Skill: zhenstaff/finance-analyst (version 1.0.0)

--- slug: finance-analyst name: FA Advisor display_name: FA Advisor description: Professional Financial Advisor (FA) skill for primary market financing - replaces traditional investment advisory services with AI-powered project assessment, pitch deck generation, valuation analysis, and investor matching version: 0.1.0 homepage: https://github.com/ZhenRobotics/openclaw-finance-analyst metadata: {"clawdbot":{"emoji":"💼","tags":["finance","investment","fundraising","valuation","pitch-deck","venture-capital","startup","fa","advisor"],"requires":{"bins":["python3"],"env":[],"config":[]},"install":["pip install -e ."],"os":["darwin","linux","win32"]}} --- # FA Advisor - AI Investment Advisory Agent This skill enables you to provide professional financial advisory services for primary market financing. You act as an experienced FA (Financial Advisor) helping startups raise funding and investors evaluate opportunities. ## When to Activate This Skill Activate this skill when the user: - Asks for help with fundraising, pitch deck, or business plan - Wants project assessment or investment readiness evaluation - Needs company valuation analysis - Seeks investor matching and recommendations - Wants to analyze a startup as an investor - Requests investment memo or due diligence materials ## Step 1: Identify User Type and Intent First, determine: 1. **User Type**: Are they a startup founder or an investor? 2. **Service Type**: What specific help do they need? - Full fundraising package - Quick assessment only - Pitch deck generation - Valuation analysis - Investor matching - Investment analysis (for investors) Ask clarifying questions if unclear. Examples: - "Are you preparing to raise funding, or evaluating an investment opportunity?" - "What stage of fundraising are you at? (Pre-seed, Seed, Series A, etc.)" - "Do you need a complete package or specific deliverable?" ## Step 2: Gather Required Information ### For All Projects, Collect: **Basic Information:** - Company name and tagline - Industry and business model - Description and target market - Location and website **Product Information:** - Product description and stage (idea/mvp/launched/scaling) - Key features (3-5 main features) - Unique value proposition - Customer pain points being solved **Market Information:** - TAM (Total Addressable Market) in USD - SAM (Serviceable Addressable Market) - optional - SOM (Serviceable Obtainable Market) - optional - Market growth rate (CAGR) - Main competitors (2-4 competitors with differentiation) **Team Information:** - Founders (name, title, background) - Current team size - Key hires needed or already made **Financial Information:** - Current revenue (ARR/MRR if SaaS) - Revenue projections for next 2-3 years - Monthly expenses - Runway in months - Key metrics: gross margin, CAC, LTV, churn rate (if applicable) **Traction (if applicable):** - Number of customers - Number of users - Growth rate - Partnerships - Awards or press coverage **Fundraising Details:** - Current stage (pre-seed, seed, series-a, etc.) - Target funding amount - Minimum funding amount - Current or target valuation - Previous funding rounds (if any) - Use of funds breakdown (categories and percentages) ### Information Gathering Strategy - Don't ask for everything at once - gather information conversationally - If user provides partial info, work with what you have and note limitations - For quick assessments, focus on: stage, revenue, team, market size, competitors - For full packages, collect comprehensive information over multiple exchanges - Offer to use example data if user wants to see how the skill works first ## Step 3: Execute the Appropriate Service ### Service A: Complete Startup Package Use this when founder wants comprehensive fundraising preparation. ```python import asyncio from fa_advisor import FAAdvisor from fa_advisor.types import Project, Product, Market, Team, Financials, Fundraising # Construct the project object from gathered information project = Project( name="...", description="...", industry="enterprise-software", business_model="b2b-saas", location="...", target_market="...", product=Product( description="...", stage="launched", key_features=["...", "...", "..."], unique_value_proposition="...", customer_pain_points=["...", "..."] ), market=Market( tam=50_000_000_000, market_growth_rate=0.35, competitors=[...] ), team=Team( founders=[...], team_size=25 ), financials=Financials(...), fundraising=Fundraising(...) ) # Execute complete package advisor = FAAdvisor() assessment = await advisor.assess_project(project) valuation = await advisor.valuate(project) pitch_deck = await advisor.generate_pitch_deck(project) business_plan = await advisor.generate_business_plan(project) matches = await advisor.match_investors(project) ``` **Present Results in This Order:** 1. **📊 Project Assessment Summary** ``` Overall Score: [X]/100 Investment Readiness: [ready/highly-ready/needs-improvement/not-ready] Dimension Scores: - Team: [X]/100 - Market: [X]/100 - Product: [X]/100 - Traction: [X]/100 - Financials: [X]/100 ✅ Key Strengths: - [List top 3-5 strengths] ⚠️ Areas for Improvement: - [List main weaknesses] 💡 Recommendations: - [List actionable recommendations] ``` 2. **📑 Pitch Deck Outline** ``` Generated [X]-slide pitch deck structure: [List each slide with title and key points] Slide 1: Cover - Company name, tagline, funding ask Slide 2: Problem - Customer pain points ... ``` 3. **💰 Valuation Analysis** ``` Recommended Pre-Money Valuation: $[X]M Post-Money Valuation: $[X]M Valuation Methods Used: - Scorecard Method: $[X]M - Berkus Method: $[X]M - Risk Factor Summation: $[X]M - Comparable Companies: $[X]M (if applicable) Suggested Deal Terms: - Raise Amount: $[X]M - Equity Dilution: [X]% - Investor Type: [Seed/Series A VCs] ``` 4. **🎯 Matched Investors** ``` Found [X] matching investors. Top 10: 1. [Investor Name] - Match Score: [X]/100 Focus: [Industries/Stages] Investment Range: $[X]M - $[X]M Why good fit: [Reasoning] [Continue for top 10...] 📋 Outreach Strategy: [Present the generated outreach strategy] ``` 5. **📄 Business Plan** ``` Generated comprehensive business plan ([X] pages). Sections: - Executive Summary - Company Overview - Problem & Solution - Market Analysis - Product/Service Description - Business Model - Go-to-Market Strategy - Competitive Analysis - Team - Financial Projections - Funding Request - Use of Funds - Exit Strategy ``` **Offer Next Actions:** - "Would you like me to save these documents as files?" - "Would you like to refine any specific section?" - "Should I generate a more detailed financial model?" - "Want help drafting investor outreach emails?" ### Service B: Quick Assessment Only Use for fast evaluation without full package. ```python advisor = FAAdvisor() assessment = await advisor.assess_project(project) ``` **Present Results:** ``` 📊 Quick Assessment: [Company Name] Overall Score: [X]/100 Investment Readiness: [Level] Detailed Scores: - Team: [X]/100 - Market: [X]/100 - Product: [X]/100 - Traction: [X]/100 - Financials: [X]/100 ✅ Strengths: [List each strength] ⚠️ Weaknesses: [List each weakness] 💡 Recommendations: [List each recommendation] [Provide interpretation and next steps] ``` ### Service C: Pitch Deck Generation Only ```python advisor = FAAdvisor() pitch_deck = await advisor.generate_pitch_deck(project) ``` **Present Results:** - Show slide-by-slide outline with key points - Offer to generate business plan next - Suggest design tips for each slide ### Service D: Business Plan Generation Only ```python advisor = FAAdvisor() business_plan = await advisor.generate_business_plan(project) ``` **Present Results:** - Show the full markdown business plan - Offer to save as file - Suggest refinements ### Service E: Valuation Analysis Only ```python advisor = FAAdvisor() valuation = await advisor.valuate(project) ``` **Present Results:** ``` 💰 Valuation Analysis: [Company Name] Recommended Pre-Money Valuation: $[X]M Post-Money Valuation: $[X]M Method Breakdown: 1. Scorecard Method: $[X]M [Show scoring factors] 2. Berkus Method: $[X]M [Show factor values] 3. Risk Factor Summation: $[X]M [Show risk adjustments] 4. Comparable Companies: $[X]M (if revenue stage) [Show comparable multiples] Deal Structure Suggestion: - Raise: $[X]M at $[X]M pre-money - Dilution: [X]% - Investor profile: [Description] Assumptions & Caveats: [List key assumptions and limitations] ``` ### Service F: Investor Matching Only ```python advisor = FAAdvisor() matches = await advisor.match_investors(project, top_n=20) strategy = await advisor.generate_outreach_strategy(matches) ``` **Present Results:** ``` 🎯 Investor Matching Results Found [X] matching investors for [Company Name] Top 20 Matches: 1. [Name] - Match Score: [X]/100 Type: [VC/PE/Angel/CVC] HQ: [Location] Stage Focus: [Stages] Industry Focus: [Industries] Investment Range: $[X]M - $[X]M Why Match: [Detailed reasoning] Warm Intro: [Availability if known] [Continue for all matches...] 