade A, and why
overview scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to overview — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🏗️ 後端架構
分層架構
Controller → Service → Repository → Database
↓ ↓ ↓
DTO Business Entity/Model
Logic
職責分離
- Controller: 處理 HTTP 請求/回應
- Service: 業務邏輯處理
- Repository: 資料存取層
- Model: 資料模型定義
🔐 API 設計
RESTful 原則
// ✅ Good
GET /api/users // 列表
GET /api/users/:id // 詳情
POST /api/users // 創建
PUT /api/users/:id // 完整更新
PATCH /api/users/:id // 部分更新
DELETE /api/users/:id // 刪除
// ❌ Bad
GET /api/getUserList
POST /api/deleteUser
回應格式
// 成功回應
{
"success": true,
"data": { /* actual data */ },
"meta": {
"timestamp": "2024-01-01T00:00:00Z",
"version": "1.0.0"
}
}
// 錯誤回應
{
"success": false,
"error": {
"code": "USER_NOT_FOUND",
"message": "User not found",
"details": { /* additional info */ }
}
}
狀態碼使用
- 200 OK: 成功取得資源
- 201 Created: 成功創建資源
- 204 No Content: 成功但無內容
- 400 Bad Request: 請求參數錯誤
- 401 Unauthorized: 未認證
- 403 Forbidden: 無權限
- 404 Not Found: 資源不存在
- 422 Unprocessable Entity: 驗證失敗
- 500 Internal Server Error: 伺服器錯誤
🛡️ 安全措施
認證授權
- JWT Token: 無狀態認證
- Refresh Token: 長期有效性
- 權限檢查: RBAC/ABAC
- Session 管理: Redis 儲存
資料驗證
// 使用 Zod 驗證
const createUserSchema = z.object({
email: z.string().email(),
password: z.string().min(8),
name: z.string().min(2).max(50),
age: z.number().min(0).max(150)
})
// 中間件驗證
const validate = (schema) => (req, res, next) => {
try {
schema.parse(req.body)
next()
} catch (error) {
res.status(422).json({ error })
}
}
SQL 注入防護
// ✅ Good: 參數化查詢
const user = await db.query(
'SELECT * FROM users WHERE id = $1',
[userId]
)
// ❌ Bad: 字串串接
const user = await db.query(
`SELECT * FROM users WHERE id = ${userId}`
)
💾 資料庫設計
Schema 設計原則
- 正規化: 至少到 3NF
- 索引策略: 查詢欄位建立索引
- 軟刪除: deleted_at 欄位
- 審計欄位: created_at, updated_at, created_by
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 257 lines · 24 tokens per session scan A 8e9d05145857
overview is a cursor rule published in the GitHub repository Zenobia0000/cursor-agentic-coding-template (5 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 1,667 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to overview, differing in 0 lines, and is treated as a copy.
Other cursor rules, from other repositories
mempalace-recall-always
Always-on MemPalace recall — search the palace before answering about past work, people, projects, or prior decisions.
dreamd-recall
Recall lessons, decisions, and prior context from the .agent/ memory daemon. Use when starting work in a project that has a .agent/ folder, when the user references a past decision, or when you are about to make a choice that has a documented prior.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
common_memory_bank
I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read…
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LLM Wiki OS operating rules.
memsprout
Cursor rule "memsprout" from memsprout/agents, covering using memsprout, search first, capturing, spaces and topics and files.