comps-analysislisted

## Environment This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk. Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables. Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`. # Comparable Company Analysis ## ⚠️ CRITICAL: Data Source Priority (READ FIRST) **ALWAYS follow this data source hierarchy:** 1. **FIRST: Check for MCP data sources** - If S&P Kensho MCP, FactSet MCP, or Daloopa MCP are available, use them exclusively for financial and trading information 2. **DO NOT use web search** if the above MCP data sources are available 3. **ONLY if MCPs are unavailable:** Then use Bloomberg Terminal, SEC EDGAR filings, or other institutional sources 4. **NEVER use web search as a primary data source** - it lacks the accuracy, audit trails, and reliability required for institutional-grade analysis **Why this matters:** MCP sources provide verified, institutional-grade data with proper citations. Web search results can be outdated, inaccurate, or unreliable for financial analysis. --- ## Overview This skill teaches the agent to build institutional-grade comparable company analyses that combine operating metrics, valuation multiples, and statistical benchmarking. The output is a structured Excel/spreadsheet that enables informed investment decisions through peer comparison. **Reference Material & Contextualization:** An example comparable com