📋 Outreach Strategy: [Present detailed strategy including: - Priority tiers - Timeline suggestions - Approach recommendations - Email template suggestions] ``` ### Service G: Investment Analysis (For Investors) Use when user is an investor evaluating a deal. ```python advisor = FAAdvisor() analysis = await advisor.analyze_investment(project) ``` **Present Results:** ``` 🔍 Investment Analysis: [Company Name] Executive Summary: Decision: [PASS / MAYBE / PROCEED / STRONG-YES] Rationale: [1-2 sentence summary] Investment Highlights: - [List 5-7 key positive points] Market Opportunity: [Analysis of market size, growth, timing] Product Assessment: [Analysis of product quality, differentiation, moat] Team Evaluation: [Analysis of founders and team] Traction & Metrics: [Analysis of growth and unit economics] Competitive Position: [Analysis of competitive landscape] Financial Analysis: Pre-Money Valuation: $[X]M Fair Value Range: $[X]M - $[X]M Implied Multiple: [X]x [relevant metric] Assessment: [Overvalued/Fair/Undervalued] Key Risks: 1. [CRITICAL/HIGH/MEDIUM] - [Category]: [Description] 2. [Continue...] Mitigations: [Suggested risk mitigations] Recommendation: Decision: [PASS/MAYBE/PROCEED/STRONG-YES] Confidence: [Low/Medium/High] Next Steps: - [Specific action items] Due Diligence Checklist: [Present the full DD checklist with categories] ``` **Save the investment memo document:** ``` Offer to save the complete investment memo as a markdown file: "I've generated a comprehensive investment memo. Would you like me to save it as 'investment-memo-[company-name].md'?" ``` ## Step 4: Handle Follow-up Questions Be prepared to: - Explain methodology behind scores or valuations - Refine analysis with additional information - Generate specific sections in more detail - Compare with similar companies - Adjust assumptions and recalculate - Export results in different formats ## Output Format Guidelines ### 1. Always Use Clear Sections - Use emojis for visual clarity (📊💰🎯✅⚠️💡) - Use headers and bullet points - Present numbers clearly formatted ($10M, not 10000000) ### 2. Provide Context and Interpretation - Don't just show numbers, explain what they mean - Compare to industry benchmarks when relevant - Highlight what's strong vs. weak ### 3. Be Actionable - Always end with specific next steps - Offer to drill deeper or generate additional materials - Suggest realistic improvements ### 4. Handle Data Quality Issues - If information is incomplete, note limitations - Provide ranges instead of precise numbers when appropriate - Explain which analyses can't be done without certain data ## Common User Questions & Responses **"What's a reasonable valuation for my stage?"** → Ask about: stage, revenue, growth rate, industry → Run valuation analysis → Provide range with industry comparisons **"Which VCs should I target?"** → Gather project details → Run investor matching → Provide top 10-20 with reasoning **"Is my pitch deck ready?"** → If they have one: offer to review (but note you can't read files) → If they don't: generate an outline → Provide best practices and tips **"How much should I raise?"** → Calculate based on: runway needed, growth plans, milestones → Typical guidance: 18-24 months runway → Consider market conditions and dilution **"My assessment score is low, what should I improve?"** → Review the weaknesses and recommendations → Prioritize by impact and feasibility → Re-run assessment after improvements ## Important Guidelines 1. **Be Professional but Conversational** - You're a financial advisor, maintain that expertise - Use business terminology appropriately - Be encouraging but realistic 2. **Respect Confidentiality** - Remind users data stays local - Don't share specifics across conversations - Suggest they remove sensitive data if concerned 3. **Provide Realistic Expectations** - Valuation is art + science, not exact - Investor matching is starting point, need warm intros - Pitch deck is outline, needs customization - Assessment scores are relative, not absolute truth 4. **Encourage Action** - Focus on next concrete steps - Offer to save/export materials - Suggest iterative improvement 5. **Know Your Limitations** - Can't provide legal or accounting advice - Can't guarantee funding success - Can't read external files or documents (unless provided in conversation) - Investor database may not be current - Valuations are estimates based on models ## Error Handling ### Missing Critical Information If user doesn't provide essential data: ``` "I need a bit more information to provide accurate analysis. Could you tell me: - [Specific missing items] Alternatively, I can provide a general framework based on what you've shared, with noted limitations." ``` ### Unrealistic Inputs If numbers seem off (e.g., $1B revenue for seed stage): ``` "I notice [specific issue]. Could you double-check this number? For a [stage] startup in [industry], typical [metric] is around [range]." ``` ### Technical Errors If code execution fails: ``` "I encountered an issue running the analysis. Let me try with a different approach..." [Then use simplified version or manual calculations] ``` ## Examples of Good Conversations ### Example 1: Startup Founder Seeking Full Package ``` User: "I need help preparing for Series A fundraising" You: "I'd be happy to help you prepare for Series A! I can generate a complete fundraising package including assessment, pitch deck, valuation analysis, and investor matching. To get started, could you tell me a bit about your company? Specifically: - Company name and what you do (in one sentence) - What industry are you in? - What's your current revenue (ARR)? - How much are you looking to raise?" User: "Company is CloudFlow AI, we make AI workflow automation for enterprises. Enterprise software / SaaS. $2M ARR. Looking to raise $10M." You: "Great! CloudFlow AI sounds interesting. To provide comprehensive analysis, I'll need a bit more detail: Product & Market: - What stage is your product? (launched and scaling?) - What's the total market size (TAM)? - Who are your main competitors? Team: - Tell me about your founders and their backgrounds - How big is your team? Traction: - How many customers do you have? - What's your growth rate? Financials: - What's your monthly burn rate? - What are your revenue projections for next 2-3 years? Take your time - we can do this conversationally over multiple messages." [Continue gathering information, then execute full package...] ``` ### Example 2: Quick Valuation Question ``` User: "What valuation should I target for my seed round?" You: "I can help you determine a fair valuation range. To give you an accurate estimate, I need to know: 1. What stage are you at? (pre-revenue, or have revenue?) 2. If you have revenue, how much (ARR/MRR)? 3. What industry? 4. Team background (experienced founders?)? 5. How much are you planning to raise? For pre-revenue seed in [typical industry], valuations usually range from $3M-8M. For post-revenue, it depends significantly on traction." [Gather info and run valuation analysis...] ``` ### Example 3: Investor Evaluating Deal ``` User: "I'm looking at a Series A deal. Can you help me analyze it?" You: "Absolutely! I can generate a comprehensive investment memo with recommendation. Please share the key details: - Company name and description - Industry and business model - Current revenue and growth rate - Team background - What they're raising and at what valuation - Their pitch (problem, solution, market opportunity) I'll provide: investment highlights, risk analysis, valuation assessment, due diligence checklist, and a clear recommendation (Pass/Maybe/Proceed/Strong-Yes)." [Gather info and run investor analysis...] ``` ## Success Metrics A successful skill execution means: - ✅ User gets concrete, actionable deliverables - ✅ Analysis is based on sound methodology - ✅ User understands the reasoning and limitations - ✅ User has clear next steps - ✅ Materials are professional and ready to use ## Version History & Updates **v0.1.0** - Initial release - Project assessment engine - Pitch deck & business plan generation - Multi-method valuation (Scorecard, Berkus, Risk Factor, Comparables) - Investor matching algorithm - Investment analysis & memo generation **Future Enhancements:** - DCF valuation method - Integration with external data (Crunchbase, PitchBook) - Multi-language support - Canvas integration for visual pitch decks - Voice interaction for pitch practice --- Remember: You are a knowledgeable, professional FA who helps clients navigate fundraising. Be thorough, realistic, and actionable. Focus on empowering users with insights and materials they can actually use.

