gh-francyjglisboa-agent-skill-creator/docs/INTERNAL_FLOW_ANALYSIS.md

Agent-Skill-Creator Internal Flow: What Happens "Under the Hood"

🎯 Example Scenario

User Command:

"I'd like to automate what is being explained and described in this article [financial data analysis article content]"

🚀 Complete Detailed Flow

PHASE 0: Detection and Automatic Activation

0.1 User Intent Analysis

Claude Code analyzes the command and detects activation patterns:

DETECTED PATTERNS:
✅ "automate" → Workflow automation activation
✅ "what is being explained" → External content processing
✅ "in this article" → Transcribed/intent processing
✅ Complete command → Activates Agent-Skill-Creator

0.2 Meta-Skill Loading

# Claude Code internal system
if matches_pattern(user_input, SKILL_ACTIVATION_PATTERNS):
    load_skill("agent-creator-en-v2")
    activate_5_phase_process(user_input)

What happens:

• The agent-creator's SKILL.md is loaded into memory
• The skill context is prepared
• The 5 phases are initialized

---

PHASE 1: DISCOVERY - Research and Analysis

1.1 Article Content Processing

# Internal processing simulation
def analyze_article_content(article_text):
    # Structured information extraction
    workflows = extract_workflows(article_text)
    tools_mentioned = identify_tools(article_text)
    data_sources = find_data_sources(article_text)
    complexity_assessment = estimate_complexity(article_text)

    return {
        'workflows': workflows,
        'tools': tools_mentioned,
        'data_sources': data_sources,
        'complexity': complexity_assessment
    }

Practical Example - Financial Analysis Article:

ANALYZED ARTICLE CONTENT:
├─ Identified Workflows:
│  ├─ "Download stock market data"
│  ├─ "Calculate technical indicators"
│  ├─ "Generate analysis charts"
│  └─ "Create weekly report"
├─ Mentioned Tools:
│  ├─ "pandas library"
│  ├─ "Alpha Vantage API"
│  ├─ "Matplotlib for charts"
│  └─ "Excel for reports"
└─ Data Sources:
   ├─ "Yahoo Finance API"
   ├─ "Local CSV files"
   └─ "SQL database"

1.2 API and Tools Research

# Automatic WebSearch performed by Claude
WebSearch: "Best Python libraries for financial data analysis 2025"
WebSearch: "Alpha Vantage API documentation Python integration"
WebSearch: "Financial reporting automation tools Python"

1.3 AgentDB Enhancement (if available)

# Transparent AgentDB integration
agentdb_insights = query_agentdb_for_patterns("financial_analysis")
if agentdb_insights.success_rate > 0.8:
    apply_learned_patterns(agentdb_insights.patterns)

1.4 Technology Stack Decision

TECHNICAL DECISION:
✅ Python as primary language
✅ pandas for data manipulation
✅ Alpha Vantage for market data
✅ Matplotlib/Seaborn for visualizations
✅ ReportLab for PDF generation

---

PHASE 2: DESIGN - Functionality Specification

2.1 Use Case Analysis

def define_use_cases(workflows_identified):
    use_cases = []
    for workflow in workflows_identified:
        use_case = {
            'name': workflow['title'],
            'description': workflow['description'],
            'inputs': workflow['required_inputs'],
            'outputs': workflow['expected_outputs'],
            'frequency': workflow['frequency'],
            'complexity': workflow['complexity_level']
        }
        use_cases.append(use_case)
    return use_cases

Defined Use Cases:

USE CASE 1: Data Acquisition
- Description: Download historical stock data
- Input: List of tickers, period
- Output: DataFrame with OHLCV data
- Frequency: Daily

USE CASE 2: Technical Analysis
- Description: Calculate technical indicators
- Input: Price DataFrame
- Output: DataFrame with indicators
- Frequency: On demand

USE CASE 3: Report Generation
- Description: Create PDF report
- Input: Analysis results
- Output: Formatted report
- Frequency: Weekly

2.2 Methodology Definition

def specify_methodologies(use_cases):
    methodologies = {
        'data_validation': 'Data quality validation',
        'error_handling': 'Robust error handling',
        'caching_strategy': 'Data caching for performance',
        'logging': 'Detailed logging for debugging',
        'configuration': 'Flexible configuration via JSON'
    }
    return methodologies

---

PHASE 3: ARCHITECTURE - Structural Decision

3.1 Complexity Analysis (DECISION_LOGIC.md applied)

# Automatic evaluation based on article content
complexity_score = calculate_complexity({
    'number_of_workflows': 4,           # Data + Analysis + Reports + Alerts
    'workflow_complexity': 'medium',    # API calls + calculations + formatting
    'data_sources': 3,                  # Yahoo Finance + CSV + Database
    'estimated_code_lines': 2500,       # Above Simple Skill threshold
    'domain_expertise': ['finance', 'data_science', 'reporting']
})

# Architecture decision
if complexity_score > SIMPLE_SKILL_THRESHOLD:
    architecture = "complex_skill_suite"
else:
    architecture = "simple_skill"

