In this tutorial, we build an advanced workflow around Anthropic’s financial-services ：https://github.com/anthropics/financial-services repository and reproduce its skill-driven architecture in pure Python. We begin by installing the required libraries, cloning the repository, and programmatically mapping its agents, vertical plugins, partner integrations, managed-agent cookbooks, and financial analysis skills. We then parse the repository’s SKILL.md files into a searchable registry and construct a reusable SkillAgent that injects selected financial playbooks into the Anthropic Messages API while supporting an iterative tool-use loop for Python calculations and file generation. Using this architecture, we execute a synthetic discounted cash flow valuation, generate a WACC and terminal-growth sensitivity heatmap, perform comparable-company analysis with formatted Excel output, draft a private-equity investment committee memo, and inspect a managed-agent deployment specification without sending a live deployment request.
We install the required Python libraries, clone Anthropic’s financial-services repository, and prepare the Google Colab runtime for execution. We retrieve the Anthropic API key from Colab secrets, environment variables, or a secure interactive prompt. We then initialize the official Anthropic SDK and select the Claude model that powers the financial-analysis workflows.
We inspect the repository structure and identify its agent plugins, vertical plugins, partner integrations, managed-agent cookbooks, and available commands. We locate MCP configuration files and display the external financial data connectors defined in the repository. We then parse each SKILL.md file, extract its YAML metadata and methodology, and register every unique skill for searchable access.
We define tools that allow Claude to execute Python calculations and save generated deliverables inside the Colab environment. We build a persistent Python namespace so numerical models, tables, and intermediate variables remain available across multiple agent turns. We then create the SkillAgent class, inject selected financial playbooks into its system prompt, and manage the Anthropic Messages API tool-use loop.
We provide synthetic operating assumptions and instruct the DCF skill agent to construct a five-year unlevered cash-flow valuation. We use the Python execution tool to calculate enterprise value, equity value, implied share price, and a two-dimensional sensitivity matrix. We then extract the structured sensitivity results and visualize the relationship between WACC, terminal growth, and implied valuation through a heatmap.
We supply a synthetic peer set and calculate enterprise value, EV-to-revenue, EV-to-EBITDA, and price-to-earnings multiples. We convert the agent’s structured JSON response into detailed comparable-company and summary-statistics DataFrames. We then export the analysis to a multi-sheet Excel workbook and apply professional header formatting and automatic column sizing.
We apply the investment-committee memo skill to a hypothetical software buyout and calculate supporting return metrics with Python. We save the resulting memo as a Markdown deliverable and verify that the generated file exists in the output directory. We then inspect a managed-agent cookbook, display the deployment specification, and conclude by reviewing the generated artifacts and possible production extensions.
By completing this tutorial, we implement a practical Colab-based approximation of Anthropic’s financial-services skill and agent framework while preserving the repository’s methodology-driven approach to financial analysis. We combine structured skill discovery, dynamic system-prompt construction, persistent Python execution, API-based tool orchestration, and automated deliverable generation in a single reusable workflow. We also demonstrate how the same agent architecture supports multiple finance use cases, including DCF valuation, trading-comps analysis, sensitivity testing, Excel reporting, and investment committee memo preparation. From here, we extend the system by loading additional skills, combining multiple valuation playbooks, connecting to licensed financial data providers via MCP integrations, and replacing the tutorial sandbox with a governed production runtime in Claude Code, Cowork, or Managed Agents.
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Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.
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