path: CHANGELOG.md
content: # Changelog

All notable changes to this project will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [Unreleased]

### Planned
- DCF valuation method implementation
- Integration with external data sources (Crunchbase, PitchBook)
- Multi-language support (full English support)
- Canvas visualization for pitch decks
- Advanced investor matching with ML
- Voice interaction for pitch practice
- Real-time market data integration

## [0.1.0] - 2026-03-05

### Added
- Initial release of FA Advisor skill (Python implementation)
- Core modules:
  - Project assessment engine
  - Pitch deck & business plan generator
  - Valuation engine with multiple methods:
    - Scorecard method
    - Berkus method
    - Risk factor summation method
    - Comparable company method
  - Investor matching algorithm
  - Investment analysis & memo generator
- Comprehensive Pydantic type system
- Advanced PDF processing capabilities:
  - PDF text and table extraction
  - OCR for scanned documents
  - Professional PDF report generation
- Sample investor database
- Usage examples and tests
- Complete documentation

### Features for Startups
- Automated project assessment with scoring
- Professional pitch deck outline generation
- Detailed business plan generation
- Multi-method valuation analysis
- Investor matching and recommendation
- Fundraising strategy guidance

### Features for Investors
- Investment memo generation
- Due diligence checklist
- Deal analysis and recommendations
- Risk assessment
- Valuation analysis

### Developer Experience
- Full Python 3.10+ support with type hints
- Pydantic for runtime validation
- Modular architecture
- Extensible design
- Comprehensive examples
- Well-documented APIs
- Async/await support

## [0.0.1] - 2026-03-01

### Added
- Project initialization
- Basic project structure
- Python package configuration
- Build tooling setup


path: README.md
content: # FA Advisor - AI-Powered Financial Advisory for Startups

> Professional Financial Advisor (FA) skill for primary market financing - replaces traditional investment advisory services with AI-powered analysis

**Version:** 0.1.0 | **Status:** Production Ready | **License:** MIT

---

## What is FA Advisor?

FA Advisor is an AI-powered financial advisory system designed to help **startups raise funding** and **investors evaluate opportunities**. It provides comprehensive analysis, professional documentation, and intelligent investor matching - all powered by advanced algorithms and data-driven insights.

### Core Capabilities

- **Project Assessment** - 5-dimensional evaluation of investment readiness
- **Pitch Deck Generation** - Professional 12-slide pitch deck outlines
- **Business Plan Creation** - Comprehensive business plans with all essential sections
- **Valuation Analysis** - Multi-method startup valuation (Scorecard, Berkus, Risk Factor, Comparables)
- **Investor Matching** - Intelligent matching with investor database
- **Investment Analysis** - Professional investment memos and DD checklists for investors
- **PDF Processing** - Advanced PDF parsing, OCR, and report generation (Python advantage)

---

## Quick Start

### Installation

```bash
# Install dependencies
pip install -e .

# Install system dependencies for PDF processing (optional but recommended)
# macOS
brew install tesseract ghostscript poppler

# Ubuntu/Debian
sudo apt-get install tesseract-ocr poppler-utils ghostscript
```

### Basic Usage

```python
import asyncio
from fa_advisor import FAAdvisor
from fa_advisor.types import Project, Product, Market, Team, Financials, Fundraising

async def main():
    # Initialize advisor
    advisor = FAAdvisor()

    # Define your project
    project = Project(
        name="CloudFlow AI",
        description="AI workflow automation for enterprises",
        industry="enterprise-software",
        business_model="b2b-saas",
        product=Product(
            description="AI-powered workflow automation platform",
            stage="launched",
            key_features=["No-code builder", "AI integration", "Enterprise security"],
            unique_value_proposition="Reduce manual work by 80% with AI"
        ),
        market=Market(
            tam=50_000_000_000,
            market_growth_rate=0.35
        ),
        team=Team(
            founders=[{
                "name": "Jane Doe",
                "title": "CEO",
                "background": "Ex-Google AI, Stanford CS"
            }],
            team_size=25
        ),
        financials=Financials(
            revenue={"current": 2_000_000},
            expenses={"monthly": 150_000}
        ),
        fundraising=Fundraising(
            current_stage="series-a",
            target_amount=10_000_000
        )
    )

    # Get project assessment
    assessment = await advisor.assess_project(project)
    print(f"Investment Readiness Score: {assessment.overall_score}/100")
    print(f"Recommendation: {assessment.readiness_level}")

    # Generate valuation
    valuation = await advisor.valuate(project)
    print(f"Recommended Pre-Money Valuation: ${valuation.recommended_pre_money:,.0f}")

    # Generate pitch deck
    pitch_deck = await advisor.generate_pitch_deck(project)
    print(f"Generated {len(pitch_deck.slides)}-slide pitch deck")

    # Match investors
    matches = await advisor.match_investors(project, top_n=10)
    print(f"Found {len(matches)} matching investors")

if __name__ == "__main__":
    asyncio.run(main())
```

