Understanding the Analysis Results

Topics are Common Themes

Each "topic" represents a distinct pattern of words that frequently appear together across Claude.md files. These aren't arbitrary groupings—they reveal how developers actually use and document Claude in real projects.

• Example: A topic containing words like "assistant", "role", "context" likely represents how developers configure Claude's behavior
• Example: Words like "test", "spec", "validate" suggest a topic about testing and quality assurance practices

Word Weights Show Importance

The numbers next to each word indicate how characteristic that word is of the topic. Higher weights mean the word is more central to that theme.

• High weight (0.8+): Core concept that defines this topic
• Medium weight (0.4-0.7): Important supporting concept
• Low weight (0.1-0.3): Related but peripheral concept

Topic Strength Indicates Prevalence

The strength percentage shows how dominant each topic is in the overall dataset. Stronger topics appear in more documents and represent more widespread practices.

• High strength: Nearly universal practices (most projects include this)
• Medium strength: Common but not universal patterns
• Low strength: Specialized or emerging practices

What This Means for You

These patterns help you understand:

• Industry Standards: What most developers include in their Claude documentation
• Best Practices: Common approaches that work across many projects
• Documentation Gaps: Areas where your Claude.md might be missing important elements
• Emerging Trends: New patterns in how teams integrate Claude into their workflows

Step 1: Data Collection

The app searches GitHub for files named claude.md (case-insensitive) using the GitHub API. It collects up to 500 files from public repositories to manage API rate limits and processing time.

📊 File Selection Process:

• Search Method: Uses GitHub's Code Search API with query filename:claude.md
• Selection Criteria: NOT random sampling - follows GitHub's "best match" algorithm
• GitHub's Ordering: Results prioritize popular, recently active, and well-maintained repositories
• Collection Process: Takes the first 500 results in order (100 files per API page)
• Bias Implications: Analysis tends to reflect "best practices" from popular repos rather than representing all Claude.md files equally

🔧 Technical Details:

• Uses GitHub's Code Search API with pagination (100 files per request)
• Falls back to GitHub Contents API for repositories without direct download URLs
• Respects GitHub API rate limits (5,000 requests/hour with authentication)
• Memory monitoring with garbage collection every 50 files

Step 2: Text Preprocessing

Raw text from claude.md files is cleaned and prepared for analysis:

• Tokenization: Break text into individual words using NLTK
• Filtering: Remove stopwords, punctuation, and very short words
• Lemmatization: Reduce words to their root forms (e.g., "running" → "run")
• Lowercase conversion: Normalize text case for consistency

Step 3: Topic Modeling

The preprocessed text is analyzed using Latent Dirichlet Allocation (LDA):

• Feature Extraction: Convert text to numerical vectors using CountVectorizer
• LDA Analysis: Discover 5 hidden topics across all documents
• Word Weights: Calculate importance of each word within topics
• Topic Interpretation: Extract top 10 most relevant words per topic

Step 4: Visualization Generation

Results are presented in a clean, interactive HTML visualization:

• Each topic displays its most representative words
• Word importance scores are shown as weights
• Topics are automatically labeled for easy interpretation

Step 5: Cleanup

The application maintains privacy and efficiency:

• Temporary files are automatically cleaned up
• Document data is cleared from memory after analysis
• No persistent storage of collected content