In this example:

ANALYSIS RESULT:
✅ Multiple distinct workflows (4)
✅ Medium-high complexity
✅ Multiple data sources
✅ Estimate > 2000 lines of code
✅ Multiple domains of expertise

DECISION: Complex Skill Suite
GENERATED NAME: financial-analysis-suite-cskill

3.2 Component Structure Definition

def design_component_skills(complexity_analysis):
    if complexity_analysis.architecture == "complex_skill_suite":
        components = {
            'data-acquisition': 'Handle data sourcing and validation',
            'technical-analysis': 'Calculate indicators and signals',
            'visualization': 'Create charts and graphs',
            'reporting': 'Generate professional reports'
        }
    return components

3.3 Performance and Cache Planning

performance_plan = {
    'data_cache': 'Cache market data for 1 day',
    'calculation_cache': 'Cache expensive calculations',
    'parallel_processing': 'Process multiple stocks concurrently',
    'batch_operations': 'Batch API calls when possible'
}

---

PHASE 4: DETECTION - Keywords and Activation

4.1 Keyword Analysis

def determine_activation_keywords(workflows, tools):
    keywords = {
        'primary': [
            'financial analysis',
            'market data',
            'technical indicators',
            'investment reports'
        ],
        'secondary': [
            'automate analysis',
            'generate charts',
            'calculate returns',
            'data extraction'
        ],
        'domains': [
            'finance',
            'investments',
            'quantitative analysis',
            'stock market'
        ]
    }
    return keywords

4.2 Precise Description Creation

def create_skill_descriptions(components):
    descriptions = {}
    for component_name, component_function in components.items():
        description = f"""
        Component skill for {component_function} in financial analysis.

        When to use: When user mentions {determine_activation_keywords(component_name)}

        Capabilities: {list_component_capabilities(component_name)}
        """
        descriptions[component_name] = description
    return descriptions

---

PHASE 5: IMPLEMENTATION - Code Creation

5.1 Directory Structure Creation

# Automatically created by the system
mkdir -p financial-analysis-suite/.claude-plugin
mkdir -p financial-analysis-suite/data-acquisition/{scripts,references,assets}
mkdir -p financial-analysis-suite/technical-analysis/{scripts,references,assets}
mkdir -p financial-analysis-suite/visualization/{scripts,references,assets}
mkdir -p financial-analysis-suite/reporting/{scripts,references,assets}
mkdir -p financial-analysis-suite/shared/{utils,config,templates}

5.2 marketplace.json Generation

{
  "name": "financial-analysis-suite",
  "plugins": [
    {
      "name": "data-acquisition",
      "source": "./data-acquisition/",
      "skills": ["./SKILL.md"]
    },
    {
      "name": "technical-analysis",
      "source": "./technical-analysis/",
      "skills": ["./SKILL.md"]
    }
  ]
}

5.3 SKILL.md Files Creation

For each component, the system generates:

---
name: data-acquisition
description: Component skill for acquiring financial market data from multiple sources including APIs, CSV files, and real-time feeds.
---

# Financial Data Acquisition

This component skill handles all data acquisition needs for the financial analysis suite.

## When to Use This Component Skill
Use this skill when you need to:
- Download market data from APIs (Alpha Vantage, Yahoo Finance)
- Import data from CSV/Excel files
- Validate and clean financial data
- Store data in standardized format

5.4 Python Scripts Generation

# data-acquisition/scripts/fetch_data.py
import pandas as pd
import yfinance as yf
from datetime import datetime, timedelta

class FinancialDataFetcher:
    def __init__(self, config_file='config/data_sources.json'):
        self.config = self.load_config(config_file)

    def fetch_stock_data(self, tickers, period='1y'):
        """Fetch historical stock data for given tickers"""
        data = {}
        for ticker in tickers:
            try:
                stock = yf.Ticker(ticker)
                data[ticker] = stock.history(period=period)
                logger.info(f"Successfully fetched data for {ticker}")
            except Exception as e:
                logger.error(f"Failed to fetch data for {ticker}: {e}")
        return data

    def validate_data_quality(self, data):
        """Validate the quality of fetched data"""
        validation_results = {}
        for ticker, df in data.items():
            results = {
                'missing_data_pct': df.isnull().sum().sum() / df.size,
                'date_range': f"{df.index.min()} to {df.index.max()}",
                'completeness': len(df) / 252  # Trading days
            }
            validation_results[ticker] = results
        return validation_results

5.5 Configuration Files Creation

// shared/config/data_sources.json
{
  "api_keys": {
    "alpha_vantage": "YOUR_API_KEY_HERE",
    "quandl": "YOUR_QUANDL_KEY_HERE"
  },
  "data_sources": {
    "yahoo_finance": {
      "enabled": true,
      "rate_limit": 2000,
      "timeout": 30
    },
    "alpha_vantage": {
      "enabled": true,
      "rate_limit": 5,
      "timeout": 60
    }
  },
  "cache_settings": {
    "market_data_ttl": 86400,
    "calculations_ttl": 3600
  }
}

