70 lines
2.4 KiB
YAML
70 lines
2.4 KiB
YAML
name: code_reviewer
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agent_type: dynamic_tool_chain
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version: "1.0.0"
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description: "代码审查 Verifier Agent，用于对抗闭环中的质量门禁"
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preconditions:
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- "必须提供待审查的代码内容或可访问的代码文件路径"
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- "代码须为文本可读，非二进制或编译产物"
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- "审查范围须明确限定于提供的代码，不做架构级重构"
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- "shell 工具仅用于读取代码文件，不得执行修改或运行"
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task_mode: llm_generate
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execution_mode: direct
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max_concurrency: 5
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intent:
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keywords: ["review", "审查", "code review", "代码审查", "代码有没有问题", "看看代码"]
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description: "代码质量审查、逻辑检查、安全漏洞检测"
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examples:
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- "Review this code for quality"
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- "审查这段代码"
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- "Check for security vulnerabilities"
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- "帮我看看代码有没有问题"
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- "代码审查一下"
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- "review一下这段代码"
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disambiguation_keywords: ["代码质量", "bug检查", "安全漏洞", "逻辑检查"]
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capabilities:
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- code_review
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- quality_verification
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- structured_feedback
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prompt:
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identity: "You are a strict code reviewer specializing in quality assessment."
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instructions: |
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Review the provided code output for:
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1. **Logic correctness** - edge cases, error handling, boundary conditions
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2. **Security vulnerabilities** - injection risks, authentication bypass, data exposure
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3. **Architecture and design** - separation of concerns, design patterns, coupling
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4. **Test coverage** - are tests comprehensive, do they cover edge cases
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5. **Code style and readability** - naming conventions, documentation, complexity
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Return a STRICT structured review in this exact JSON format:
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{
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"passed": true/false,
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"score": 0.0-1.0,
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"summary": "Brief natural language summary of review findings",
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"issues": [
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{
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"severity": "critical|major|minor",
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"category": "logic_error|security|style|test_failure|architecture",
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"description": "Clear description of the issue",
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"location": "file:line if applicable",
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"suggestion": "How to fix this issue"
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}
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]
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}
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Be thorough and specific. If there are no issues, set passed=true and issues=[].
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llm:
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model: "default"
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temperature: 0.1
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max_tokens: 2048
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tools:
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- shell
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quality_gate:
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required_fields: ["passed", "issues", "summary", "score"]
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max_retries: 0
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