evaluating-investment-opportunities
Structures PE/VC investment evaluation with business model assessment, market analysis, and return potential. Use when evaluating deals, screening investment opportunities, or assessing company fit.
Best use case
evaluating-investment-opportunities is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structures PE/VC investment evaluation with business model assessment, market analysis, and return potential. Use when evaluating deals, screening investment opportunities, or assessing company fit.
Teams using evaluating-investment-opportunities should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/evaluating-investment-opportunities/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How evaluating-investment-opportunities Compares
| Feature / Agent | evaluating-investment-opportunities | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structures PE/VC investment evaluation with business model assessment, market analysis, and return potential. Use when evaluating deals, screening investment opportunities, or assessing company fit.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Evaluating Investment Opportunities ## When To Use - Screening a new deal from an inbound teaser, CIM, or pitch deck - Conducting preliminary diligence before issuing an LOI or term sheet - Comparing multiple opportunities in a pipeline review - Preparing an investment committee memo or deal screening summary - Reassessing a portfolio company for follow-on investment or exit readiness ## Inputs To Gather - **Company materials**: CIM, pitch deck, financial model, or data room access - **Financials**: At minimum 3 years of historical P&L, balance sheet, and cash flow; projections if available - **Deal parameters**: Proposed valuation, investment size, instrument type (equity, convertible, preferred), ownership target - **Market data**: Industry reports, comparable transactions, public comps where relevant - **Management information**: Org chart, founder/CEO background, key person dependencies - **Fund context**: Fund strategy, thesis fit, sector/stage mandate, portfolio overlap risk ## Workflow 1. **Frame the opportunity** - Confirm investment stage (seed, growth, buyout, recap) and applicable evaluation lens - Identify the core investment thesis — what must be true for this to be a strong return - Flag any immediate disqualifiers (sector exclusion, size mismatch, geographic restriction) 2. **Assess the business model** - Revenue model: recurring vs. transactional, unit economics, customer concentration - Gross margin profile and trajectory; path to operating leverage - Customer acquisition cost (CAC), lifetime value (LTV), and LTV/CAC ratio for SaaS/consumer - Capital intensity and working capital dynamics - Defensibility: IP, network effects, switching costs, regulatory moats 3. **Analyze the market** - TAM/SAM/SOM sizing with bottom-up validation where possible - Growth rate and secular tailwinds or headwinds - Competitive landscape: direct competitors, substitutes, and new entrant risk - Regulatory environment and pending policy changes [VERIFY — jurisdiction-specific] 4. **Evaluate financial performance and projections** - Revenue CAGR, gross margin trend, EBITDA margin progression - Cash conversion and free cash flow generation - Quality of earnings: one-time items, add-backs, normalization adjustments - Reasonableness of management projections vs. historical performance and market benchmarks - Working capital seasonality or volatility 5. **Model return potential** - Entry valuation relative to comps (EV/Revenue, EV/EBITDA, P/E as appropriate) - Base, upside, and downside return scenarios - Target IRR and MOIC under each scenario; sensitivity to exit multiple and hold period - Capital structure impact: leverage, dilution from future rounds, liquidation preferences - Exit path assumptions: strategic sale, IPO, secondary, sponsor-to-sponsor [VERIFY — exit market conditions] 6. **Assess management and governance** - Track record of founders/CEO in scaling businesses - Key person risk and depth of management bench - Board composition and investor governance rights - Alignment of incentives: vesting, co-invest, earnout structures 7. **Identify key risks and mitigants** - Rank risks by likelihood and severity: customer concentration, technology obsolescence, regulatory, execution - Map each material risk to a specific mitigant or diligence workstream - Flag any risks that are binary and unmitigable (deal-breakers vs. pricing adjustments) ## Output Structure the evaluation report with these sections: - **Executive Summary**: One-paragraph investment thesis, proposed terms, and recommendation (proceed / pass / conditional proceed) - **Company & Market Overview**: Business description, market sizing, competitive positioning - **Financial Analysis**: Historical performance, projection assessment, quality-of-earnings highlights - **Return Analysis**: Entry valuation, scenario-based IRR/MOIC, sensitivity tables - **Risk Matrix**: Top 5–8 risks with severity rating, likelihood, and mitigant for each - **Diligence Priorities**: Ordered list of open items to resolve before final decision - **Recommendation**: Clear proceed/pass/conditional with specific conditions or next steps ## Quality Checks - Entry valuation benchmarked against at least 3 comparable transactions or public comps - Revenue and EBITDA projections stress-tested under downside assumptions, not just management case - All add-backs and normalization adjustments individually identified and justified - Customer concentration quantified (top 1, top 5, top 10 as % of revenue) - Exit assumptions grounded in recent market data, not aspirational multiples [VERIFY — current exit environment] - Every [VERIFY] tag resolved or explicitly flagged as an open diligence item - No forward-looking statements presented as facts; all projections labeled as estimates - Fund mandate and portfolio construction fit explicitly addressed
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