Skills：金融应用

Skills 在金融场景的实践

🌐 查看英文原文 | 源码 Notebook

Claude Skills for Financial Applications

使用 Claude 的 Excel、PowerPoint 和 PDF 技能构建真实的金融仪表板、投资组合分析和自动化报告工作流程。

💡 实际影响： 这些技能与 Claude Creates Files 中的技能相同，使 Claude 能够直接在界面中创建专业的金融文件。

你将学习到： - 使用公式和图表在 Excel 中创建全面的财务模型 - 从财务数据生成高管演示文稿 - 使用风险指标构建投资组合分析工具 - 自动化多格式报告管道

先决条件

本笔记本假设你已经完成了 笔记本 1：Skills 简介。

如果你还没有： 1. 首先完成笔记本 1 中的设置 2. 使用测试单元验证你的环境 3. 确保你可以创建和下载文件

所需： - 配置 Anthropic API 密钥 - 安装 SDK 版本 0.69.0（从 whl 安装） - 激活虚拟环境

1. 设置与数据加载

让我们首先导入我们的依赖项并加载在本笔记本中我们将要使用的数据。

请注意：以下代码块将展示如何设置和加载数据。

# 代码示例
``````python
# 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}")
```### 加载财务数据

我们有四个数据集，代表公司财务状况的不同方面：

- [数据集1](#)
- [数据集2](#)
- [数据集3](#)
- [数据集4](#)

| 数据集 | 描述 |
| --- | --- |
| 数据集1 | ... |
| 数据集2 | ... |
| 数据集3 | ... |
| 数据集4 | ... |

::: callout-注意
请注意，以下代码块中的`RAG`和`embedding`是技术术语，应保持英文。
:::

```python
# 加载数据
data = load_data()

# 处理数据
processed_data = process_data(data)

# 使用RAG模型进行嵌入
embeddings = rag_model(processed_data)

# 使用API进行查询
response = api_query(embeddings)

# 将结果转换为JSON格式
json_result = json.dumps(response)

# 将结果存储到数据库
sql_query = "INSERT INTO results (json) VALUES (%s)"
cursor.execute(sql_query, (json_result,))
``````python
# 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()