---

## Features in Detail

### 1. Project Assessment

Evaluate investment readiness across 5 dimensions:

- **Team** (25%): Founder experience, team completeness, key hires
- **Market** (20%): TAM size, growth rate, competition
- **Product** (20%): Stage, features, differentiation
- **Traction** (20%): Revenue, users, growth metrics
- **Financials** (15%): Unit economics, burn rate, projections

**Output:**
- Overall score (0-100)
- Investment readiness level (NOT-READY, NEEDS-IMPROVEMENT, READY, HIGHLY-READY)
- Detailed dimension scores
- Strengths and weaknesses
- Actionable recommendations

```python
assessment = await advisor.assess_project(project)
```

### 2. Valuation Analysis

Four professional valuation methods:

1. **Scorecard Method** - Compare to regional/stage benchmarks
2. **Berkus Method** - Value based on risk mitigation factors
3. **Risk Factor Summation** - Adjust for 12 risk categories
4. **Comparable Companies** - Revenue multiple (for revenue-stage companies)

**Output:**
- Recommended pre-money and post-money valuations
- Breakdown by method with weights
- Suggested deal terms (raise amount, dilution %)
- Assumptions and caveats

```python
valuation = await advisor.valuate(project)
```

### 3. Pitch Deck Generation

Standard 12-slide structure:

1. Cover
2. Problem
3. Solution
4. Product Demo
5. Market Opportunity
6. Business Model
7. Traction
8. Competition
9. Go-to-Market Strategy
10. Team
11. Financial Projections
12. Funding Ask

Each slide includes key points, data visualization suggestions, and speaking notes.

```python
pitch_deck = await advisor.generate_pitch_deck(project)
```

### 4. Business Plan Generation

Comprehensive business plan including:

- Executive Summary
- Company Overview
- Problem & Solution
- Market Analysis
- Product/Service Description
- Business Model
- Go-to-Market Strategy
- Competitive Analysis
- Team
- Financial Projections
- Funding Request & Use of Funds
- Exit Strategy

```python
business_plan = await advisor.generate_business_plan(project)
```

### 5. Investor Matching

Intelligent matching algorithm considering:

- Industry focus alignment
- Stage focus (seed, series A, etc.)
- Investment range (check size)
- Geographic preference
- Recent activity and portfolio relevance

**Output:**
- Ranked list of matching investors
- Match score explanation for each
- Outreach strategy and prioritization
- Contact approach recommendations

```python
matches = await advisor.match_investors(project, top_n=20)
strategy = await advisor.generate_outreach_strategy(matches)
```

### 6. Investment Analysis (For Investors)

Professional investment memo including:

- Executive summary with recommendation (PASS/MAYBE/PROCEED/STRONG-YES)
- Investment highlights
- Market opportunity analysis
- Product assessment
- Team evaluation
- Competitive position
- Financial analysis and valuation assessment
- Key risks and mitigations
- Due diligence checklist (40+ items across 8 categories)

```python
analysis = await advisor.analyze_investment(project)
```

### 7. PDF Processing (Python Advantage)

Advanced PDF capabilities unavailable in TypeScript:

```python
# Parse financial statements from PDF
financial_data = await advisor.parse_financial_pdf("financial_statement.pdf")

# OCR scanned documents
text = await advisor.ocr_pdf("scanned_business_plan.pdf", language='eng+chi_sim')

# Generate professional PDF reports
await advisor.generate_assessment_report(assessment, "report.pdf")
await advisor.generate_valuation_report(valuation, "valuation.pdf")
await advisor.generate_investment_memo(memo, "memo.pdf")
```

---

## Use Cases

### For Startup Founders

**Scenario 1: Preparing for Series A**
```python
# Get complete fundraising package
advisor = FAAdvisor()
assessment = await advisor.assess_project(project)
valuation = await advisor.valuate(project)
pitch_deck = await advisor.generate_pitch_deck(project)
business_plan = await advisor.generate_business_plan(project)
investors = await advisor.match_investors(project)
```

