Crawler Summary

real-estate-lead-gen answer-first brief

Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. --- name: real-estate-lead-gen description: Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. --- Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Freshness

Last checked 4/15/2026

Best For

real-estate-lead-gen is best for listing, multiple workflows where MCP compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

Claim this agent
Agent DossierGitHubSafety: 94/100

real-estate-lead-gen

Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. --- name: real-estate-lead-gen description: Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. ---

MCPself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Apr 15, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Trust evidence available

Trust score

Unknown

Compatibility

MCP

Freshness

Apr 15, 2026

Vendor

Estherperdana7

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

Setup snapshot

git clone https://github.com/estherperdana7/real-estate-lead-gen.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Estherperdana7

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

MCP

contractmedium
Observed Apr 15, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

5

Snippets

0

Languages

typescript

Parameters

Executable Examples

bash

mkdir -p ~/lead_gen_pipeline

text

1. create_product: name="Real Estate Lead Intelligence — {METRO}"
2. create_price: Starter $149/mo recurring monthly
3. create_price: Starter $1,490/yr recurring yearly
4. create_price: Professional $299/mo recurring monthly
5. create_price: Professional $2,990/yr recurring yearly
6. create_price: Enterprise $599/mo recurring monthly
7. create_price: Enterprise $5,990/yr recurring yearly
8. create_payment_link: for each monthly price
9. create_coupon: name="FIRSTMONTH50", percent_off=50, duration="once"

text

You are a real estate lead qualification specialist for the {METRO} market.

Lead to Qualify: {{input}}

Score using this weighted rubric (1-10 each):
- Motivation (30%): 10=must sell, 5=considering, 1=curious
- Timeline (25%): 10=30 days, 7=90 days, 4=6 months, 1=none
- Property Value (20%): 10=$2M+, 8=$1M-2M, 5=$500K-1M, 3=under $500K
- Engagement (15%): 10=responded, 7=open house, 5=active, 3=none
- Competition (10%): 10=no agent (FSBO), 7=expired, 3=has agent

Tasks:
1. Search online for the property to verify details
2. Research comparable recent sales in the neighborhood
3. Assess seller motivation from available signals
4. Calculate weighted final score (1-10)
5. Write a personalized 3-4 sentence outreach script
6. Classify: HOT (8-10), WARM (5-7.9), NURTURE (1-4.9)

text

Research this real estate agent as a potential customer for an automated
lead generation service that delivers AI-qualified seller leads daily.

Agent: {{input}}

Find: website, email, market focus, estimated annual volume, buying signals
(active marketing, growing team, high volume, awards, thought leadership).

Score purchase likelihood (1-10).
Write a 3-4 sentence outreach email referencing their specific achievements
and offering a free sample report. Service pricing: Starter $149/mo,
Professional $299/mo, Enterprise $599/mo.

text

cron: "0 0 7 * * 1-5"
name: "Daily {METRO} Lead Discovery"
prompt: [Run stages 3-4 using files in ~/lead_gen_pipeline/]

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. --- name: real-estate-lead-gen description: Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. ---

Full README

name: real-estate-lead-gen description: Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area.

Real Estate Lead Generation Pipeline

Automated pipeline that discovers motivated seller leads, qualifies them with AI scoring, prospects for agent customers, monetizes via Stripe, and delivers results via Gmail on a recurring schedule.

Overview

The pipeline has 6 stages executed in order:

  1. Configure — Set target market, ICP, and scoring rubric
  2. Monetize — Create Stripe products, pricing tiers, and payment links
  3. Discover — Scan listing sites for motivated seller leads
  4. Qualify — Score leads in parallel using weighted rubric
  5. Prospect — Research and qualify target agent customers
  6. Automate — Schedule recurring discovery and delivery

Determine which stages to run based on user request. For first-time setup, run all 6. For recurring execution, run stages 3-4 only.

Stage 1: Configure

Create a working directory and configuration files.

mkdir -p ~/lead_gen_pipeline

Read the ICP template: references/icp_template.md. Customize for the user's target metro area by replacing placeholders ({METRO}, {BOROUGH_LIST}, {NEIGHBORHOOD_LIST}).