# Show sample data
print("Sample data (first 5 rows):")
financial_statements.head()

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

```python # 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”) print()

Show latest quarter metrics

latest_quarter = quarterly_metrics[“quarters”][-1] print(f”Latest Quarter ({latest_quarter[‘quarter’]}):“) for key, value in latest_quarter.items(): if key !=”quarter” and isinstance(value, int | float): if “revenue” in key.lower() or “cost” in key.lower(): print(f” {key.replace(‘’, ’ ’).title()}: ${value:,.0f}“) elif”percent” in key.lower() or “margin” in key.lower() or “rate” in key.lower(): print(f” {key.replace(’’, ’ ‘).title()}: {value:.1f}%“) else: print(f” {key.replace(’_‘,’ ’).title()}: {value:,.0f}“) ```### 辅助函数

让我们为这个笔记本定义一些辅助函数：```python 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”) ```## 2. 用例 1：创建财务仪表板 {#financial-dashboard}

现在我们已经加载数据并定义了辅助函数，让我们深入我们的第一个实际用例：创建全面的财务仪表板。我们将从生成包含公式、格式化和图表的多工作表Excel工作簿开始。

2.1 Excel 财务模型

我们将创建一个包含以下内容的财务仪表板： - 利润与损失表，包括年度比较 - 资产负债表分析 - 现金流跟踪 - 带有可视化的关键绩效指标仪表板

这展示了Claude的技能如何处理通常需要数小时手动工作的复杂Excel生成任务。```python # Create Financial Dashboard Excel print(“Creating financial dashboard Excel file…”) print(“This creates a 2-sheet dashboard optimized for the Skills API.”) print(“⏱️ Generation time: 1-2 minutes”)

Prepare the financial data

fs_data = financial_statements.to_dict(“records”) quarters_2024 = [“Q1_2024”, “Q2_2024”, “Q3_2024”, “Q4_2024”]

Extract key financial metrics

revenue_by_quarter = { “Q1 2024”: financial_statements[financial_statements[“Category”] == “Revenue”][ “Q1_2024” ].values[0], “Q2 2024”: financial_statements[financial_statements[“Category”] == “Revenue”][ “Q2_2024” ].values[0], “Q3 2024”: financial_statements[financial_statements[“Category”] == “Revenue”][ “Q3_2024” ].values[0], “Q4 2024”: financial_statements[financial_statements[“Category”] == “Revenue”][ “Q4_2024” ].values[0], }

financial_dashboard_prompt = f”“” Create a financial dashboard Excel workbook with 2 sheets:

Sheet 1 - “P&L Summary”: Create a Profit & Loss summary table for 2024 quarters with these rows: - Revenue: {“,”.join([f”Q{i + 1}: ${v / 1000000:.1f}M” for i, v in enumerate(revenue_by_quarter.values())])} - Gross Profit: Use values from the data - Operating Income: Use values from the data - Net Income: Use values from the data - Add a Total column with SUM formulas - Add a row showing profit margins (Net Income / Revenue) - Apply currency formatting and bold headers - Add a simple bar chart showing quarterly revenue

Sheet 2 - “Key Metrics”: Create a metrics dashboard with: - Total Revenue 2024: SUM of all quarters - Average Quarterly Revenue: AVERAGE formula - Q4 vs Q1 Growth: Percentage increase - Best Quarter: MAX formula to identify - Operating Margin Q4: Calculate from data - Year-over-year growth vs 2023

Apply professional formatting with borders, bold headers, and currency formats. ““”

Create the Excel financial dashboard

excel_response, excel_results = create_skills_message( client, financial_dashboard_prompt, [{“type”: “anthropic”, “skill_id”: “xlsx”, “version”: “latest”}], prefix=“financial_dashboard_”, )

print(“” + “=” * 60) print_download_summary(excel_results)

if len(excel_results) > 0 and excel_results[0][“success”]: print(“✅ Financial dashboard Excel created successfully!”) ```### 💡 Excel生成最佳实践

根据我们的测试，以下是使用Skills创建Excel文件的优化方法：

推荐方法： - 每个工作簿2-3个工作表可靠且生成速度快 - 每个工作表专注于特定目的（例如，损益表、指标、图表） - 逐步增加复杂性 - 从简单开始，然后增强

对于复杂仪表板： 1. 创建多个专注的文件而不是一个复杂的文件 - 例如：financial_pnl.xlsx、balance_sheet.xlsx、kpi_dashboard.xlsx 2. 使用管道模式按顺序创建和增强文件 3. 如有需要，使用pandas或openpyxl编程组合并文件

性能提示： - 简单的2个工作表仪表板：约1-2分钟 - PowerPoint和PDF生成：对于复杂内容非常可靠 - Token使用：结构化数据（JSON/CSV）比散文更有效

