CCJK (Claude Code JinKu) is a powerful CLI toolkit that transforms Claude Code setup from hours to seconds. With zero-configuration initialization, professional workflow templates, and multi-provider API support.

How do I install CCJK?

Simply run "npx ccjk" in your terminal. For global installation, use "npm install -g ccjk".

Yes, CCJK is 100% free and open source under the MIT license.

What AI providers does CCJK support?

CCJK supports 13+ AI providers including Anthropic, OpenRouter, DeepSeek, Groq, Gemini, Ollama, and various Chinese providers like 302.AI, Qwen, and SiliconFlow.

Transparent & Data-Driven

Our Ranking Methodology

Discover how we evaluate and rank AI tools using transparent, data-driven methods. Mathematical rigor meets practical insights.

View Algorithm Verify Data

Why Methodology Matters

In a world flooded with AI tools, making the right choice requires more than marketing claims. Our methodology combines rigorous data collection, mathematical formalization, and transparent processes to help you make confident decisions.

500+

Tools Tracked

50,000+

Data Points Analyzed

Weekly

Update Frequency

Our Core Principles

Data-Driven: Every ranking is backed by quantifiable metrics, not opinions
Transparent: Our methodology is public and verifiable by anyone
Objective: We use mathematical formulas to eliminate bias
Fresh: Weekly updates ensure rankings reflect current performance

Data Collection Process

We collect data from multiple authoritative sources to ensure accuracy and completeness. Our automated systems run continuously to keep data fresh.

Data Collection Pipeline

Source Discovery

Data Extraction

Validation

Normalization

Storage

🐙

GitHub API

Stars, forks, issues, commits, contributors, and activity metrics from official repositories

📊

Official Documentation

Feature lists, pricing information, supported platforms, and technical specifications

👥

Community Feedback

User reviews, ratings, success stories, and real-world usage patterns

🔍

Performance Testing

Response time, uptime monitoring, API reliability, and benchmark results

Scoring Algorithm

Our ranking algorithm evaluates tools across 7 dimensions, each weighted based on importance to developers. The final score is a weighted sum normalized to 0-100.

Mathematical Formalization

S_total = Σ(w_i × s_i) where i ∈ {1, 2, ..., 7}

Where:

S_total = Total weighted score (0-100)
w_i = Weight coefficient for dimension i
s_i = Normalized score for dimension i (0-100)

7 Scoring Dimensions

1. Performance

w₁ = 0.20

Response time, throughput, latency, and computational efficiency

s₁ = (1 / avg_response_time) × k₁

2. Cost Efficiency

w₂ = 0.15

Pricing model, value for money, free tier availability, and cost predictability

s₂ = (features / price) × k₂

3. Feature Completeness

w₃ = 0.20

Breadth of features, depth of capabilities, and unique functionalities

s₃ = (implemented_features / total_features) × 100

4. Community & Ecosystem

w₄ = 0.15

GitHub stars, community size, plugin ecosystem, and third-party integrations

s₄ = log₁₀(stars + forks + contributors) × k₄

5. Documentation Quality

w₅ = 0.10

Completeness, clarity, examples, tutorials, and API reference quality

s₅ = (doc_completeness + doc_clarity) / 2

6. Maintenance Activity

w₆ = 0.10

Update frequency, issue response time, bug fix rate, and development velocity

s₆ = (commits_last_90_days / 90) × k₆

7. User Experience

w₇ = 0.10

Ease of use, learning curve, UI/UX quality, and user satisfaction ratings

s₇ = avg_user_rating × 20

Score Normalization

All raw scores are normalized to a 0-100 scale using min-max normalization to ensure fair comparison across different metrics.

s_normalized = (s_raw - min) / (max - min) × 100

Update Frequency & Freshness

We believe fresh data is critical for accurate rankings. Our automated systems continuously collect and update data to reflect the latest tool performance.

Update Schedule

Weekly
Full ranking recalculation with all metrics
Daily
GitHub metrics (stars, forks, commits)
Hourly
Performance monitoring and uptime checks
Real-time
User reviews and community feedback

Data Freshness Guarantee

GitHub Data< 24 hours

Performance Metrics< 1 hour

Pricing Info< 7 days

Last Full Update

2 hours ago

Transparency & Public Changelog

We believe in radical transparency. Every change to our methodology is documented and publicly available. You can verify our data and challenge our rankings.

Methodology Changelog

2026-01-15

Added User Experience dimension

Introduced UX scoring based on user satisfaction ratings and ease of use metrics

2026-01-10

Updated weight distribution

Increased Performance weight from 0.15 to 0.20 based on community feedback

2025-12-20

Enhanced GitHub metrics

Added contributor count and commit frequency to community scoring

2025-12-01

Improved normalization algorithm

Switched to min-max normalization for better score distribution

2025-11-15

Launched methodology page

Published transparent methodology documentation for public review

View Full Changelog →

Open Data Access

Download our complete dataset including raw metrics, calculated scores, and historical data. Verify our rankings yourself.

Download Dataset

Public API

Access our ranking data programmatically via our public API. Build your own tools and analyses.

View API Docs

How to Verify Our Rankings

Don't just trust us—verify our data yourself. We provide multiple ways for you to validate our rankings and methodology.

🔍

Cross-Reference Sources

Compare our data with official GitHub repos, documentation, and public APIs

📊

Download Raw Data

Access our complete dataset and recalculate scores using our published formulas

🧮

Use Our API

Query individual metrics and verify calculations programmatically

Found an Error?

If you discover inaccurate data or calculation errors, please report them. We review all submissions and update rankings accordingly.

Report an Issue

How We're Different

Unlike other ranking sites, we prioritize transparency, mathematical rigor, and verifiability over subjective opinions.

Feature	Claude Home	Others
Public Methodology	✓	✗
Mathematical Formalization	✓	✗
Open Data Access	✓	✗
Public API	✓	Paid
Update Frequency	Weekly	Monthly
Data Sources	4+ sources	1-2 sources
Scoring Dimensions	7 dimensions	3-4 dimensions
Community Verification	✓	✗

Ready to Explore Rankings?

Now that you understand our methodology, explore our data-driven rankings and find the perfect AI tools for your needs.

View Rankings Compare Tools