Save customized ICP to ~/lead_gen_pipeline/icp.md.

Read the email templates: references/email_templates.md. Customize company name and metro area. Save to ~/lead_gen_pipeline/email_templates.md.

Scoring Rubric (always use these weights):

| Factor | Weight | 10 = Best | 1 = Worst | |--------|--------|-----------|----------| | Motivation | 30% | Must sell (foreclosure, estate, divorce) | Just curious | | Timeline | 25% | Within 30 days | No timeline | | Property Value | 20% | $2M+ | Under $200K | | Engagement | 15% | Responded / open house | No signals | | Competition | 10% | No agent (FSBO) | Exclusive listing |

Final Score = (Motivation × 0.3) + (Timeline × 0.25) + (Value × 0.2) + (Engagement × 0.15) + (Competition × 0.1)

Tiers: HOT (8-10), WARM (5-7.9), NURTURE (1-4.9)

Stage 2: Monetize

Use the Stripe MCP server to create products and pricing. Run these commands in order:

1. create_product: name="Real Estate Lead Intelligence — {METRO}"
2. create_price: Starter $149/mo recurring monthly
3. create_price: Starter $1,490/yr recurring yearly
4. create_price: Professional $299/mo recurring monthly
5. create_price: Professional $2,990/yr recurring yearly
6. create_price: Enterprise $599/mo recurring monthly
7. create_price: Enterprise $5,990/yr recurring yearly
8. create_payment_link: for each monthly price
9. create_coupon: name="FIRSTMONTH50", percent_off=50, duration="once"

Save all IDs and links to ~/lead_gen_pipeline/stripe_config.md.

Tier features:

| Tier | Boroughs | Frequency | Leads/week | |------|----------|-----------|------------| | Starter $149/mo | 1 | Weekly digest | ~20 | | Professional $299/mo | 3 | Daily + weekly | ~50 | | Enterprise $599/mo | All | Real-time + daily + weekly | Unlimited |

Stage 3: Discover

Scan multiple sources for each lead category. Adapt source URLs to the target metro.

Category 1 — Price Reductions: Browse the metro's primary listing site (StreetEasy for NYC, Redfin/Zillow for others) filtered by price drops > 5% in the last 14 days. Collect: address, neighborhood, type, price, drop amount, beds/baths/sqft, brokerage.

Category 2 — FSBO: Browse Zillow FSBO section filtered to the target metro. Collect: address, borough, price, beds/baths/sqft, notes.

Category 3 — Expired/Long DOM: Search for listings with 90+ days on market or recently expired. Collect: address, neighborhood, type, price, DOM, brokerage.

Category 4 — Pre-Foreclosure: Search for lis pendens, NOD filings, or pre-foreclosure listings in the metro. Collect: address, borough, estimated value, filing type.

Save all raw leads to ~/lead_gen_pipeline/daily_leads_raw.md with date header and tables per category.

Target: 20-50 raw leads per scan.

Stage 4: Qualify

Use the map tool to qualify all discovered leads in parallel.

Prompt template for each lead:

You are a real estate lead qualification specialist for the {METRO} market.

Lead to Qualify: {{input}}

Score using this weighted rubric (1-10 each):
- Motivation (30%): 10=must sell, 5=considering, 1=curious
- Timeline (25%): 10=30 days, 7=90 days, 4=6 months, 1=none
- Property Value (20%): 10=$2M+, 8=$1M-2M, 5=$500K-1M, 3=under $500K
- Engagement (15%): 10=responded, 7=open house, 5=active, 3=none
- Competition (10%): 10=no agent (FSBO), 7=expired, 3=has agent

Tasks:
1. Search online for the property to verify details
2. Research comparable recent sales in the neighborhood
3. Assess seller motivation from available signals
4. Calculate weighted final score (1-10)
5. Write a personalized 3-4 sentence outreach script
6. Classify: HOT (8-10), WARM (5-7.9), NURTURE (1-4.9)

Output schema for map tool:

| Field | Type | Description | |-------|------|-------------| | address | string | Full property address | | lead_type | string | Price Reduction / FSBO / Expired / Pre-Foreclosure | | estimated_value | string | Dollar amount | | motivation_score | number | 1-10 | | timeline_score | number | 1-10 | | value_score | number | 1-10 | | engagement_score | number | 1-10 | | competition_score | number | 1-10 | | final_score | number | 1.0-10.0 | | tier | string | HOT / WARM / NURTURE | | outreach_script | string | 3-4 sentence personalized script | | key_signals | string | Comma-separated signals |

Save qualified leads sorted by score to ~/lead_gen_pipeline/daily_leads_qualified.md.

