Claude Code Skill编写实战：从零构建高效AI代码生成器

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在复杂业务场景下使用 AI 生成代码时，开发者常遇到以下典型问题：

上下文丢失：当需求描述超过模型上下文窗口时，关键业务规则会被截断，导致生成代码与预期不符
风格不一致：同一功能在不同批次生成中可能出现变量命名、代码结构差异，增加维护成本
安全缺陷：模型可能生成包含敏感信息泄露或安全隐患的代码片段，如硬编码凭证、SQL 注入漏洞

维度	模板引擎	GPT-4	Claude
响应速度	<100ms	2-5s	1-3s
可解释性	高（固定逻辑）	低（黑箱模型）	中（可追溯 prompt）
规范遵守	严格	不稳定	可控
长代码生成	需人工拼接	自动续写	分块生成

business_context = """
Requirement: Create a Flask endpoint for user profile update
Input params: 
- user_id (int, required)
- email (str, validated format)
- phone (str with country code)
Security: JWT auth required
Response: JSON with updated fields
"""

coding_standard = """
Rules:
1. Use SQLAlchemy ORM
2. PEP8 compliance
3. Type hints for all functions
4. Error handling with HTTP codes
5. Async/await for DB operations
"""

safety_check = """
Validation:
1. Prevent SQL injection
2. Sanitize input/output
3. Rate limiting decorator
4. Log sensitive actions
"""

import asyncio
from anthropic import AsyncAnthropic
from typing import Optional, Dict

class CodeGenerator:
    def __init__(self, api_key: str):
        self.client = AsyncAnthropic(api_key=api_key)

    async def generate_code(
        self,
        prompt: str,
        max_retry: int = 3,
        temperature: float = 0.3
    ) -> Optional[Dict]:
        """
        Generate code with retry mechanism
        :param prompt: Complete prompt with layered instructions
        :param max_retry: Maximum API call attempts
        :param temperature: 0-1, lower for deterministic output
        """
        for attempt in range(max_retry):
            try:
                response = await self.client.completions.create(
                    model="claude-2",
                    prompt=prompt,
                    max_tokens_to_sample=4000,
                    temperature=temperature,
                )
                return self._validate_output(response.completion)

            except Exception as e:
                if attempt == max_retry - 1:
                    raise RuntimeError(f"Failed after {max_retry} retries: {str(e)}")
                await asyncio.sleep(2 ** attempt)  # Exponential backoff

    def _validate_output(self, raw_code: str) -> Dict:
        """Sanitize and validate generated code"""
        # Implement actual validation logic
        return {
            "code": raw_code,
            "warnings": self._detect_warnings(raw_code),
            "metrics": {"token_count": len(raw_code.split()),
                "import_check": self._verify_imports(raw_code)
            }
        }