Open House Lead Manager (pf-open-house) v1.1

Turn messy open house sign-in data into BANT-qualified leads, CAN-SPAM compliant follow-ups, and NAR-standard seller reports in minutes — included with your subscription.

Quick Start

Installation

1. Install the plugin via Cowork: pip install cowork && cowork plugin install pf-open-house
2. Run setup: /open-house:setup (provide your workspace directory)
3. Edit .open-house/config.json with your agent branding, license number, and physical address

Usage

/open-house:event                    # Create a new open house event
/open-house:process [csv-path]       # Import sign-in CSV
/open-house:score                    # BANT-qualified lead scoring (0-100)
/open-house:follow-up                # CAN-SPAM compliant follow-up emails
/open-house:report                   # NAR-standard seller report
/open-house:run [csv-path]           # Run full pipeline at once
/open-house:status                   # Check progress

Commands

Command	Purpose	Inputs	Outputs
setup	Initialize workspace with regulatory defaults	Workspace path	Folders + config.json
event	Create open house with NAR disclosure tracking	Property details	Event record + CSV template
process	Import CSV with Fair Housing compliant validation	CSV file path	Validated visitor data
score	BANT-adapted lead qualification (Fair Housing compliant)	Visitor data	Scored leads + market insights
follow-up	CAN-SPAM compliant emails with buyer rep disclosure	Scored visitors	JSON/Markdown/CSV emails
report	NAR-standard seller report with anonymized analytics	Scored data	DOCX with BANT analysis
run	Full pipeline (process→score→follow-up→report)	CSV path	All outputs
status	Check pipeline progress	(none)	Completion status + lead summary

Workflow

1. /open-house:setup
   ↓
2. /open-house:event [property details]
   ↓
   (Collect visitor sign-ins on CSV with buyer rep disclosure)
   ↓
3. /open-house:process [sign-ins.csv]
   ↓
4. /open-house:score  ← BANT-Adapted Qualification Framework
   ↓
5. /open-house:follow-up  ← CAN-SPAM Compliant + NAR Disclosures
   ├─→ Review emails (outbound/open-house-follow-ups.md)
   └─→ Send (copy/paste or CRM import)
   ↓
6. /open-house:report  ← NAR-Standard Seller Report
   └─→ Email seller report (outbound/open-house-seller-report.docx)

Or run all stages at once with /open-house:run.

AI-Powered Features

1. BANT-Adapted Lead Scoring: Professional qualification framework adapted from IBM's BANT methodology — scores visitors on Budget (pre-approval), Authority (agent status), Need (timeline urgency), and Timeline (behavioral signals) with Fair Housing Act compliance
2. Fair Housing Compliant Analytics: All scoring uses only objective financial readiness and behavioral engagement signals — no protected class characteristics per 42 USC §3601-3619
3. CAN-SPAM Compliant Email Generation: Every follow-up email includes physical address, unsubscribe mechanism, commercial identification, and honest subject lines per 15 USC §7701-7713
4. NAR Buyer Representation Disclosures: Emails to unrepresented buyers include seller representation disclosure per NAR 2024 settlement
5. Intelligent Tier Classification: Hot (≥70), Warm (40-69), Cold (<40) with transparent BANT-labeled rationale for each score
6. Market Sentiment Analysis: Keyword-extracted themes from visitor comments — pricing sentiment, property strengths, buyer concerns
7. Buyer Readiness Indicators: Aggregate BANT metrics — pre-approved %, unrepresented %, active timeline %
8. Professional Seller Reports: NAR-standard DOCX with anonymized visitor analytics, BANT tier distribution, and data-driven pricing/staging recommendations
9. Privacy-First Architecture: All data stays local; visitor names anonymized in seller reports per NAR Code of Ethics Article 1
10. Regulatory-Aware Configuration: Setup includes Fair Housing acknowledgment, CAN-SPAM address requirement, NAR Code of Ethics year, and buyer rep disclosure toggle

Feature Comparison vs. Competitors

Feature	pf-open-house	Spacio	Open Home Pro	Curb Hero
Digital sign-in (tablet/QR)	CSV import	Full	Full	Full
Lead scoring framework	✓ BANT-adapted	✓ Proprietary	Partial	✗
Fair Housing compliance built-in	✓ (42 USC §3601)	Unknown	Unknown	Unknown
CAN-SPAM compliant emails	✓ (15 USC §7701)	✓	✓	Partial
NAR buyer rep disclosure	✓ (2024 settlement)	Unknown	Unknown	Unknown
Personalized follow-up emails	✓ (tier-specific)	✓	✓	Partial
Seller reports	✓ (professional DOCX)	✓	Partial	✗
CRM integration	CSV export	Full	Partial	Full (Zapier)
Automatic email sending	✗ (manual send)	✓	✓	Partial
Data privacy (local storage)	✓	✗	✗	✗
Customizable templates	✓	Partial	Partial	Partial
Offline capability	✓	✗	✓	Partial
Cost	Included	$300-1,200/yr	$240/yr	Included (basic)

Regulatory Compliance

Fair Housing Act (42 USC §3601-3619)

All lead scoring factors are strictly objective — financial readiness and behavioral engagement signals only. No scoring factor correlates with HUD-protected classes: race, color, religion, sex, disability, familial status, or national origin.

CAN-SPAM Act (15 USC §7701-7713)

Every generated follow-up email includes: physical mailing address, unsubscribe mechanism, commercial message identification, accurate headers, and honest subject lines. FTC penalty for non-compliance: up to $53,088 per email.

