Institutional Comparable Company Analysis
Build auditable peer valuation analyses with spreadsheet formulas, operating metrics, and trading multiples.
The skill prioritizes institutional MCP or SEC/Bloomberg sources, requires source comments, and recommends staged user confirmation, which reduces misuse risk; however, it does not define least-privilege access, sensitive financial-data handling, connector authorization boundaries, data flows, revocation, or rollback, so points are deducted.
The documentation provides a substantial workflow, formula rules, quality checks, and sanity checks; however, there are no key-path tests or executable reproduction evidence, some example layout and statistics requirements are inconsistent, and failure feedback remains thin, so points are deducted.
The audience, use cases, non-fit company types, industry-specific metrics, and decision questions are described; however, semantic trigger conditions, Chinese-language support, and mainland-China network reachability are not specified, while some data access depends on potentially restricted overseas institutional services, so points are deducted.
The skill is well organized with progressive sections and includes environment notes, example guidance, limitations, and quality checklists; the supplied repository also provides Apache-2.0 licensing and official provenance. It lacks skill-level versioning, changelog, explicit maintenance ownership, and an update path, so points are deducted.
The skill targets a directly usable Excel/spreadsheet comparable-company analysis and covers operating metrics, valuation multiples, statistics, and methodology documentation; however, no representative output is statically verifiable, results still require human review, and quality depends on external data access and modeling execution, so the static cap applies.
The methods, formulas, and source-priority rules are auditable, and the repository includes general plugin-validation and secret-scanning workflows; however, there are no skill-specific key-path tests, third-party execution results, or corroborating validation evidence, so only limited credit is awarded.
- Before use, confirm connector authorization, data residency, cost, reachability, and sensitive-data handling for MCP, Bloomberg, or other institutional sources.
- Professionals must verify every raw input, period, unit, adjustment, and valuation assumption; the documented reasonableness ranges are not a substitute for audit or investment judgment.
- Add explicit trigger conditions, abnormal-input handling, failure feedback, version history, and reproducible tests.
What it does & when to use it
This skill guides the creation of comparable company analyses in Excel or spreadsheet format, combining operating metrics, valuation multiples, and statistical benchmarking. It emphasizes defining the peer group and structure first, documenting raw inputs, and calculating derived values with transparent formulas. It is intended for valuation, performance benchmarking, IPO or financing pricing, and investment committee materials. It is best suited to companies with comparable public peers and is a poor fit for companies with no meaningful peers, highly unique models, or no revenue.
It sets up analysis headers, company lists, periods, units, operating-metric sections, valuation-multiple sections, and methodology notes. It specifies formulas for revenue growth, gross margin, EBITDA margin, EV/revenue, EV/EBITDA, P/E, and related metrics, plus maximum, 75th percentile, median, 25th percentile, and minimum statistics. Valuation calculations are instructed to cross-reference the operating metrics section rather than duplicate raw inputs. Hard-coded inputs should include source or assumption comments, while the notes should document periods, definitions, valuation methodology, and quality checks. The skill describes Office JS for Excel and Python/openpyxl for standalone .xlsx generation, with S&P Kensho MCP, FactSet MCP, or Daloopa MCP preferred when available.
- An investment banking analyst compares an acquisition target with public peers for a pitch or M&A analysis.
- An equity researcher benchmarks a company’s growth, margins, and trading multiples against its sector.
- An investment team develops valuation references for an IPO or funding round.
- An investment committee reviews whether a company trades at a premium, discount, or statistical outlier level.
- An industry researcher prepares a sector overview with operating statistics and valuation distributions.
Pros & cons
- Requires formulas for derived values instead of pasted hardcodes, supporting automatic updates.
- Covers operating metrics, valuation multiples, quartile statistics, methodology notes, and sanity checks.
- Emphasizes source documentation, input comments, consistent periods, and auditability.
- Includes metric-selection guidance for SaaS, financial services, manufacturing, retail, and other sectors.
- Not designed for private companies without comparable public peers, highly diversified conglomerates, distressed companies, or pre-revenue startups.
- It requires reliable financial and trading inputs; the source does not provide a dataset or built-in data extraction implementation.
- Although it names MCP, Bloomberg, and SEC sources, access permissions and subscriptions are not handled by the skill.
- The source provides no test suite or platform validation results.
How to install
The README documents repository-level installation: in Cowork, open Settings → Plugins → Add plugin, paste https://github.com/anthropics/financial-services, and choose the relevant plugin. In Claude Code, run claude plugin marketplace add anthropics/financial-services, then claude plugin install financial-analysis@claude-for-financial-services. A standalone installation command for this individual skill is not documented.
How to use
After installation, trigger the skill with /comps. Provide the companies and tickers, analysis period, currency units, key question, audience, and formatting preferences. The workflow then proceeds through structure, raw inputs, operating formulas, valuation multiples, statistics, and quality checks. Confirm sources and periods and review margins, multiples, and formula errors during the build.
Compared to similar skills
The source explicitly prioritizes S&P Kensho MCP, FactSet MCP, and Daloopa MCP for financial and trading information, with Bloomberg Terminal, SEC EDGAR filings, or other institutional sources as fallbacks. It does not provide a feature comparison with other comparable-company-analysis tools or skills.