Comparable Company Valuation Analyst
Build institutional-grade peer valuation analyses with operating metrics, trading multiples, and statistical benchmarks in spreadsheets.
The skill prioritizes institutional data sources, requires source comments and audit trails, and calls for staged user confirmation; the repository also states that outputs require professional review. However, sensitive financial-data handling, permission isolation, external-write impact, rollback, and end-to-end data-flow disclosure are not adequately specified, so points are deducted.
The document gives a fairly detailed workflow, formula rules, sanity checks, and separate Office JS/openpyxl paths. However, no skill-specific tests cover key paths, some statistics and example-layout instructions are inconsistent, and abnormal-input handling and failure feedback are thin; the static score is therefore limited.
The intended audience, scenarios, non-fit company types, industry metric guidance, and decision questions are clearly described. However, semantic trigger conditions, Chinese-language support, and mainland-China reachability are not addressed, while core data access may depend on subscription- and key-gated overseas MCPs or terminals, so points are deducted.
The documentation is well layered and includes installation context, methodology, example structure, quality checks, limitations, Apache-2.0 licensing, and verified official provenance. The skill itself lacks a version, changelog, named maintenance owner, and explicit update path, and dependency setup is not sufficiently operational, so points are deducted.
The skill clearly targets a structured Excel/spreadsheet deliverable covering operating metrics, valuation multiples, statistical benchmarking, and methodology notes. The core value is plausible, but there is no verifiable produced output, connectors and data still require user configuration, and direct usability or advantage over manual alternatives is not demonstrated, so points are deducted.
Formula transparency, source-comment requirements, data-source prioritization, and repository CI/secret scanning provide some auditability. However, there are no skill-specific tests, real execution records, or corroborating output evidence, and static review cannot confirm correctness, so the score is limited.
- Do not assume MCP, Bloomberg, or SEC data availability, accuracy, or subscription access; confirm connectors, permissions, periods, and sources before use.
- The document requests staged user confirmation but does not define standard handling or failure feedback for rejection, missing data, division by zero, negative EBITDA, loss-making companies, or abnormal multiples.
- Investment-analysis outputs require qualified professional review; the skill provides no demonstrated rollback, sensitive-data isolation, or mainland-China reachability guarantee.
What it does & when to use it
This skill guides the creation of comparable company analyses covering operating data, valuation multiples, statistical distributions, and methodology notes. It prioritizes institutional data sources and requires derived metrics to be calculated with spreadsheet formulas rather than hardcoded values. The intended output is a structured, auditable, updateable Excel or standalone .xlsx analysis. It is designed for public-company valuation, peer benchmarking, IPO or financing pricing, and investment committee materials.
It directs the assistant to define the peer group, analytical question, industry metrics, periods, and units before collecting raw inputs such as revenue, growth, gross profit, EBITDA, market capitalization, and enterprise value. It uses Excel or Office JS, or Python/openpyxl for a standalone .xlsx file, to create formulas for margins, valuation multiples, maximums, quartiles, medians, and minimums. It requires source or assumption comments for hardcoded inputs and staged checks of operating metrics, valuation multiples, and statistics. It also structures documentation covering data sources, definitions, valuation methodology, and the analysis framework.
- An investment banking analyst builds a public-company trading comparables table for an M&A or valuation engagement.
- An equity research team compares peer growth, margins, operating efficiency, and valuation levels.
- A financing adviser establishes valuation benchmarks and outlier analysis for an IPO or funding round.
- An investment committee prepares peer analysis requiring transparent formulas, source documentation, and distribution statistics.
- An industry researcher creates a sector overview combining operating performance and valuation comparisons.
Pros & cons
- Covers the workflow from peer selection and metric design through statistical analysis and methodology documentation.
- Explicitly requires formulas for derived values and cross-references valuation calculations to the operating metrics section.
- Prioritizes traceable sources such as MCP providers, Bloomberg, and SEC filings, with source or assumption documentation for hardcoded inputs.
- Includes industry-specific metric guidance, quartile analysis, and multiple quality-control checks.
- The skill does not supply financial data; MCP, Bloomberg, SEC, or other institutional data access may require separate permissions or subscriptions.
- Its output depends on an Excel, Office JS, or Python/openpyxl environment, and the complete runtime setup is not documented.
- No test suite, example execution result, or platform compatibility evidence is provided.
- It is not intended for private companies without public peers, highly diversified conglomerates, distressed or bankrupt companies, pre-revenue startups, or unique business models.
How to install
Install the repository's plugins/vertical-plugins/financial-analysis/ as the financial-analysis plugin. The README says Cowork users can open Settings → Plugins → Add plugin and paste https://github.com/anthropics/financial-services, or upload a directory under plugins/. For Claude Code, run claude plugin marketplace add anthropics/financial-services, then claude plugin install financial-analysis@claude-for-financial-services. The source does not document a separate installation procedure for this individual skill file.
How to use
After installing the financial-analysis plugin, trigger the skill with /comps, or provide a request such as: “Create an LTM comparable company analysis in Excel for these peer companies, including operating metrics, EV/Revenue, EV/EBITDA, P/E, and five statistical measures.” Specify formatting preferences, audience, key question, industry context, peer group, period, units, and available data sources. The skill requires staged confirmation of the structure, raw inputs, operating formulas, and valuation multiples before completing the analysis.