NAR Code of Ethics (2026)

Compliant with Article 1 (client interest), Article 9 (honest representation), Article 10 (equal service), Article 11 (market knowledge), Article 12 (truthful communication), and Article 16 (exclusive representation).

NAR 2024 Buyer Representation Settlement

Emails to unrepresented buyers include disclosure that the agent represents the seller, per the NAR settlement effective August 17, 2024.

TCPA (47 USC §227)

Phone numbers are stored for agent reference only. The plugin does not enable automated calling. Agents must obtain prior express consent before any automated phone outreach.

Estimated Cost per Use

Disclaimer: Token estimates are approximate and based on typical usage patterns measured from skill prompt sizes. Actual costs vary with input data size, conversation length, and complexity. Estimates use Claude Sonnet 4.6 pricing ($3/1M input, $15/1M output). Cowork and Claude Desktop subscription users (Pro/Max/Team) are not charged per-token — these estimates apply only to direct Anthropic API usage. Running stages individually in fresh sessions uses fewer input tokens than running the full pipeline sequentially, because pipeline mode accumulates conversation history across stages.

Per skill (run individually in a fresh session):

Stage	Skill Prompt	User Input	Total Input	Output	Est. Cost
open-house-report	~5.5K	~800	~9.1K	~6.0K	~$0.12
open-house-process	~4.7K	~800	~8.3K	~4.7K	~$0.09
open-house-score	~7.4K	~800	~11.0K	~6.0K	~$0.12
open-house-event	~2.8K	~800	~6.4K	~3.0K	~$0.06
open-house-follow-up	~5.3K	~800	~8.9K	~5.3K	~$0.11
Standalone total			~43.8K	~25.0K	~$0.51

Full pipeline (all stages in one session — context accumulates):

Stage	Base Input	+ History	Total Input	Output	Est. Cost
open-house-report	~9.3K	0	~9.3K	~6.0K	~$0.12
open-house-process	~8.5K	~6.8K	~15.3K	~4.7K	~$0.12
open-house-score	~11.2K	~12.3K	~23.5K	~6.0K	~$0.16
open-house-event	~6.6K	~19.1K	~25.7K	~3.0K	~$0.12
open-house-follow-up	~9.1K	~22.9K	~32.0K	~5.3K	~$0.18
Pipeline total			~105.8K	~25.0K	~$0.69

Running the full pipeline once typically costs $0.48–$0.90 in API tokens (Claude Sonnet 4.6).

Known Limitations

1. No Real-Time Digital Sign-In: Requires CSV import from external form tool (Google Forms, Jotform, paper-to-sheet).

2. No CRM Auto-Sync: Emails are generated as text/CSV for manual copying into email client or CRM.

3. No Persistent Email Scheduling: All follow-ups are generated at once; agent schedules in their email client.

4. Session-Based Operation: Data stored as local JSON files, not cloud database.

5. Lead Scoring is Informational Only: Scores do not guarantee buyer intent. All outputs include legal disclaimer with statutory references.

Scoring Methodology — BANT-Adapted Real Estate Qualification

Each visitor is scored 0-100 using the BANT framework adapted for real estate:

BANT Factor	Real Estate Adaptation	Max Points
Budget	Pre-approval status (mortgage readiness)	20 pts
Authority	Agent representation (unrepresented = higher)	20 pts
Need	Buyer timeline urgency (0-6mo = 30pts)	30 pts
Timeline	Behavioral signals (comments + return visits)	30 pts

Tiers: Hot ≥70, Warm 40-69, Cold <40

Configuration

Edit .open-house/config.json to customize:

{
  "agent_name": "Your Name",
  "brokerage": "Your Brokerage",
  "phone": "(555) 555-5555",
  "email": "agent@example.com",
  "license_number": "LICENSE123",
  "physical_address": "123 Main St, Suite 100, City, ST 00000",
  "compliance": {
    "fair_housing_acknowledgment": "...",
    "can_spam_physical_address_required": true,
    "nar_code_of_ethics_year": 2026,
    "buyer_representation_disclosure_enabled": true
  },
  "follow_up_templates": { "hot": {...}, "warm": {...}, "cold": {...} }
}

Privacy & Legal

• Data Privacy: All data stored locally; visitor PII never uploaded or shared
• Buyer Privacy: Visitor names anonymized in seller reports per NAR Code of Ethics Article 1
• Fair Housing: Compliant with 42 USC §3601-3619 — no protected class factors in scoring
• CAN-SPAM: All emails include physical address, unsubscribe, and commercial identification per 15 USC §7701-7713
• TCPA: Phone data stored for reference only; no automated calling per 47 USC §227
• NAR Ethics: Compliant with 2026 Code of Ethics Articles 1, 9, 10, 11, 12, 16

Support

• Logs: Check .open-house/logs/ for detailed execution logs
• Troubleshooting: Run /open-house:status to check pipeline progress
• Feature Requests: Consider customizing your config or editing SKILL.md files

Important Disclaimers

• AI-Generated Content: This plugin uses AI (LLM) technology which can produce inaccurate or incomplete outputs. All content should be treated as a starting point and reviewed for accuracy before use.
• Not Professional Advice: Outputs do not constitute legal, financial, tax, medical, or other professional advice. Consult qualified professionals before making decisions based on generated content.
• No Compliance Guarantee: References to industry standards, regulations, or guidelines are for informational purposes only. This plugin does not guarantee compliance with any law or regulation. Users are responsible for verifying all outputs meet their specific regulatory requirements.
• No Endorsement or Affiliation: Mention of third-party products, standards, or organizations does not imply endorsement, partnership, or certification by those entities.