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, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• 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)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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