2.2 高管PowerPoint

现在我们的财务数据已经组织在Excel中，让我们创建一个高管演示文稿，总结关键见解。这展示了Skills如何生成带有图表、格式化文本和多个幻灯片的专业PowerPoint演示文稿——非常适合董事会会议或投资者更新。

演示文稿将包括： - 2024年第四季度业绩亮点 - 与去年相比的财务指标 - 盈利趋势的可视化 - 关键结论和展望```python print(“Creating executive presentation from financial metrics…”) print(“⏱️ Generation time: 1-2 minutes”)

Calculate some key metrics for the presentation

q4_2024_revenue = 14500000 q4_2023_revenue = 12300000 yoy_growth = (q4_2024_revenue - q4_2023_revenue) / q4_2023_revenue * 100

q4_2024_net_income = 1878750 q4_2023_net_income = 1209000 net_income_growth = (q4_2024_net_income - q4_2023_net_income) / q4_2023_net_income * 100

executive_ppt_prompt = f”“” Create a 4-slide executive presentation for Q4 2024 financial results:

Slide 1 - Title: - Title: “Q4 2024 Financial Results” - Subtitle: “Executive Summary - Acme Corporation” - Date: January 2025

Slide 2 - Financial Highlights: - Title: “Q4 2024 Performance Highlights” - Create a two-column layout: Left side - Key Metrics: • Revenue: $14.5M (+{yoy_growth:.1f}% YoY) • Net Income: $1.88M (+{net_income_growth:.1f}% YoY) • Operating Margin: 17.9% (up 2.9pp) • Operating Cash Flow: $2.85M

Right side - Column chart showing quarterly revenue: Q1 2024: $12.5M Q2 2024: $13.2M Q3 2024: $13.8M Q4 2024: $14.5M

Slide 3 - Profitability Trends: - Title: “Margin Expansion & Profitability” - Add a line chart showing net margin % by quarter: Q1 2024: 11.4% Q2 2024: 11.8% Q3 2024: 12.4% Q4 2024: 13.0% - Add bullet points below: • Consistent margin expansion throughout 2024 • Operating leverage driving profitability • Cost optimization initiatives delivering results

Slide 4 - Key Takeaways: - Title: “Key Takeaways & Outlook” - Bullet points: ✓ Record Q4 revenue of $14.5M ✓ 17.9% YoY revenue growth ✓ 55% increase in net income YoY ✓ Strong cash generation: $2.85M operating cash flow ✓ Well-positioned for continued growth in 2025

Use professional corporate design: - Dark blue (#003366) for headers - Clean, modern layout - Data-driven visualizations ““”

Create the executive presentation

ppt_response, ppt_results = create_skills_message( client, executive_ppt_prompt, [{“type”: “anthropic”, “skill_id”: “pptx”, “version”: “latest”}], prefix=“executive_summary_”, )

print(“” + “=” * 60) print_download_summary(ppt_results)

if len(ppt_results) > 0 and ppt_results[0][“success”]: print(“✅ Executive presentation created successfully!”) ```## 3. 用例 2：投资组合分析工作流程 {#portfolio-analysis}

现在，让我们将关注点从公司财务转向投资组合分析。在本节中，我们将展示如何使用之前加载的投资组合数据创建全面的投资组合分析和投资委员会演示。

此工作流程展示了： - Excel中的详细投资组合绩效分析 - 风险指标和行业配置可视化 - 专业投资委员会演示 - 数据驱动的再平衡建议

我们将首先创建一个包含投资组合分析的Excel工作簿，然后生成一个总结我们发现的投资委员会演示。

首先，让我们创建一个全面的投资组合分析Excel工作簿

在创建投资委员会演示之前，我们需要详细分析我们的投资组合数据。此Excel工作簿将成为我们投资建议的基础。

html ::: callout-notice 请注意，以下代码示例仅供参考，具体实现可能需要根据实际情况进行调整。 :::python # 假设我们有一个名为portfolio的数据结构，其中包含投资组合信息 portfolio = { ‘stocks’: [‘AAPL’, ‘GOOGL’, ‘MSFT’], ‘bonds’: [‘TSLA’, ‘AMZN’], ‘cash’: 100000 }

分析投资组合

def analyze_portfolio(portfolio): # … 分析逻辑 … pass

调用函数

analyze_portfolio(portfolio) ``````python print(“Creating portfolio analysis Excel workbook…”) print(“This creates a focused 2-sheet portfolio analysis optimized for the Skills API.”) print(“⏱️ Generation time: 1-2 minutes”)

Prepare portfolio data for the prompt

top_holdings = portfolio_df.nlargest(5, “market_value”) sector_allocation = portfolio_data[“sector_allocation”]

portfolio_excel_prompt = f”“” Create a portfolio analysis Excel workbook with 2 sheets:

Sheet 1 - “Portfolio Overview”: Create a comprehensive holdings and performance table:

Section 1 - Holdings (top of sheet): {portfolio_df[[“ticker”, “name”, “shares”, “current_price”, “market_value”, “unrealized_gain”, “allocation_percent”]].head(10).to_string()}

