Many finance teams struggle to turn raw assumptions into polished analyses because they must stitch together disparate tools, manually run calculations, and format deliverables each time. The Anthropic financial‑services repository shows how a skill‑driven architecture can automate this loop, but adapting it to pure Python can be opaque for newcomers. This post gives a concise, hands‑on recipe that reproduces the repository’s agent‑skill pattern in a single Colab‑friendly script.
First install the core libraries (anthropic, pandas, openpyxl, pyyaml, matplotlib) and clone the repo. A helper walks the plugin folders, counts SKILL.md files and commands, and builds a searchable registry by parsing each skill’s YAML front‑matter. With the registry ready, a lightweight SkillAgent class injects the selected skill’s description and body into the system prompt, then drives a standard tool‑use loop against the Claude Messages API. Two tools are exposed: run_python (which executes code in a persistent namespace and returns stdout) and save_file (which writes text to an ./outputs folder). The agent therefore can perform arithmetic, call pandas, and persist artefacts without leaving the conversation loop.
To see it in action, the tutorial runs three demos. A DCF skill computes enterprise value, equity value, implied share price and outputs a WACC‑vs‑terminal‑growth sensitivity grid that is visualized as a heatmap. A comps skill calculates EV, EV/Revenue, EV/EBITDA and P/E for a synthetic peer set, exports the full table and summary statistics to a formatted Excel workbook, and applies bold headers and auto‑sized columns. Finally an IC‑memo skill drafts a first‑round investment‑committee memo, uses run_python for quick MOIC/IRR math, and saves the markdown file.
All outputs land in ./outputs for inspection. Swap the synthetic data for real feeds via the repository’s MCP connectors, stack multiple skills (e.g., comps + dcf) for a football‑field workflow, or replace the Colab loop with a governed Managed Agents deployment for production. The pattern is reusable across valuation, LBO, merger, earnings, rebalance, tax‑loss‑harvest and KYC playbooks.
#AI #Product #FinTech #DataScience #LLM #Automation