doc-analyzer

Purpose

The doc-analyzer skill provides comprehensive capabilities for analyzing fetched library and framework documentation to extract actionable implementation guidance. This skill helps the Documentation Researcher agent identify relevant code patterns, best practices, common pitfalls, and integration strategies that enable high-quality feature implementations.

This skill emphasizes:

• Pattern Recognition: Identify reusable code patterns and architectural approaches
• Best Practice Extraction: Discover recommended practices from authoritative sources
• Example Compilation: Collect and categorize working code examples
• Pitfall Identification: Recognize common mistakes and antipatterns
• Integration Guidance: Extract patterns for combining libraries and frameworks

The doc-analyzer skill ensures that documentation research provides practical, actionable guidance rather than raw documentation dumps.

When to Use

This skill auto-activates when the agent describes:

• "Analyze documentation for..."
• "Extract patterns from..."
• "Identify best practices..."
• "Find examples of..."
• "Discover common pitfalls..."
• "Extract API usage patterns..."
• "Analyze integration approaches..."
• "Identify security considerations..."

Provided Capabilities

1. Code Pattern Extraction

What it provides:

• Initialization and setup patterns
• Common usage patterns
• Integration patterns between libraries
• Configuration patterns
• Testing patterns

Pattern Categories:

Initialization Patterns:

def extract_initialization_patterns(docs: dict) -> list:
    """
    Extract initialization and setup patterns from documentation.
    """
    keywords = [
        "setup", "initialize", "config", "configuration",
        "getting started", "first steps", "__init__", "setup.py"
    ]

    patterns = []
    for section in docs["sections"]:
        if contains_keywords(section, keywords):
            patterns.append({
                "type": "initialization",
                "title": section["title"],
                "code": extract_code_blocks(section),
                "description": section["description"],
                "prerequisites": extract_prerequisites(section)
            })

    return patterns

# Example extracted pattern
{
    "type": "initialization",
    "title": "FastAPI Application Setup",
    "code": """
from fastapi import FastAPI

app = FastAPI(
    title="My API",
    description="API description",
    version="1.0.0"
)
    """,
    "description": "Basic FastAPI application initialization with metadata",
    "prerequisites": ["fastapi installed", "Python 3.7+"]
}