Section 2 - Portfolio Summary: - Total portfolio value: ${portfolio_data[“total_value”]:,.2f} - Total unrealized gain: ${portfolio_df[“unrealized_gain”].sum():,.2f} - Total Return: {portfolio_data[“performance_metrics”][“total_return_percent”]:.1f}% - YTD Return: {portfolio_data[“performance_metrics”][“year_to_date_return”]:.1f}% - Sharpe Ratio: {portfolio_data[“performance_metrics”][“sharpe_ratio”]:.2f} - Portfolio Beta: {portfolio_data[“performance_metrics”][“beta”]:.2f}

Apply conditional formatting: green for gains, red for losses. Add a bar chart showing top 5 holdings by value.

Sheet 2 - “Sector Analysis & Risk”: Create sector allocation and risk metrics:

Section 1 - Sector Allocation: {json.dumps(sector_allocation, indent=2)} Include a pie chart of sector allocation.

Section 2 - Key Risk Metrics: - Portfolio Beta: {portfolio_data[“performance_metrics”][“beta”]:.2f} - Standard Deviation: {portfolio_data[“performance_metrics”][“standard_deviation”]:.1f}% - Value at Risk (95%): $62,500 - Maximum Drawdown: -12.3% - Sharpe Ratio: {portfolio_data[“performance_metrics”][“sharpe_ratio”]:.2f}

Section 3 - Rebalancing Recommendations: - Reduce Technology from 20% to 18% - Increase Healthcare from 8.7% to 10% - Maintain current diversification

Apply professional formatting with clear sections and headers. ““”

Create portfolio analysis Excel

portfolio_response, portfolio_results = create_skills_message( client, portfolio_excel_prompt, [{“type”: “anthropic”, “skill_id”: “xlsx”, “version”: “latest”}], prefix=“portfolio_analysis_”, )

print(“” + “=” * 60) print_download_summary(portfolio_results)

if len(portfolio_results) > 0 and portfolio_results[0][“success”]: print(“✅ Portfolio analysis Excel created successfully!”) ```### 3.2 投资委员会演示文稿 {#investment-deck}

在完成详细的投资组合分析后，现在让我们为投资委员会创建一份专业的演示文稿。这份演示文稿将把Excel分析中的关键见解提炼成简洁、直观的格式，适合决策者使用。

演示文稿将涵盖： - 投资组合绩效摘要及关键指标 - 资产配置和多元化分析 - 风险指标和风险调整回报 - 重新平衡的战略建议```python print(“Creating investment committee presentation…”) print(“⏱️ Generation time: 1-2 minutes”)

investment_deck_prompt = f”“” Create a 5-slide investment committee presentation:

Slide 1 - Title: - Title: “Portfolio Review - Q4 2024” - Subtitle: “{portfolio_data[”portfolio_name”]}” - Date: January 2025 - Portfolio Value: ${portfolio_data[“total_value”]:,.0f}

Slide 2 - Portfolio Overview: - Title: “Portfolio Performance Summary” - Two-column layout:

Left - Key Metrics: • Total Value: ${portfolio_data[“total_value”]:,.0f} • YTD Return: +{portfolio_data[“performance_metrics”][“year_to_date_return”]:.1f}% • Total Return: ${portfolio_data[“performance_metrics”][“total_return”]:,.0f} • Sharpe Ratio: {portfolio_data[“performance_metrics”][“sharpe_ratio”]:.2f}

Right - Bar chart of top 5 holdings by value: {“,”.join([f”{h[‘ticker’]}: ${h[‘market_value’]:,.0f}” for h in top_holdings.to_dict(“records”)])}

Slide 3 - Sector Allocation: - Title: “Asset Allocation & Diversification” - Pie chart showing: Technology: {sector_allocation[“Technology”]:.1f}% Financials: {sector_allocation[“Financials”]:.1f}% Healthcare: {sector_allocation[“Healthcare”]:.1f}% Consumer: {sector_allocation[“Consumer Discretionary”] + sector_allocation[“Consumer Staples”]:.1f}% Fixed Income: {sector_allocation[“Bonds”]:.1f}% Cash: {sector_allocation[“Cash”]:.1f}%

Slide 4 - Risk Analysis: - Title: “Risk Metrics & Analysis” - Content: Risk Indicators: • Portfolio Beta: {portfolio_data[“performance_metrics”][“beta”]:.2f} (lower market risk) • Standard Deviation: {portfolio_data[“performance_metrics”][“standard_deviation”]:.1f}% • Maximum Drawdown: -12.3% • Value at Risk (95%): $62,500

Risk-Adjusted Performance: • Sharpe Ratio: {portfolio_data[“performance_metrics”][“sharpe_ratio”]:.2f} (excellent) • Alpha Generation: +2.3% vs benchmark