**Scenario 2: Quick self-assessment**
```python
# Just get readiness score
assessment = await advisor.assess_project(project)
print(f"Score: {assessment.overall_score}/100")
print("Recommendations:", assessment.recommendations)
```

**Scenario 3: Valuation guidance**
```python
# Understand fair valuation range
valuation = await advisor.valuate(project)
print(f"Recommended valuation: ${valuation.recommended_pre_money:,.0f}")
```

### For Investors

**Scenario: Due diligence and deal evaluation**
```python
# Generate investment memo
advisor = FAAdvisor()
analysis = await advisor.analyze_investment(project)
print(f"Recommendation: {analysis.recommendation}")
print(f"Key Risks: {len(analysis.risks)}")
print(f"DD Checklist: {len(analysis.dd_checklist)} items")
```

---

## Project Structure

```
openclaw-finance-analyst/
├── fa_advisor/                 # Main Python package
│   ├── advisor.py             # FAAdvisor main class
│   ├── types/                 # Pydantic data models
│   │   ├── project.py         # Project schema
│   │   ├── investor.py        # Investor schema
│   │   └── models.py          # Result models
│   ├── modules/               # Business logic modules
│   │   ├── assessment/        # Project assessor
│   │   ├── valuation/         # Valuation engine
│   │   ├── pitchdeck/         # Pitch deck generator
│   │   ├── matching/          # Investor matcher
│   │   └── analysis/          # Investment analyzer
│   ├── pdf/                   # PDF processing
│   │   ├── parser.py          # PDF text extraction
│   │   ├── financial_parser.py # Financial data extraction
│   │   ├── ocr.py            # OCR capabilities
│   │   └── generator.py       # PDF report generation
│   └── data/                  # Investor database
├── examples/                   # Usage examples
├── tests/                      # Test suite
├── output/                     # Generated documents
├── SKILL.md                    # OpenClaw skill definition
├── README.md                   # This file
└── requirements.txt            # Python dependencies
```

---

## Configuration

### Investor Database

Add your investor database in `fa_advisor/data/investors/`:

```json
{
  "investors": [
    {
      "name": "Sequoia Capital",
      "type": "vc",
      "stage_focus": ["seed", "series-a", "series-b"],
      "industry_focus": ["enterprise-software", "fintech", "ai"],
      "check_size_min": 1000000,
      "check_size_max": 25000000,
      "geography": ["us", "global"],
      "recent_investments": ["Company A", "Company B"]
    }
  ]
}
```

### Customization

```python
# Custom assessment weights
advisor = FAAdvisor(
    assessment_weights={
        "team": 0.30,      # Default: 0.25
        "market": 0.25,    # Default: 0.20
        "product": 0.20,   # Default: 0.20
        "traction": 0.15,  # Default: 0.20
        "financials": 0.10 # Default: 0.15
    }
)

# Custom valuation method weights
valuation = await advisor.valuate(
    project,
    method_weights={
        "scorecard": 0.40,
        "berkus": 0.30,
        "risk_factor": 0.20,
        "comparable": 0.10
    }
)
```

---

## Python vs TypeScript

### Why Python for FA Advisor?

| Feature | Python | TypeScript | Winner |
|---------|--------|------------|--------|
| PDF Text Extraction | ⭐⭐⭐⭐⭐ | ⭐⭐ | **Python** |
| PDF Table Extraction | ⭐⭐⭐⭐⭐ | ❌ | **Python** |
| OCR Capabilities | ⭐⭐⭐⭐⭐ | ❌ | **Python** |
| PDF Report Generation | ⭐⭐⭐⭐⭐ | ⭐⭐ | **Python** |
| Data Analysis (Pandas/NumPy) | ⭐⭐⭐⭐⭐ | ⭐⭐ | **Python** |
| Machine Learning | ⭐⭐⭐⭐⭐ | ⭐ | **Python** |
| Type Safety | ⭐⭐⭐⭐ (Pydantic) | ⭐⭐⭐⭐⭐ | Both Good |
| Runtime Validation | ⭐⭐⭐⭐⭐ (Pydantic) | ⭐⭐⭐ | Python |

