Borrowing it
Nothing to install: this file belongs to MisonL/Ling. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MisonL/Ling/main/.agents/skills/python-patterns/SKILL.mdgit clone --depth 1 https://github.com/MisonL/LingWrote 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/skills/misonl/ling/python-patterns)<a href="https://agentmods.dev/skills/misonl/ling/python-patterns"><img src="https://agentmods.dev/badge/skills/misonl/ling/python-patterns/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/skills/misonl/ling/python-patterns"><img src="https://agentmods.dev/badge/skills/misonl/ling/python-patterns.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 | $0.00043 | $0.02860 |
| Opus 5.5 | $0.00017 | $0.01144 |
| Sonnet 5.5 | $0.00009 | $0.00572 |
| Haiku 4.5 | $0.00004 | $0.00286 |
Grade A, and why
python-patterns 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.
How it starts
The opening of the file, as written. The whole thing — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python 模式
面向 2025 的 Python 开发原则与决策方法。
学习如何思考，不要只记模式。
[WARN] 本技能使用方式
本技能强调决策原则，不是固定代码模板。
- 需求不明确时，先询问用户框架偏好
- 根据 Context（上下文）决定 async（异步）或 sync（同步）
- 不要每次都默认同一框架
1. 框架选型（2025）
决策树
你要构建什么？
|
+-- API 优先（API-first）/ 微服务
| +-- FastAPI（async、现代、速度快）
|
+-- 全栈 Web / CMS / 管理后台
| +-- Django（开箱即用，batteries-included）
|
+-- 简单应用 / 脚本 / 学习
| +-- Flask（极简、灵活）
|
+-- AI/ML API 服务化
| +-- FastAPI（Pydantic、async、Uvicorn）
|
+-- 后台任务
+-- Celery + 任意 Web 框架
对比原则
| 维度 | FastAPI | Django | Flask |
|---|---|---|---|
| 适用场景 | API、微服务 | 全栈、CMS | 简单项目、学习 |
| 异步支持 | 原生支持 | Django 5.0+ | 依赖扩展 |
| 管理后台 | 手动构建 | 内置 Admin（管理后台） | 依赖扩展 |
| ORM（对象关系映射） | 自由选择 | Django ORM | 自由选择 |
| 学习曲线 | 低 | 中 | 低 |
选型前必须询问：
- 是纯 API 还是全栈应用？
- 是否需要后台管理界面？
- 团队是否熟悉 async？
- 是否有现有基础设施约束？
2. 异步与同步决策
何时使用异步（async）
async def（异步函数）更适合：
+-- I/O 密集操作（数据库、HTTP、文件）
+-- 大量并发连接
+-- 实时交互场景
+-- 微服务通信
+-- FastAPI/Starlette/Django ASGI（异步服务器网关接口）
def（同步函数）更适合：
+-- CPU 密集操作
+-- 简单脚本
+-- 遗留代码库
+-- 团队不熟悉 async
+-- 依赖阻塞型库（无 async 版本）
黄金法则
I/O bound（I/O 密集）-> async（等待外部资源）
CPU bound（CPU 密集）-> sync + multiprocessing（本地计算）
不要：
+-- 随意混用 sync 与 async
+-- 在 async 代码里调用阻塞库
+-- 为 CPU 任务强行上 async
异步库选型
| 需求 | 异步库 |
|---|---|
| HTTP 客户端 | httpx |
| PostgreSQL | asyncpg |
| Redis（内存数据库） | aioredis / redis-py async |
| 文件 I/O | aiofiles |
| 数据库 ORM | SQLAlchemy 2.0 async, Tortoise |
3. 类型标注策略
哪些地方要标注
必须标注：
+-- 函数参数
+-- 返回类型
+-- 类属性
+-- 对外公开 API
可省略：
+-- 局部变量（让推断工作）
+-- 一次性脚本
+-- 测试代码（通常可选）
常见类型模式
# 下面是模式示例，请理解其语义：
# Optional（可选）-> 可能为 None
from typing import Optional
def find_user(id: int) -> Optional[User]: ...
# Union（联合类型）-> 多类型之一
def process(data: str | dict) -> None: ...
# 泛型集合
def get_items() -> list[Item]: ...
def get_mapping() -> dict[str, int]: ...
# Callable（可调用）
from typing import Callable
def apply(fn: Callable[[int], str]) -> str: ...
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 · 442 lines · 43 tokens per session scan A 8de8dd5e7263
python-patterns is a skill published in the GitHub repository MisonL/Ling (8 stars, last pushed 7mo ago), licensed MIT. It adds 43 tokens to every session and 2,860 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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