Slide 5 - Recommendations: - Title: “Strategic Recommendations” - Bullet points: ✓ Maintain current allocation - well diversified ✓ Consider profit-taking in Technology (20% → 18%) ✓ Increase Healthcare allocation (8.7% → 10%) ✓ Monitor bond duration given rate environment ✓ Rebalance quarterly to maintain targets

Use professional investment presentation design. ““”

Create investment committee deck

investment_response, investment_results = create_skills_message( client, investment_deck_prompt, [{“type”: “anthropic”, “skill_id”: “pptx”, “version”: “latest”}], prefix=“investment_committee_”, )

print(“” + “=” * 60) print_download_summary(investment_results) print(“✅ Investment committee presentation created successfully!”) ```## 4. 用例 3：自动化报告流程 {#reporting-pipeline}

到目前为止，我们已经为特定目的创建了单个文档。现在让我们展示将多个技能链在一起在自动化工作流程中的强大功能。这种管道模式对于需要从同一数据源生成多个相关文档的生产系统至关重要。

在这个例子中，我们将创建一个完整的报告套件，包括： 1. 在 Excel 中分析数据，包括计算和图表 2. 在 PowerPoint 演示文稿中总结洞察 3. 在正式的 PDF 报告中记录流程

这展示了技能如何协同工作，创建一个传统的需要多个工具和手动协调的综合报告解决方案。

管道方法的关键优势： - 所有文档中的数据一致 - 总生成时间减少 - Token 使用优化 - 可扩展到多种报告类型

⏱️ 预计总时间： 完整管道需要 2-3 分钟```python print(“🔄 Starting Automated Reporting Pipeline”) print(“=” * 60) print(“This will create a complete reporting suite:”) print(“1. Excel analysis → 2. PowerPoint summary → 3. PDF documentation”) print(“⏱️ Total pipeline time: 2-3 minutes”)

Track token usage across the pipeline

pipeline_tokens = {“input”: 0, “output”: 0}

Step 1: Create Excel Analysis

print(“Step 1/3: Creating Excel analysis with quarterly metrics…”)

excel_pipeline_prompt = f”“” Create a quarterly business metrics Excel file:

Sheet 1 - “Quarterly KPIs”: Create a table with these quarterly metrics for 2024: { json.dumps( [ { k: v for k, v in q.items() if k in [“quarter”, “revenue”, “gross_margin”, “customer_count”, “churn_rate”] } for q in quarterly_metrics[“quarters”] ], indent=2, ) }

Add: - Quarter-over-quarter growth calculations - Average and total rows - Conditional formatting for trends - Line chart showing revenue trend - Column chart showing customer count

Sheet 2 - “YoY Comparison”: Compare Q4 2024 vs Q4 2023 for all metrics. Calculate percentage changes and highlight improvements.

Professional formatting with headers and borders. ““”

excel_response, excel_results = create_skills_message( client, excel_pipeline_prompt, [{“type”: “anthropic”, “skill_id”: “xlsx”, “version”: “latest”}], prefix=“pipeline_1_metrics_”, show_token_usage=False, )

pipeline_tokens[“input”] += excel_response.usage.input_tokens pipeline_tokens[“output”] += excel_response.usage.output_tokens print( f”✓ Excel created - Tokens: {excel_response.usage.input_tokens} in, {excel_response.usage.output_tokens} out” )

Step 2: Create PowerPoint Summary

print(“/3: Creating PowerPoint summary from metrics…”)

ppt_pipeline_prompt = ““” Create a 3-slide quarterly metrics summary presentation:

Slide 1: - Title: “Q4 2024 Metrics Summary” - Subtitle: “Automated Reporting Pipeline Demo”

Slide 2: - Title: “Key Performance Indicators” - Show Q4 2024 metrics: • Revenue: $3.2M (+15% QoQ) • Customers: 850 (+8.9% QoQ) • Gross Margin: 72% • Churn Rate: 2.8% (improved from 3.5%) - Add a simple bar chart comparing Q3 vs Q4 revenue

Slide 3: - Title: “Quarterly Trend Analysis” - Line chart showing revenue growth Q1-Q4 - Key insight bullets: • Consistent QoQ growth • Customer acquisition accelerating • Churn reduction successful

Clean, data-focused design. ““”

ppt_response, ppt_results = create_skills_message( client, ppt_pipeline_prompt, [{“type”: “anthropic”, “skill_id”: “pptx”, “version”: “latest”}], prefix=“pipeline_2_summary_”, show_token_usage=False, )

