Designing Skill-Driven Financial Analysis Agents with Claude, Python, MCP Connectors, and Automated Deliverables
In this tutorial, we build an advanced workflow around Anthropic’s 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 fin
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This tutorial describes how to build financial analysis agents using Claude, Python, and skill-driven architecture from Anthropic's financial-services repository. The approach involves installing required libraries, cloning the repository, parsing skill definitions from markdown files into a searchable registry, and constructing a reusable agent that injects financial playbooks into Claude while supporting iterative tool use for calculations and file generation. The tutorial demonstrates practical applications including discounted cash flow valuation, sensitivity analysis, comparable-company analysis with Excel output, and investment committee memo drafting. This matters because it shows how to combine AI models with structured financial methodologies and external data connectors to automate complex analytical workflows.
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