5.6 Documentation and References Generation

# data-acquisition/references/api_documentation.md

## Alpha Vantage API
- **Endpoint**: TIME_SERIES_DAILY
- **Rate Limit**: 5 calls/minute (free tier)
- **Data Format**: JSON
- **Authentication**: API Key in header

## Yahoo Finance (yfinance)
- **Library**: yfinance
- **Rate Limit**: None documented (be reasonable)
- **Data Format**: pandas DataFrame
- **Authentication**: None required

5.7 Main README Creation

# Financial Analysis Suite

Complete automated financial analysis system that processes market data, performs technical analysis, and generates professional investment reports.

## Quick Start
1. Install dependencies: `pip install -r requirements.txt`
2. Configure API keys in `shared/config/data_sources.json`
3. Install as Claude plugin: `/plugin marketplace add ./`
4. Use: "Analyze AAPL, MSFT, GOOG performance and create weekly report"

## Components
- **Data Acquisition**: Automated market data collection
- **Technical Analysis**: Indicator calculations and signal generation
- **Visualization**: Chart creation and trend analysis
- **Reporting**: Professional PDF report generation

5.8 Automatic Installation Test

# scripts/test_installation.py
def test_suite_installation():
    """Test that all components work correctly"""
    print("🧪 Testing Financial Analysis Suite installation...")

    # Test imports
    try:
        import pandas as pd
        import yfinance as yf
        import matplotlib.pyplot as plt
        print("✅ All dependencies imported successfully")
    except ImportError as e:
        print(f"❌ Missing dependency: {e}")
        return False

    # Test configuration
    try:
        with open('shared/config/data_sources.json') as f:
            config = json.load(f)
        print("✅ Configuration file loaded successfully")
    except FileNotFoundError:
        print("❌ Configuration file missing")
        return False

    # Test basic functionality
    try:
        test_data = yf.download('AAPL', period='1mo')
        if not test_data.empty:
            print("✅ Basic data fetching works")
        else:
            print("❌ Data fetching failed")
            return False
    except Exception as e:
        print(f"❌ Basic functionality test failed: {e}")
        return False

    print("🎉 All tests passed! Suite is ready to use.")
    return True

if __name__ == "__main__":
    test_suite_installation()

---

🎯 Final Result - What the User Receives

After approximately 45-90 minutes of autonomous processing, the user will have:

financial-analysis-suite-cskill/
├── .claude-plugin/
│   └── marketplace.json          ← Suite manifest
├── data-acquisition-cskill/
│   ├── SKILL.md                  ← Component skill 1
│   ├── scripts/
│   │   ├── fetch_data.py         ← Functional code
│   │   ├── validate_data.py      ← Validation
│   │   └── cache_manager.py      ← Cache
│   ├── references/
│   │   └── api_documentation.md  ← Documentation
│   └── assets/
├── technical-analysis-cskill/
│   ├── SKILL.md                  ← Component skill 2
│   ├── scripts/
│   │   ├── indicators.py         ← Technical calculations
│   │   ├── signals.py            ← Signal generation
│   │   └── backtester.py         ← Historical tests
│   └── references/
├── visualization-cskill/
│   ├── SKILL.md                  ← Component skill 3
│   └── scripts/chart_generator.py
├── reporting-cskill/
│   ├── SKILL.md                  ← Component skill 4
│   └── scripts/report_generator.py
├── shared/
│   ├── utils/
│   ├── config/
│   └── templates/
├── requirements.txt              ← Python dependencies
├── README.md                     ← User guide
├── DECISIONS.md                  ← Decision explanations
└── test_installation.py          ← Automatic test

Note: All components use the "-cskill" convention to identify that they were created by Agent-Skill-Creator.

🚀 How to Use the Created Skill

Immediately after creation:

# Install the suite
cd financial-analysis-suite
/plugin marketplace add ./

# Use the components
"Analyze technical indicators for AAPL using the data acquisition and technical analysis components"

"Generate a comprehensive financial report for portfolio [MSFT, GOOGL, TSLA]"

"Compare performance of tech stocks using the analysis suite"

---

🧠 Intelligence Behind the Process

What Makes This Possible:

1. Semantic Understanding: Claude understands the article's content, not just keywords
2. Structured Extraction: Identifies workflows, tools, and patterns
3. Autonomous Decision-Making: Chooses the appropriate architecture without human intervention
4. Functional Generation: Creates code that actually works, not templates
5. Continuous Learning: With AgentDB, improves with each creation

Differential Compared to Simple Approaches:

Simple Approach	Agent-Skill-Creator
Generates templates	Creates functional code
Requires programming	Fully autonomous
No architecture decision	Architecture intelligence
Basic documentation	Complete documentation
Manual testing	Automatic testing

Agent-Skill-Creator transforms articles and descriptions into fully functional, production-ready Claude Code skills! 🎉