Claude-Skills business-investment-advisor

install

source · Clone the upstream repo

git clone https://github.com/borghei/Claude-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/borghei/Claude-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/finance/business-investment-advisor" ~/.claude/skills/borghei-claude-skills-business-investment-advisor && rm -rf "$T"

manifest: finance/business-investment-advisor/SKILL.md

source content

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.

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

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

Workflow 2: Portfolio Health Check

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

Workflow 3: Due Diligence Sprint

Run
```
due_diligence_checklist.py
```
with target parameters
Assign checklist items to team members with deadlines
Score each item as investigation progresses (0-10)
Re-run with
```
--score-file
```
to get weighted DD score
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