majiayu000 OpportunityIQ Skill
What This Skill Does
OpportunityIQ is a two-layer system that helps financial advisors systematically discover and capture revenue opportunities:
Layer 1: Scenario Discovery - Extract structured revenue scenarios from financial advisor publications, articles, and market trends. Transform industry insights into actionable opportunity templates.
Layer 2: Client Matching - Match clients to scenarios using systematic criteria, calculate revenue potential, and generate ranked opportunity lists (Top 25 weekly reports).
Together, these layers create a repeatable system for identifying opportunities that might otherwise be missed in day-to-day practice.
When to Use This Skill
Scenario Discovery Triggers
Use this skill when the user:
- Pastes or references an article from financial publications (ThinkAdvisor, Financial Advisor Magazine, Barron's, etc.)
- Says "help me find opportunities from this article"
- Asks to "extract a scenario" or "build my scenario library"
- Mentions wanting to discover new revenue opportunities from industry trends
- References specific publications: "I read an article about..."
Client Matching Triggers
Use this skill when the user:
- Has client data and asks to "scan my book" or "find opportunities"
- Requests "Top 25 opportunities" or "weekly opportunity report"
- Asks "which clients should I call this week?"
- Wants to know "what opportunities exist in my book"
- Provides client data (Google Sheet, CSV) and asks for analysis
- Says "match my clients to scenarios"
Combined Workflow Triggers
- "Build me a complete OpportunityIQ system"
- "Help me set up weekly opportunity scanning"
- "I want to discover scenarios AND find client matches"
How to Use This Skill
MODE 1: Scenario Extraction from Articles
User provides: Article text, URL, or publication content
Claude's process:
- Read and analyze the article
- Identify opportunity patterns using the Scenario Extraction Framework (see references/)
- Ask the 6 key questions:
- What's the opportunity?
- Who is this for?
- How do you identify them?
- What's the revenue?
- What are the exclusions?
- What's the next action?
- Structure the scenario using the standard template
- Provide the completed scenario ready to add to the user's library
Output format: Structured scenario with:
- Scenario ID and name
- Business case (why it matters)
- Matching criteria (specific, testable)
- Revenue calculation formula
- Exclusions
- Implementation steps
- Source attribution
Example interaction:
User: "I found this ThinkAdvisor article about rising FIA rates. Help me extract a scenario."
Claude: [Reads article, identifies opportunity, asks clarifying questions, then provides:]
"**Scenario: FIA-001 - Surrender Period Ending Review**
Business Case: FIA rates have increased 2-3% over past 24 months. Clients who purchased during low-rate environment are earning significantly less. Post-surrender is optimal upgrade window.
Matching Criteria:
- Product_Type = 'FIA'
- Purchase_Date >= 5 years ago
- Current_Cap_Rate < 5.5%
- Surrender_End_Date within 12 months
Revenue: FIA_Value × 0.05 (5% commission)
Example: $500K FIA = $25,000 revenue
Next Actions:
1. Pull surrender schedule
2. Run current vs. new product illustration
3. Schedule 30-min review call
..."
MODE 2: Client Opportunity Matching
User provides:
- Scenario library (scenarios they want to test)
- Client data (Google Sheet, CSV, or structured data)
Claude's process:
- Review the scenario library
- Understand the client data structure
- For each scenario, apply matching criteria to client data
- Identify all client-scenario matches
- Calculate revenue potential using formulas from references/
- Apply business rules (max 1 opportunity per client, urgency weighting)
- Rank by revenue potential
- Filter to Top 25 (or user-specified number)
- Format as actionable report
Output format: Ranked opportunity list with:
- Client name/ID
- Opportunity scenario
- Why it's a match (specific data points)
- Revenue estimate with calculation shown
- Urgency indicator
- Next action recommendation
Example interaction:
User: "Here's my client data [Google Sheet link]. I have 12 scenarios in my library. Find my Top 25 opportunities."