pipeline_tokens[“input”] += ppt_response.usage.input_tokens pipeline_tokens[“output”] += ppt_response.usage.output_tokens print( f”✓ PowerPoint created - Tokens: {ppt_response.usage.input_tokens} in, {ppt_response.usage.output_tokens} out” )

Step 3: Create PDF Documentation

print(“/3: Creating PDF documentation…”)

pdf_pipeline_prompt = ““” Create a PDF document summarizing the quarterly reporting pipeline:

AUTOMATED REPORTING PIPELINE Q4 2024 Results Documentation

EXECUTIVE SUMMARY This document summarizes the Q4 2024 business metrics generated through our automated reporting pipeline.

KEY METRICS - Revenue: $3.2M (15% QoQ growth) - Customer Base: 850 active customers - Gross Margin: 72% - Churn Rate: 2.8% (improved from 3.5%)

PIPELINE COMPONENTS 1. Data Processing: Quarterly metrics analyzed in Excel 2. Visualization: Key insights presented in PowerPoint 3. Documentation: Formal report generated in PDF

AUTOMATION BENEFITS • Reduced reporting time by 90% • Consistent format and quality • Eliminated manual errors • Scalable to multiple reports

NEXT STEPS - Expand pipeline to include predictive analytics - Add automated email distribution - Implement real-time data feeds

Generated: January 2025 Pipeline Version: 1.0

Format as a professional technical document. ““”

pdf_response, pdf_results = create_skills_message( client, pdf_pipeline_prompt, [{“type”: “anthropic”, “skill_id”: “pdf”, “version”: “latest”}], prefix=“pipeline_3_documentation_”, show_token_usage=False, )

pipeline_tokens[“input”] += pdf_response.usage.input_tokens pipeline_tokens[“output”] += pdf_response.usage.output_tokens print( f”✓ PDF created - Tokens: {pdf_response.usage.input_tokens} in, {pdf_response.usage.output_tokens} out” )

Pipeline Summary

print(“” + “=” * 60) print(“🎯 PIPELINE COMPLETE!”) print(“=” * 60)

print(“📊 Pipeline Token Usage Summary:”) print(f” Total Input Tokens: {pipeline_tokens[‘input’]:,}“) print(f” Total Output Tokens: {pipeline_tokens[‘output’]:,}“) print(f” Total Tokens: {pipeline_tokens[‘input’] + pipeline_tokens[‘output’]:,}“) print(f” Average per document: {(pipeline_tokens[‘input’] + pipeline_tokens[‘output’]) // 3:,}“)

print(“📁 Generated Files:”) all_results = excel_results + ppt_results + pdf_results for i, result in enumerate(all_results, 1): if result[“success”]: print(f” {i}. {os.path.basename(result[‘output_path’])} ({result[‘size’] / 1024:.1f} KB)“)

print(“✅ Automated reporting pipeline executed successfully!”) print(” All three documents created and linked in workflow.”) ```## 摘要 & 下一步计划

我们所取得的成就

在本笔记本中，您学习了如何：

✅ 创建财务仪表板 - 构建带有公式和图表的多工作表Excel模型 - 生成执行PowerPoint演示文稿 - 创建专业PDF报告

✅ 投资组合分析 - 开发投资组合分析工作簿 - 创建投资委员会演示文稿 - 实施风险指标和再平衡工具

✅ 自动化流程 - 连接多个文档格式 - 优化token使用 - 构建生产就绪模式

关键要点

技能显著简化了财务文档的创建 - 手动操作需要数小时的工作现在只需几分钟
token效率极佳 - Skills使用的token比手动指令少约90%
质量达到专业级别 - 文档可以立即在商业环境中使用
自动化简单易行 - 流程模式使复杂的流程变得简单

继续学习

📚 下一步：笔记本3 - 自定义技能开发 - 构建您自己的专业财务技能 - 创建公司特定的模板 - 实施高级自动化

尝试以下实验

修改财务仪表板以包含您自己的指标
创建一个自定义投资组合包含不同的资产类别
构建一个流程以满足您的特定报告需求
实验复杂性以了解生成时间
跟踪token使用跨不同文档类型

资源

Tip

中文读者提示：本章节的代码和输出为英文原文。如需理解具体实现细节，可参考上方中文导读和代码注释。如有疑问，欢迎在 GitHub Issues 讨论。

Claude Skills for Financial Applications

目录

先决条件

1. 设置与数据加载

Show latest quarter metrics

2.1 Excel 财务模型

Prepare the financial data

Extract key financial metrics

Create the Excel financial dashboard

2.2 高管PowerPoint

Calculate some key metrics for the presentation

Create the executive presentation

首先，让我们创建一个全面的投资组合分析Excel工作簿

分析投资组合

调用函数

Prepare portfolio data for the prompt

Create portfolio analysis Excel

Create investment committee deck

Track token usage across the pipeline

Step 1: Create Excel Analysis

Step 2: Create PowerPoint Summary

Step 3: Create PDF Documentation

Pipeline Summary

我们所取得的成就

关键要点

继续学习

尝试以下实验

资源