ALWAYS follow this data source hierarchy:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncomps-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches comps-analysis from anthropics/financial-services-plugins and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate comps-analysis. Access via /comps-analysis in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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ALWAYS follow this data source hierarchy:
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 Web search results can be outdated, inaccurate, or unreliable for financial analysis.
This skill teaches Claude 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 company analysis is provided in examples/comps_example.xlsx. When using this or other example files in this skill directory, use them intelligently:
DO use examples for:
DO NOT use examples for:
ALWAYS ask yourself first:
Adapt based on specifics:
Core principle: Use template principles (clear structure, statistical rigor, transparent formulas) but vary execution based on context. The goal is institutional-quality analysis, not institutional-looking templates.
User-provided examples and explicit preferences always take precedence over defaults.
"Build the right structure first, then let the data tell the story."
Start with headers that force strategic thinking about what matters, input clean data, build transparent formulas, and let statistics emerge automatically. A good comp should be immediately readable by someone who didn't build it.
Environment — Office JS vs Python:
Excel.run(async (context) => {...})). Write formulas via range.formulas = [["=E7/C7"]], not range.values. No separate recalc step — Excel handles it natively. Use range.format.* for colors/fonts.cell.value = "=E7/C7" (formula string)..merge() then set .values on the merged range (throws InvalidArgument — range still reports its pre-merge dimensions). Instead write the value to the top-left cell alone, then merge + format the full range:
ws.getRange("A1").values = [["TECHNOLOGY — COMPARABLE COMPANY ANALYSIS"]];
const hdr = ws.getRange("A1:H1");
hdr.merge();
hdr.format.fill.color = "#1F4E79";
hdr.format.font.color = "#FFFFFF";
hdr.format.font.bold = true;
Formulas, not hardcodes:
cell.value = "=E7/C7" (formula string), NOT cell.value = 0.687 (computed result)Verify step-by-step with the user:
Row 1: [ANALYSIS TITLE] - COMPARABLE COMPANY ANALYSIS
Row 2: [List of Companies with Tickers] • [Company 1 (TICK1)] • [Company 2 (TICK2)] • [Company 3 (TICK3)]
Row 3: As of [Period] | All figures in [USD Millions/Billions] except per-share amounts and ratios
Why this matters: Establishes context immediately. Anyone opening this file knows what they're looking at, when it was created, and how to interpret the numbers.
IMPORTANT: These are suggested defaults only. Always prioritize:
Suggested Font & Typography:
Default Color & Shading — Professional Blue/Grey Palette (minimal is better):
#1F4E79 or #17365D navy)#D9E1F2 or similar pale blue)#F2F2F2)Suggested Formatting Conventions:
Note: If the user provides a template file or specifies different formatting, use that instead.
// Core ratios - these are always calculated
Gross Margin (F7): =E7/C7
EBITDA Margin (H7): =G7/C7
// Optional ratios - include if relevant
FCF Margin: =[FCF]/[Revenue]
Net Margin: =[Net Income]/[Revenue]
Rule of 40: =[Growth %]+[FCF Margin %]
Golden Rule: Every ratio should be [Something] / [Revenue] or [Something] / [Something from this sheet]. Keep it simple.
CRITICAL: Add statistics formulas for all comparable metrics (ratios, margins, growth rates, multiples).
[Leave one blank row for visual separation]
- Maximum: =MAX(B7:B9)
- 75th Percentile: =QUARTILE(B7:B9,3)
- Median: =MEDIAN(B7:B9)
- 25th Percentile: =QUARTILE(B7:B9,1)
- Minimum: =MIN(B7:B9)
Columns that NEED statistics (comparable metrics):
Columns that DON'T need statistics (size metrics):
Note: Add one blank row between company data and statistics rows for visual separation. Do NOT add a "SECTOR STATISTICS" or "VALUATION STATISTICS" header row.
Why quartiles matter: They show distribution, not just average. A 75th percentile multiple tells you what "premium" companies trade at.
Key Principle: Include 3-5 core multiples that matter for your industry. Don't include every possible metric just because you can.
// Core multiples - always include these
EV/Revenue: =[Enterprise Value]/[LTM Revenue]
EV/EBITDA: =[Enterprise Value]/[LTM EBITDA]
P/E Ratio: =[Market Cap]/[Net Income]
// Optional multiples - include if data available
FCF Yield: =[LTM FCF]/[Market Cap]
PEG Ratio: =[P/E]/[Growth Rate %]
CRITICAL: Valuation multiples MUST reference the operating metrics section. Never input the same raw data twice. If revenue is in C7, then EV/Revenue formula should reference C7.
Same structure as operating section: Max, 75th, Median, 25th, Min for every metric. Add one blank row for visual separation between company data and statistics. Do NOT add a "VALUATION STATISTICS" header row.
Data Sources & Quality:
Key Definitions:
Valuation Methodology:
Analysis Framework:
"Which company is undervalued?" → Focus on: EV/Revenue, EV/EBITDA, P/E, Market Cap → Skip: Operational details, growth metrics
"Which company is most efficient?" → Focus on: Gross Margin, EBITDA Margin, FCF Margin, Asset Turnover → Skip: Size metrics, absolute dollar amounts
"Which company is growing fastest?" → Focus on: Revenue Growth %, EBITDA CAGR, User/Customer Growth → Skip: Margin metrics, leverage ratios
"Which is the best cash generator?" → Focus on: FCF, FCF Margin, FCF Conversion, CapEx intensity → Skip: EBITDA, P/E ratios
Software/SaaS: Must have: Revenue Growth, Gross Margin, Rule of 40 Optional: ARR, Net Dollar Retention, CAC Payback Skip: Asset Turnover, Inventory metrics
Manufacturing/Industrials: Must have: EBITDA Margin, Asset Turnover, CapEx/Revenue Optional: ROA, Inventory Turns, Backlog Skip: Rule of 40, SaaS metrics
Financial Services: Must have: ROE, ROA, Efficiency Ratio, P/E Optional: Net Interest Margin, Loan Loss Reserves Skip: Gross Margin, EBITDA (not meaningful for banks)
Retail/E-commerce: Must have: Revenue Growth, Gross Margin, Inventory Turnover Optional: Same-Store Sales, Customer Acquisition Cost Skip: Heavy R&D or CapEx metrics
5 operating metrics - Revenue, Growth, 2-3 margins/efficiency metrics 5 valuation metrics - Market Cap, EV, 3 multiples = 10 total columns - Enough to tell the story, not so many you lose the thread
If you have more than 15 metrics, you're probably including noise. Edit ruthlessly.
Input all raw data first - Complete the blue text before writing formulas
Add cell comments to ALL hard-coded inputs - Right-click cell → Insert Comment → Document source OR assumption
For sourced data, cite exactly where it came from:
For assumptions, explain the reasoning:
Why this matters: Enables audit trails, data verification, assumption transparency, and future updates
Build formulas row by row - Test each calculation before moving on
Use absolute references for headers - $C$6 locks the header row
Format consistently - Percentages as percentages, not decimals
Add conditional formatting - Highlight outliers automatically
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend comps-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: comps-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
comps-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in comps-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
comps-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for comps-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: comps-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: comps-analysis is focused, and the summary matches what you get after install.
comps-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend comps-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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