Stage 5: Prospect

Use the map tool to research target agents in parallel.

Identify 10 top-producing agents in the metro by searching for "top real estate agents {METRO} {YEAR}" and collecting names, brokerages, and specialties.

Prompt template for each agent:

Research this real estate agent as a potential customer for an automated
lead generation service that delivers AI-qualified seller leads daily.

Agent: {{input}}

Find: website, email, market focus, estimated annual volume, buying signals
(active marketing, growing team, high volume, awards, thought leadership).

Score purchase likelihood (1-10).
Write a 3-4 sentence outreach email referencing their specific achievements
and offering a free sample report. Service pricing: Starter $149/mo,
Professional $299/mo, Enterprise $599/mo.

Output schema for map tool:

| Field | Type | Description | |-------|------|-------------| | agent_name | string | Full name | | brokerage | string | Company name | | market_focus | string | Borough/neighborhoods | | annual_volume | string | Dollar amount or Unknown | | website | string | URL or Not found | | email | string | Email or Not found | | buying_signals | string | Comma-separated signals | | purchase_likelihood | number | 1-10 | | outreach_draft | string | Personalized email body |

Save to ~/lead_gen_pipeline/agent_prospects.md.

Stage 6: Automate

Schedule recurring tasks using the schedule tool.

Daily lead scan (weekdays 7 AM):

cron: "0 0 7 * * 1-5"
name: "Daily {METRO} Lead Discovery"
prompt: [Run stages 3-4 using files in ~/lead_gen_pipeline/]

The scheduled prompt should instruct Manus to:

  1. Read ICP from ~/lead_gen_pipeline/icp.md
  2. Read email templates from ~/lead_gen_pipeline/email_templates.md
  3. Execute Stage 3 (Discover) for all categories
  4. Execute Stage 4 (Qualify) using map tool
  5. For HOT leads (8+), send alert via Gmail MCP using Template 1
  6. Log activity to ~/lead_gen_pipeline/delivery_log.md

Sending Emails

Use the Gmail MCP server (manus-mcp-cli tool call send_email --server gmail) to deliver leads.

HOT lead alert: Send immediately when a lead scores 8+. Use Template 1 from email templates.

Weekly digest: Compile all qualified leads from the week into a summary table. Use Template 2 from email templates.

Agent outreach: Send personalized prospecting emails to agent prospects. Use Template 3 from email templates.

Always ask user for confirmation before sending emails (confirm_browser_operation).

File Inventory

After full execution, the working directory contains:

| File | Purpose | |------|--------| | icp.md | Ideal Customer Profile and scoring rubric | | email_templates.md | Email templates for alerts, digests, outreach | | stripe_config.md | Stripe product IDs, prices, payment links | | daily_leads_raw.md | Raw discovered leads (updated daily) | | daily_leads_qualified.md | Scored and tiered leads (updated daily) | | agent_prospects.md | Researched agent customers with outreach drafts | | delivery_log.md | Audit log of all deliveries |

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

MCP: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract"
curl -s "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

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Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Viral Content Creation Agent

This agent researches trends, scripts videos, sets up engagement automation, and compiles everything into a shareable document.

MCPagentassistantautomation
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "MCP"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-10-09T02:29:24.710Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "MCP",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "listing",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multiple",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:MCP|unknown|profile capability:listing|supported|profile capability:multiple|supported|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "category": "integration",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "vendor",
    "label": "Vendor",
    "value": "Estherperdana7",
    "category": "vendor",
    "href": "https://github.com/estherperdana7/real-estate-lead-gen",
    "sourceUrl": "https://github.com/estherperdana7/real-estate-lead-gen",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T01:15:14.761Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "MCP",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T01:15:14.761Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  }
]

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