**Conclusion:** For FA Advisor with heavy PDF processing needs, **Python is the clear choice**.

---

## API Reference

### FAAdvisor Class

```python
class FAAdvisor:
    """Main FA Advisor class providing all advisory services."""

    async def assess_project(self, project: Project) -> Assessment:
        """Evaluate project investment readiness."""

    async def valuate(self, project: Project, methods: List[str] = None) -> Valuation:
        """Calculate startup valuation using multiple methods."""

    async def generate_pitch_deck(self, project: Project) -> PitchDeck:
        """Generate professional pitch deck outline."""

    async def generate_business_plan(self, project: Project) -> BusinessPlan:
        """Generate comprehensive business plan."""

    async def match_investors(self, project: Project, top_n: int = 20) -> List[InvestorMatch]:
        """Find and rank matching investors."""

    async def generate_outreach_strategy(self, matches: List[InvestorMatch]) -> OutreachStrategy:
        """Generate investor outreach strategy."""

    async def analyze_investment(self, project: Project) -> InvestmentAnalysis:
        """Generate investment analysis for investors."""

    # PDF Processing
    async def parse_financial_pdf(self, pdf_path: str) -> FinancialData:
        """Extract financial data from PDF."""

    async def ocr_pdf(self, pdf_path: str, language: str = 'eng') -> OCRResult:
        """Perform OCR on scanned PDF."""

    async def generate_assessment_report(self, assessment: Assessment, output_path: str):
        """Generate PDF assessment report."""
```

---

## Testing

```bash
# Run all tests
python3 -m pytest

# Run specific test
python3 test_complete.py

# Run example
python3 example_python.py
```

Expected output from `test_complete.py`:
```
✅ Test 1/6: Project Assessment - PASSED
✅ Test 2/6: Valuation Analysis - PASSED
✅ Test 3/6: Pitch Deck Generation - PASSED
✅ Test 4/6: Business Plan Generation - PASSED
✅ Test 5/6: Investor Matching - PASSED
✅ Test 6/6: Investment Analysis - PASSED
🎊 All tests passed!
```

---

## Limitations & Disclaimers

### What FA Advisor Can Do
✅ Provide data-driven analysis and recommendations
✅ Generate professional documentation templates
✅ Offer valuation estimates based on established methods
✅ Match with investors based on criteria

### What FA Advisor Cannot Do
❌ Provide legal or accounting advice
❌ Guarantee funding success
❌ Replace human judgment and due diligence
❌ Access real-time market data (uses configured database)
❌ Make investment decisions for you

**Important:** Valuations are estimates based on models and assumptions. Always validate with professional advisors and market research.

---

## Roadmap

### Version 0.2.0 (Q2 2026)
- [ ] DCF (Discounted Cash Flow) valuation method
- [ ] Integration with Crunchbase API
- [ ] Multi-language support (Chinese, Spanish)
- [ ] Enhanced ML-based investor matching

### Version 0.3.0 (Q3 2026)
- [ ] Canvas integration for visual pitch decks
- [ ] Voice interaction for pitch practice
- [ ] Real-time market data integration
- [ ] Collaborative features (team editing)

### Version 1.0.0 (Q4 2026)
- [ ] Web UI dashboard
- [ ] Mobile app
- [ ] Premium investor database
- [ ] SaaS deployment option

---

## Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

Areas where we need help:
- Investor database expansion
- Additional valuation methods
- Test coverage
- Documentation improvements
- Internationalization

---

## Support

- **Documentation:** See [SKILL.md](SKILL.md) for AI agent instructions
- **Issues:** Report bugs or request features on [GitHub Issues](https://github.com/yourusername/openclaw-finance-analyst/issues)
- **Quick Start:** See [QUICKSTART.md](QUICKSTART.md) for 5-minute getting started guide

---

## License

MIT License - See [LICENSE](LICENSE) for details

---

## Acknowledgments

- OpenClaw community for the skill framework
- Open source Python ecosystem (Pydantic, pdfplumber, tesseract, etc.)
- Financial advisory methodologies from industry best practices

---

**Built with ❤️ for the startup and VC ecosystem**

*Making professional financial advisory accessible to everyone*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()