💻

Code Analysis

👤 openlark 📦 v1.0.0 ⭐ 4.1 ⬇️ 504 下載
💻 開發程式設計 免費

📖 技能介紹


name: code-analysis description: Perform multi-dimensional advanced code analysis including knowledge graph generation, code quality evaluation, performance analysis, security review, architecture review, and test coverage analysis, outputting structured reports with actionable recommendations.


Code Analysis

Perform multi-dimensional advanced code analysis, outputting structured reports and improvement roadmaps.

Use Cases

Use when users need "code analysis", "code review", "code quality assessment", "performance analysis", or "security review".

Analysis Dimensions

Select one or more based on user needs:

1. Knowledge Graph Generation

  • Map relationships between components
  • Visualize dependencies
  • Identify architectural patterns

2. Code Quality Evaluation

  • Complexity metrics (cyclomatic complexity, cognitive complexity)
  • Maintainability index
  • Technical debt assessment
  • Code duplication detection

3. Performance Analysis

  • Identify performance bottlenecks
  • Memory usage patterns
  • Algorithm complexity analysis
  • Database query optimization recommendations

4. Security Review

  • Vulnerability scanning
  • Input validation checks
  • Authentication/authorization review
  • Sensitive data handling checks

5. Architecture Review

  • Design pattern adherence
  • SOLID principles compliance
  • Coupling and cohesion analysis
  • Module boundary assessment

6. Test Coverage Analysis

  • Coverage percentages

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  • Untested code paths
  • Test quality assessment
  • Missing edge cases

Workflow

  1. Read user-provided code files or directories
  2. Select analysis types based on user needs
  3. Execute analysis and generate comprehensive report
  4. Provide actionable improvement recommendations
  5. Prioritize optimization items by impact

Output Format

## Code Analysis Report

### Executive Summary
[Overall assessment overview, 1-2 paragraphs]

### Detailed Findings
#### [Dimension 1]
- Finding 1: xxx
- Finding 2: xxx

#### [Dimension 2]
- Finding 1: xxx

### Risk Assessment
| Risk Level | Issue | Impact Scope | Recommendation |
|------------|-------|--------------|----------------|
| 🔴 High | xxx | xxx | xxx |
| 🟡 Medium | xxx | xxx | xxx |
| 🟢 Low | xxx | xxx | xxx |

### Improvement Roadmap
1. Immediate fixes (high priority)
2. Short-term optimization (1-2 weeks)
3. Long-term improvements (1-3 months)

### Code Examples
[Code examples of key issues with improvement suggestions]

🤖 AI 評測

整體質量中等偏上,分析框架完整、結構規範。主要優勢是維度覆蓋全面、報告輸出格式專業。不足之處在於功能描述偏概念化,缺乏具體實現細節和質量保障機制,實際使用效果需要依賴呼叫者的程式碼理解能力。對於需要深度程式碼分析的使用者來說,這個Skill提供了良好的分析框架,但實際效果還需進一步驗證。

📊 多維度評分

適應性4.3
規範性4
有效性4.3
可靠性3.9
可信度4.3

📁 包含檔案 (3 個)

📄 SKILL.md 2.4 KB
📄 _meta.json 132 B
📄 skill-card.md 2 KB