Summary

Enable users to use LLM agents (via Claude Code) to analyze and work with their personal financial data stored in fafycat.

Use Cases

• Generate custom reports and insights by describing what you want ("show me my top spending categories", "analyze my spending trends")
• Automate financial analysis and data manipulation tasks
• Build dynamic dashboards or queries through natural language

Implementation Options

Two potential approaches to evaluate:

1. SKILL.md — Create a custom skill that exposes fafycat data to Claude Code. Lighter weight, integrates directly with Claude Code harness.
2. MCP Server — Build a Model Context Protocol server to expose fafycat as a data source. More standardized, but requires additional infrastructure.

Next Steps

• Research task: Evaluate whether MCP is worth the implementation effort vs. a simpler SKILL.md approach
  • Document pros/cons of each approach
  • Prototype both options or evaluate existing patterns
  • Recommend a path forward

Acceptance Criteria

• Clear recommendation on MCP vs SKILL.md with documented reasoning
• Design document or RFC outlining the chosen approach
• Prototype or POC demonstrating feasibility