Business Investment Advisor Skill

Overview

Production-ready investment analysis toolkit for screening opportunities, analyzing portfolio composition, and generating due diligence checklists. Designed for business owners, angel investors, and corporate development teams evaluating investments from $50K to $50M.

Clarify First

Before the analysis, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Which task: screen opportunities, analyze a portfolio, or generate a DD checklist (selects the script and the required input JSON).
• Screening thresholds: minimum ROI, maximum payback, and acceptable risk level (drives which opportunities pass and how they rank).
• Target profile for DD: investment type, stage, and check size (drives which items and weights the due-diligence checklist generates).
• Risk tolerance / concentration limits: max exposure per holding or sector (drives portfolio rebalancing recommendations).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Quick Start

# Screen investments by criteria (ROI, risk, payback)
python scripts/investment_screener.py opportunities.json --min-roi 15 --max-payback 36

# Analyze portfolio diversification and risk exposure
python scripts/portfolio_analyzer.py portfolio.json

# Generate due diligence checklist for an investment target
python scripts/due_diligence_checklist.py --type saas --stage series-a --amount 500000

Tools Overview

Tool	Purpose	Input	Output
investment_screener.py	Filter & rank investments	JSON with opportunity data	Ranked opportunities + scores
portfolio_analyzer.py	Portfolio risk & diversification	JSON with holdings	Risk report + recommendations
due_diligence_checklist.py	DD checklist generation	Investment parameters	Structured checklist + scoring

Workflows

Workflow 1: Opportunity Evaluation Pipeline

1. Compile investment opportunities into JSON format (see Common Patterns)
2. Run investment_screener.py with your criteria filters
3. Review ranked results focusing on composite score
4. For top candidates, run due_diligence_checklist.py to generate investigation plan
5. After DD completion, update portfolio model and run portfolio_analyzer.py

Workflow 2: Portfolio Health Check

1. Export current holdings to JSON format
2. Run portfolio_analyzer.py to assess diversification
3. Review concentration risk, sector exposure, and liquidity analysis
4. Use recommendations to identify rebalancing opportunities
5. Screen new opportunities with investment_screener.py to fill gaps

Workflow 3: Due Diligence Sprint

1. Run due_diligence_checklist.py with target parameters
2. Assign checklist items to team members with deadlines
3. Score each item as investigation progresses (0-10)
4. Re-run with --score-file to get weighted DD score
5. Use composite score to support go/no-go decision

Reference Documentation

See references/investment-frameworks.md for detailed frameworks including:

• Investment scoring methodology
• Risk assessment matrix
• Portfolio diversification guidelines
• Due diligence phase frameworks
• Industry-specific evaluation criteria

Common Patterns

Pattern: Investment Opportunities JSON

{
  "opportunities": [
    {
      "name": "TechCo SaaS",
      "type": "equity",
      "sector": "technology",
      "stage": "series-a",
      "amount": 250000,
      "expected_roi_pct": 25.0,
      "risk_level": "high",
      "payback_months": 36,
      "revenue": 1200000,
      "revenue_growth_pct": 85.0,
      "gross_margin_pct": 78.0,
      "burn_rate_monthly": 80000,
      "runway_months": 18
    }
  ]
}

Pattern: Portfolio Holdings JSON

{
  "portfolio": {
    "total_invested": 2000000,
    "holdings": [
      {
        "name": "Investment A",
        "type": "equity",
        "sector": "technology",
        "invested": 250000,
        "current_value": 375000,
        "date_invested": "2024-06-15",
        "stage": "series-a",
        "liquidity": "illiquid",
        "status": "active"
      }
    ]
  }
}

Risk Level Definitions

Level	Expected Return	Loss Probability	Typical Payback
Low	5-10%	< 10%	< 24 months
Medium	10-20%	10-30%	24-48 months
High	20-40%	30-50%	36-60 months
Very High	40%+	> 50%	48+ months

Scope & Limitations

In Scope:

• Screening and ranking investment opportunities with investment_screener.py on a weighted composite score (return, risk, growth, efficiency, payback), with filters for minimum ROI, maximum payback, risk level, sector and amount
• Portfolio diversification and concentration analysis with portfolio_analyzer.py against a conservative, moderate or aggressive risk profile
• Due diligence checklists from due_diligence_checklist.py by company type (SaaS, e-commerce, marketplace, hardware, general), stage and check size, plus a weighted DD score from a score file
• Frameworks in references/investment-frameworks.md: scoring method, risk assessment matrix, diversification guidelines and DD phases

Out of Scope:

• DCF valuation, ratio analysis and financial statement modelling (see finance/financial-analyst/)
• SaaS metric calculation such as MRR, churn, LTV and CAC (see finance/saas-metrics-coach/)
• M&A deal process and integration planning (see c-level-advisor/ma-playbook/)
• Legal review of term sheets and investment documents (see c-level-advisor/general-counsel-advisor/)
• Pricing of public securities, trade execution and tax treatment

Important Caveats:

• The tools score only what is entered in the JSON. Expected ROI, risk level, payback and current holding values are manual inputs, and the tools fetch no market or company data.
• Composite weights, score bands and the risk level definitions are fixed heuristics. They rank options consistently but do not estimate the real probability of loss.
• The output is decision support, not investment, legal or tax advice. A generated DD checklist does not show that diligence was done.