Claude: [Analyzes data, applies matching criteria, calculates revenue, ranks]
"**Top 25 Opportunities - Week of [Date]**
1. **Martinez, Sofia - FIA Surrender Review**
- Current FIA: $487K, purchased 2019, cap 4.2%
- Surrender ends: 2 months
- New rates: 6.5-7% available
- Revenue Est: $24,350 (5% commission)
- Action: Schedule review before March 15
2. **Johnson, Robert - Cash Drag Opportunity**
- Cash balance: $180K earning 0.5%
- Move to money market at 5.0%
- Revenue Est: $1,800/year (1% AUM)
- Action: 15-min call to reposition
3. **Davis, Jennifer - Concentrated Position**
- 45% portfolio in AAPL ($320K)
- Diversification opportunity
- Revenue Est: $16,000 (alternatives placement)
- Action: Risk review + hedging conversation
..."
MODE 3: Combined Workflow
User says: "Help me build a complete OpportunityIQ system"
Claude guides through:
- Discovery setup: Which publications to monitor, extraction schedule
- Library building: Extract 10-15 starter scenarios or use provided library
- Data integration: Connect to client data source
- First scan: Run initial matching to prove concept
- Ongoing workflow: Set up weekly discovery + weekly scanning cadence
The Scenario Extraction Framework
When extracting scenarios from articles, always gather:
1. Opportunity Identification
What's the specific action an advisor can take?
- Not just "rates are rising" but "review clients with low-yielding cash"
- Must be actionable, not just informational
2. Client Segmentation
Who does this apply to?
- Demographics (age, net worth, life stage)
- Product holdings (FIA, life insurance, concentrated positions)
- Behavioral triggers (recent events, concerns)
3. Matching Criteria
How do you identify them systematically?
- Must be specific, testable criteria
- Data-driven (can query from database)
- Example:
Product_Type = 'FIA' AND Purchase_Date > 5 years ago
4. Revenue Calculation
How do you monetize this?
- Product commission formula
- AUM fee calculation
- Planning fee estimate
- Must be quantifiable
5. Exclusions
Who does this NOT apply to?
- Prevents false positives
- Client preferences or circumstances
- Recent actions that disqualify
6. Implementation Path
What's the actual next action?
- First conversation/meeting
- Data gathering needed
- Implementation timeline
For detailed extraction methodology, see references/scenario-extraction-framework.md
Revenue Calculation Methods
OpportunityIQ uses standard financial advisor revenue models:
Product Sales (Commission-Based)
Revenue = Product_Value × Commission_Rate
FIA Replacement: 5-6% of product value
Life Insurance: 1% of face value (varies by product)
Annuity Sale: 4-7% depending on type
Asset Management (AUM-Based)
Revenue = New_AUM × Annual_Fee_Rate
Standard: 1% annually
Examples:
- $100K to managed account = $1,000/year
- $500K portfolio reposition = $5,000/year
Planning Services (Fee-Based)
Revenue = Hours × Hourly_Rate
OR
Revenue = Flat_Fee
Tax planning: $500-2,500
Estate planning: $2,000-10,000
Comprehensive plan: $3,000-15,000
For complete formulas and examples, see references/revenue-calculation-formulas.md
Business Rules for Opportunity Ranking
When generating Top 25 lists, apply these rules:
One Opportunity Per Client Rule
- If a client matches multiple scenarios, select highest revenue
- Exception: Bundle complementary opportunities (tax + reposition)
Urgency Weighting
- Time-sensitive (deadline): 1.3x multiplier
- Urgent (next 30 days): 1.2x multiplier
- Near-term (31-90 days): 1.1x multiplier
- Strategic (90+ days): 1.0x multiplier
Complexity Adjustment
- Simple (one call): No adjustment
- Moderate (standard meeting): No adjustment
- Complex (multiple meetings): ÷ 1.1x
- Advanced (professional coordination): ÷ 1.2x
Minimum Revenue Threshold
- Only include opportunities > $500 estimated revenue
- Adjustable based on practice size
Example Scenarios in Starter Library
Users can begin with these 12 pre-built scenarios:
Fixed Indexed Annuities (3)
- FIA-001: Surrender Period Ending Review
- FIA-002: Low Crediting Rate Upgrade
- FIA-003: Income Rider Optimization
Market/Cash Management (3)
- MKT-001: Rising Rate Bond Ladder Opportunity
- MKT-002: Cash Drag Repositioning
- MKT-003: Equity Volatility Protection
Diversification (3)
- DIV-001: Concentrated Position Review
- DIV-002: Single Sector Overweight
- DIV-003: International Equity Underweight
Tax Planning (3)
- TAX-001: Year-End Tax Loss Harvesting
- TAX-002: Q1 Tax Loss + Roth Conversion
- TAX-003: Market Downturn Tax Loss
See assets/starter-scenarios.md for complete details on each scenario.
