agentleFS
d-wweid-wwei/openclaw-financial-services/skills/fsi-fa-cmd-comps/SKILL.md

fsi-fa-cmd-comps

Skill16 starsChanged 9 months agoApache-2.0View on GitHub

Build a comparable company analysis with trading multiples — usage: /comps [company name or ticker]

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If a company name or ticker is provided, use it.

    line 16
  2. Very specific:

    Use skill: "comps-analysis" to build the analysis:

    line 21

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Comparable Company Analysis Command

Build an institutional-grade comparable company analysis with operating metrics, valuation multiples, and statistical benchmarking.

Workflow

Step 1: Gather Company Information

If a company name or ticker is provided, use it. Otherwise ask:

  • "What company would you like to analyze?"

Step 2: Load Comps Analysis Skill

Use skill: "comps-analysis" to build the analysis:

  1. Clarify the analysis purpose:
    • "What's the key question?" (valuation, efficiency, growth comparison)
    • "Who is the audience?" (IC, board, quick reference)
    • "Do you have a preferred format or template?"
  2. Identify peer group (4-6 comparable companies):
    • Similar business model
    • Similar scale/market cap range
    • Same industry/sector
    • Geographic comparability
  3. Gather data (prioritize MCP sources if available):
    • Operating metrics: Revenue, Growth, Gross Margin, EBITDA, EBITDA Margin
    • Valuation: Market Cap, Enterprise Value, EV/Revenue, EV/EBITDA, P/E
    • Additional metrics based on industry (Rule of 40 for SaaS, etc.)
  4. Build the analysis:
    • Operating Statistics section with company data + statistics (Max, 75th, Median, 25th, Min)
    • Valuation Multiples section with same statistical summary
    • Notes & Methodology documentation

Step 3: Create Excel Output

Generate Excel file with:

  • Header block (analysis title, companies, date, units)
  • Operating Statistics & Financial Metrics section
  • Valuation Multiples section
  • Statistical summary for each metric
  • Notes section documenting sources and methodology

Step 4: Deliver Output

Provide:

  1. Excel file (.xlsx) - the comps analysis
  2. Summary highlighting:
    • Peer group selection rationale
    • Key insights (who trades at premium/discount)
    • Median multiples for reference

Output Format Reference

┌─────────────────────────────────────────────────────────────────┐
│ [SECTOR] - COMPARABLE COMPANY ANALYSIS                          │
│ [Company 1] • [Company 2] • [Company 3] • [Company 4]          │
│ As of [Date] | All figures in USD Millions                      │
├─────────────────────────────────────────────────────────────────┤
│ OPERATING STATISTICS & FINANCIAL METRICS                        │
├──────────┬─────────┬─────────┬──────────┬─────────┬────────────┤
│ Company  │ Revenue │ Growth  │ Gross    │ EBITDA  │ EBITDA     │
│          │ (LTM)   │ (YoY)   │ Margin   │ (LTM)   │ Margin     │
├──────────┼─────────┼─────────┼──────────┼─────────┼────────────┤
│ [Data rows for each company]                                    │
│                                                                 │
│ Maximum  │ =MAX    │ =MAX    │ =MAX     │ =MAX    │ =MAX       │
│ 75th %   │ =QUART  │ =QUART  │ =QUART   │ =QUART  │ =QUART     │
│ Median   │ =MEDIAN │ =MEDIAN │ =MEDIAN  │ =MEDIAN │ =MEDIAN    │
│ 25th %   │ =QUART  │ =QUART  │ =QUART   │ =QUART  │ =QUART     │
│ Minimum  │ =MIN    │ =MIN    │ =MIN     │ =MIN    │ =MIN       │
├─────────────────────────────────────────────────────────────────┤
│ VALUATION MULTIPLES                                             │
├──────────┬──────────┬──────────┬──────────┬───────────┬────────┤
│ Company  │ Mkt Cap  │ EV       │ EV/Rev   │ EV/EBITDA │ P/E    │
├──────────┼──────────┼──────────┼──────────┼───────────┼────────┤
│ [Data rows + statistics]                                        │
└─────────────────────────────────────────────────────────────────┘

Industry-Specific Metrics

IndustryAdditional Metrics
Software/SaaSARR, Net Dollar Retention, Rule of 40
RetailSame-store sales, Inventory Turns
FinancialsROE, ROA, Efficiency Ratio
ManufacturingAsset Turnover, CapEx/Revenue
HealthcareR&D/Revenue, Pipeline Value

Quality Checklist

Before delivery:

  • 4-6 truly comparable companies
  • Consistent time periods (all LTM or all FY)
  • All formulas reference cells (no hardcoded values)
  • Cell comments on all hardcoded inputs with sources
  • Statistics include Max, 75th, Median, 25th, Min
  • Notes section documents sources and methodology
  • Blue = inputs, Black = formulas
  • Sanity checks pass (margins logical, multiples reasonable)

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.