用 AI 做那些烦人的事：几个真正能省时间的提效套路

#!/usr/bin/env python3
"""从 Python 源码自动生成 pytest 测试用例"""

import ast
import os
import sys
from openai import OpenAI

# ---- 配置区：按你的环境修改 ----
API_KEY = os.getenv("OPENAI_API_KEY", "sk-xxx")
BASE_URL = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
MODEL = os.getenv("AI_MODEL", "gpt-4o-mini")
# ---- 配置区结束 ----

client = OpenAI(api_key=API_KEY, base_url=BASE_URL)


def extract_functions(source_path: str) -> list[dict]:
    """解析源文件，提取函数名、参数、docstring"""
    with open(source_path) as f:
        tree = ast.parse(f.read())
    funcs = []
    for node in ast.walk(tree):
        if isinstance(node, ast.FunctionDef):
            args = [a.arg for a in node.args.args]
            doc = ast.get_docstring(node) or ""
            funcs.append({"name": node.name, "args": args, "docstring": doc})
    return funcs


def generate_tests(source_path: str, funcs: list[dict]) -> str:
    """调用 LLM 为提取到的函数生成 pytest 测试"""
    source_code = open(source_path).read()
    func_descriptions = "\n".join(
        f"- `{f['name']}({', '.join(f['args'])})`: {f['docstring']}" for f in funcs
    )
    prompt = (
        f"你是测试工程师。根据以下 Python 源码和函数列表，生成完整的 pytest 测试文件。\n"
        f"要求：覆盖正常路径和边界情况；每个函数至少 2 个测试；"
        f"只输出代码，不要解释。\n\n"
        f"源码:\n```python\n{source_code}\n```\n\n"
        f"函数列表:\n{func_descriptions}"
    )
    resp = client.chat.completions.create(
        model=MODEL,
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3,
    )
    text = resp.choices[0].message.content
    # 剥掉 markdown 代码块包裹
    if text.startswith("```"):
        text = text.split("\n", 1)[1].rsplit("```", 1)[0]
    return text


def main():
    if len(sys.argv) < 2:
        print("用法: python gen_tests.py <源文件路径>")
        sys.exit(1)
    src = sys.argv[1]
    funcs = extract_functions(src)
    if not funcs:
        print("未找到函数定义，跳过")
        return
    test_code = generate_tests(src, funcs)
    out_path = os.path.splitext(src)[0] + "_test.py"
    with open(out_path, "w") as f:
        f.write(test_code)
    print(f"测试已写入 {out_path}，共 {len(funcs)} 个函数")
    print("下一步: pytest " + out_path + "  # 跑一遍，删掉失败的用例")


if __name__ == "__main__":
    main()

# 安装依赖
pip install openai

# 设置 API（支持任何 OpenAI 兼容接口，比如 DeepSeek、本地 Ollama）
export OPENAI_API_KEY="你的key"
export OPENAI_BASE_URL="https://api.deepseek.com/v1"  # 换成你用的服务
export AI_MODEL="deepseek-chat"

# 对任意 Python 文件生成测试
python gen_tests.py my_module.py

#!/usr/bin/env bash
# ai_commit.sh — 用 AI 生成 git commit message
# 依赖: git, curl, jq

# ---- 配置区 ----
API_KEY="${OPENAI_API_KEY:-sk-xxx}"
BASE_URL="${OPENAI_BASE_URL:-https://api.openai.com/v1}"
MODEL="${AI_MODEL:-gpt-4o-mini}"
# ---- 配置区结束 ----

DIFF=$(git diff --cached --stat && echo "---详细diff---" && git diff --cached)

PROMPT="根据以下 git diff 生成一条简洁的中文 commit message，格式: <类型>: <描述>。类型用 feat/fix/refactor/docs/chore。只输出一行，不要解释。

${DIFF}"

RESPONSE=$(curl -s "${BASE_URL}/chat/completions" \
  -H "Authorization: Bearer ${API_KEY}" \
  -H "Content-Type: application/json" \
  -d "$(jq -n \
    --arg model "$MODEL" \
    --arg prompt "$PROMPT" \
    '{model: $model, messages: [{role: "user", content: $prompt}], temperature: 0.2}' \
  )" \
)

MSG=$(echo "$RESPONSE" | jq -r '.choices[0].message.content' | sed 's/^"//;s/"$//')

echo "建议 commit message:"
echo "  $MSG"
echo ""
read -p "采用此 message? [Y/n/e(手动编辑)] " ACTION

case "$ACTION" in
  n) echo "跳过"; exit 1 ;;
  e) read -p "输入你的 message: " CUSTOM; git commit -m "$CUSTOM" ;;
  *) git commit -m "$MSG" ;;
esac

git add your_files
bash ai_commit.sh