name	opportunityiq
description	Intelligent revenue opportunity discovery system for financial advisors. Extracts structured revenue scenarios from publications and matches them to client books of business to identify high-value opportunities. Use when analyzing industry articles to discover new scenarios, or when scanning client data to find revenue opportunities and generate Top 25 opportunity reports.

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.

Install Skill

SKILL.md