Data Requirements
For Scenario Extraction
Input: Article or publication content No data integration required
For Client Matching
Required data fields:
- Client ID/Name
- Basic demographics (age, net worth)
- Product holdings (type, value, purchase date)
- Account data (cash balances, yields, holdings)
Optional but helpful:
- Risk tolerance
- Recent communications/notes
- Life events
- Goals/objectives
Supported formats:
- Google Sheets (preferred)
- CSV files
- Structured data in conversation
Output Formats
Scenario Extraction Output
Structured scenario document with all fields completed, ready to add to library or test against client data.
Client Matching Output
Standard format: Top 25 opportunities ranked by revenue
Optional formats:
- Top 10 for focused week
- Opportunities by scenario type
- Opportunities by client segment
- Urgency-sorted (deadlines first)
Delivery options:
- Text report in conversation
- Markdown document
- Email-ready format
- Google Sheet export
Tips for Best Results
Scenario Discovery
- Start with high-quality sources: Stick to Financial Advisor Magazine, ThinkAdvisor, Barron's, Best's Review
- Look for specific triggers: Articles with "opportunity for advisors" or "clients should review"
- Test scenarios: Always validate matching criteria against sample clients before activating
- Build gradually: Start with 10-15 scenarios, expand to 25-50 over time
Client Matching
- Clean data first: Ensure client data is current and accurate
- Validate matches: Spot-check first 5-10 matches to ensure criteria work correctly
- Adjust thresholds: Fine-tune minimum revenue or urgency weights based on your practice
- Act quickly: Top 25 should be actionable THIS WEEK, not aspirational
Combined System
- Weekly cadence: Discover scenarios weekly (1-2 hours), scan clients weekly (automated)
- Track performance: Note which scenarios generate actual revenue
- Retire underperformers: Remove scenarios that don't produce opportunities after 3 months
- Refine criteria: Adjust matching rules based on false positives/negatives
Supporting Documentation
This skill references detailed methodologies in the references/ directory:
- scenario-extraction-framework.md: Complete extraction methodology, examples, and templates
- client-matching-methodology.md: Detailed matching logic, business rules, and edge cases
- revenue-calculation-formulas.md: All revenue calculation methods with examples
- publication-sources.md: Recommended publications and how to monitor them
Pre-built assets in the assets/ directory:
- starter-scenarios.md: Complete details on 12 ready-to-use scenarios
- scenario-library-template.csv: Template for building your own scenario library
Skill Evolution
As you use OpportunityIQ, the skill improves through:
- Performance tracking: Which scenarios generate actual revenue
- Criteria refinement: Adjusting matching rules to reduce false positives
- Library expansion: Growing from 12 → 50+ scenarios over 6-12 months
- Pattern recognition: Identifying which types of opportunities work best for your practice
The goal is a self-improving system that gets better at finding opportunities the longer you use it.
Quick Start Guide
Week 1: Prove the concept
- Use the 12 starter scenarios (no extraction needed)
- Provide client data (10-50 clients)
- Run first scan
- Review Top 25 opportunities
- Validate: Would you act on at least 5 of these?
Week 2-4: Expand the system
- Extract 5-10 new scenarios from recent articles
- Re-scan clients with expanded library
- Set up weekly discovery workflow (1 hour Friday)
- Set up automated weekly scanning
Month 2+: Optimize and scale
- Track which scenarios generate revenue
- Retire underperformers, double down on winners
- Expand library to 30-50 scenarios
- Fine-tune matching criteria based on results
Questions During Use
If the user asks:
- "How do I find publications to monitor?" → Reference publication-sources.md
- "How do I calculate revenue for [X]?" → Reference revenue-calculation-formulas.md
- "Show me an example extraction" → Reference scenario-extraction-framework.md
- "What are the starter scenarios?" → Reference starter-scenarios.md
- "How do I test matching criteria?" → Use a small sample of client data, validate matches manually
- "What if I have too many matches?" → Increase minimum revenue threshold or tighten criteria
- "What if I have too few matches?" → Loosen criteria, expand scenario library, or check data quality
Always guide users toward building a systematic, repeatable process rather than one-off analysis.