Claude Skills for financial applications Oct 2025 • Skills Build financial dashboards and portfolio analytics using Claude's Excel, PowerPoint, PDF skills.

# Standard imports
import json
import os
import sys
from pathlib import Path
import pandas as pd

# Add parent directory for imports
sys.path.insert(0, str(Path.cwd().parent))

# Anthropic SDK
from anthropic import Anthropic
from dotenv import load_dotenv

# Our utilities
from file_utils import (
    download_all_files,
    print_download_summary,
)

# Load environment
load_dotenv(Path.cwd().parent / ".env")

# Configuration
API_KEY = os.getenv("ANTHROPIC_API_KEY")
MODEL = "claude-sonnet-4-6"

if not API_KEY:
    raise ValueError("ANTHROPIC_API_KEY not found. Please configure your .env file.")

# Initialize client
client = Anthropic(api_key=API_KEY)

# Setup directories
OUTPUT_DIR = Path.cwd().parent / "outputs" / "financial"
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
DATA_DIR = Path.cwd().parent / "sample_data"

print("✓ Environment configured")
print(f"✓ Output directory: {OUTPUT_DIR}")
print(f"✓ Data directory: {DATA_DIR}")

def create_skills_message(client, prompt, skills, prefix="", show_token_usage=True):
    """
    Helper function to create messages with Skills.
    
    Args:
        client: Anthropic client
        prompt: User prompt
        skills: List of skill dicts [{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}]
        prefix: Prefix for downloaded files
        show_token_usage: Whether to print token usage
    
    Returns:
        Tuple of (response, download_results)
    """
    response = client.beta.messages.create(
        model=MODEL,
        max_tokens=4096,
        container={"skills": skills},
        tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
        messages=[{"role": "user", "content": prompt}],
        betas=[
            "code-execution-2025-08-25",
            "files-api-2025-04-14",
            "skills-2025-10-02",
        ],
    )
    
    if show_token_usage:
        print(f"\n📊 Token Usage: {response.usage.input_tokens} in, {response.usage.output_tokens} out")
    
    # Download files
    results = download_all_files(client, response, output_dir=str(OUTPUT_DIR), prefix=prefix)
    return response, results

def format_financial_value(value, is_currency=True, decimals=0):
    """Format financial values for display."""
    if is_currency:
        return f"${value:,.{decimals}f}"
    else:
        return f"{value:,.{decimals}f}"

print("✓ Helper functions defined")

# Load financial statements
financial_statements = pd.read_csv(DATA_DIR / "financial_statements.csv")
print("📊 Financial Statements Overview:")
print(f" Shape: {financial_statements.shape}")
print(f" Categories: {len(financial_statements['Category'].unique())} financial metrics")
print(f" Quarters: {list(financial_statements.columns[1:5])}")
print()

# Load portfolio holdings
with open(DATA_DIR / "portfolio_holdings.json") as f:
    portfolio_data = json.load(f)
print("💼 Portfolio Overview:")
print(f" Portfolio: {portfolio_data['portfolio_name']}")
print(f" Total Value: ${portfolio_data['total_value']:,.2f}")
print(f" Holdings: {len(portfolio_data['holdings'])} stocks")
print(f" Cash Position: ${portfolio_data['cash_position']['amount']:,.2f}")
print(f" Total Return: {portfolio_data['performance_metrics']['total_return_percent']:.1f}%")
print()

# Convert holdings to DataFrame
portfolio_df = pd.DataFrame(portfolio_data["holdings"])
print("Top 5 holdings by value:")
print(portfolio_df.nlargest(5, "market_value")[["ticker", "name", "market_value", "unrealized_gain"]])

# Load quarterly metrics
with open(DATA_DIR / "quarterly_metrics.json") as f:
    quarterly_metrics = json.load(f)
print("📈 Quarterly Metrics Overview:")
print(f" Quarters available: {len(quarterly_metrics['quarters'])}")
print(f" Metrics per quarter: {len(quarterly_metrics['quarters'][0])} KPIs")