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lead-intelligence skill

/.gemini/skills/lead-intelligence

This skill performs deep lead qualification and predictive analysis for real estate prospects, leveraging EnterpriseHub and 28-feature scoring to boost

npx playbooks add skill chunkytortoise/enterprisehub --skill lead-intelligence

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: lead-intelligence
description: Advanced lead qualification and analysis using the EnterpriseHub GHL integration and predictive intelligence tools.
---

# Lead Intelligence Skill

Use this skill to perform deep lead qualification, property research, and predictive analysis for Jorge Salas' real estate business.

## Core Capabilities

1. **Public Records Research**: Use `get_public_records` to fetch property details.
2. **Predictive Analytics**: Use `detect_life_event_triggers` and `predict_propensity_to_sell` to identify high-intent leads.
3. **Lead Scoring Framework**: Apply the 28-feature pipeline logic to categorize leads.

## Lead Scoring Guidelines (28-Feature Pipeline)

When analyzing a lead, evaluate these core factors:

### 1. Budget Qualification (25%)
- Stated budget vs. market reality.
- Pre-approval status.
- Down payment availability.

### 2. Timeline Urgency (20%)
- Stated timeline to purchase/sell.
- Current housing situation.
- Market timing awareness.

### 3. Engagement Level (20%)
- Response rate to communications.
- Website interaction patterns (if available via CRM).
- Property viewing requests.

### 4. Geographic Focus (15%)
- Preferred area specificity.
- Realistic area expectations.

### 5. Behavioral Indicators (20%)
- Communication style and tone.
- Question quality and specificity.

## Scoring Grades

- **A-Grade (9-10)**: Hot leads ready to transact.
- **B-Grade (7-8)**: Warm leads needing nurturing.
- **C-Grade (5-6)**: Qualified prospects with longer timeline.
- **D-Grade (3-4)**: Requires significant qualification.
- **F-Grade (1-2)**: Not qualified or likely unviable.

## Workflow Integration

- Use the `enterprise-hub` MCP tools to fetch live data.
- Store results in the `PostgreSQL` database for long-term tracking.
- Update the `active-session.md` when a significant lead milestone is reached.

Overview

This skill delivers advanced lead qualification and predictive analysis tailored for a real estate business using EnterpriseHub GHL integration and intelligence tools. It combines public records, behavioral signals, and a 28-feature scoring pipeline to prioritize leads. The output produces actionable grades and recommended next steps for each lead.

How this skill works

The skill fetches property and contact data from public records and the EnterpriseHub connector, then runs life-event detection and propensity-to-sell models to surface high-intent signals. It computes a composite score across five pillar categories (budget, timeline, engagement, geography, behavior) and assigns a grade with recommended actions. Results are stored in PostgreSQL for tracking and workflow integration.

When to use it

  • When prioritizing incoming leads from GHL to decide immediate outreach.
  • During list enrichment to add public record and predictive signals before campaigns.
  • Before allocating showing resources or scheduling appointments.
  • When building monthly reports on pipeline quality and conversion risk.
  • To re-score stale leads after new activity or updated public records.

Best practices

  • Ensure contact and property identifiers are accurate before enrichment to avoid mismatches.
  • Regularly sync CRM activity and website interaction logs for up-to-date engagement signals.
  • Treat the score as a prioritization aid, not a single source of truth—always validate with a human touch.
  • Tune weighting periodically based on closed-deal outcomes to improve predictive precision.
  • Log model outputs and milestones to PostgreSQL for auditability and retrospective analysis.

Example use cases

  • Identify A-Grade leads for same-day outreach and expedited showing scheduling.
  • Segment B-Grade leads into a nurture workflow with targeted financing content.
  • Flag C/D leads for qualification calls to clarify budget and timeline details.
  • Refresh lead scores weekly after new public-record updates or CRM events.
  • Generate a report of high-propensity neighborhoods for hyperlocal marketing.

FAQ

What data sources does this skill use?

It combines EnterpriseHub CRM data, public records lookups, and predictive life-event and propensity models to build a unified lead profile.

How are scores translated into actions?

Scores map to grades (A–F) with predefined actions: immediate outreach for A, nurture sequences for B, qualification calls for C/D, and deprioritization or re-verification for F.