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Claude Code를 위한 #1 AI 코딩 강화 툴킷

제품

  • 기능
  • AI 에이전트
  • 스킬
  • 랭킹
  • 프로바이더
  • 마켓플레이스

도구

  • 다운로드
  • 도구 진단
  • 도구 비교
  • 방법론
  • 공급업체 포털

리소스

  • 아티클
  • 문서
  • API 레퍼런스
  • 예제
  • 변경 로그

법적 정보

  • MIT 라이선스

© 2025 CCJK 팀. 모든 권리 보유.

Algorithm Transparency

Ranking Algorithm

Our ranking algorithm is fully transparent, based on a multi-dimensional scoring system, ensuring fair, objective, and authoritative AI tool rankings.

v2.0.0
Version
7
Dimensions
100%
Transparency
Hourly
Updates

Algorithm Overview

Multi-dimensional AI tool ranking algorithm with transparent scoring

Overall Score Formula

score = Σ(dimension_score × dimension_weight) / 100

All dimension scores are normalized to 0-100 range, weighted and summed to get the final overall score.

Weight Distribution

20%20%15%15%100%
성능20%
기능20%
커뮤니티15%
비용15%
문서10%
유지보수10%
사용자 경험10%

Scoring Dimensions

Performance

20%

Evaluates speed, response time, and execution efficiency. Normalized score based on benchmark tests and user reports.

• Average response time
• Processing speed
• Resource efficiency

Cost

15%

Evaluates price-to-value ratio and cost effectiveness. Weighted score considering pricing model and feature availability.

• Pricing model
• Cost per feature
• Free tier limitations

Features

20%

Evaluates feature completeness and capabilities. Based on feature count and quality assessment.

• Number of features
• Feature quality
• Unique capabilities

Community

15%

Evaluates community engagement and popularity. Logarithmic scale based on community metrics.

• GitHub stars
• User votes
• Active contributors

Documentation

10%

Evaluates documentation quality and completeness. Manual assessment and automated checks.

• Documentation availability
• Tutorial quality
• API reference completeness

Maintenance

10%

Evaluates project maintenance and update frequency. Time-based decay with recent updates weighted higher.

• Last update date
• Update frequency
• Issue response time

User Experience

10%

Evaluates user experience and ease of use. Based on user feedback and usability testing.

• Interface design
• Learning curve
• User satisfaction

Personalized Ranking

You can adjust the weights of each dimension according to your needs to generate personalized ranking results. The system automatically normalizes weights to ensure they sum to 100%.

Try Personalized RankingView API Docs

Transparency Commitment

Open Data

All ranking data is available via public API, accessible and verifiable by anyone.

Public Methodology

Algorithm details are fully public, ensuring transparency and credibility of the scoring process.

Update Log

Algorithm changes are logged and announced, maintaining communication with the community.

Community Driven

Community voting influences rankings, ensuring results reflect real user needs.