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icp-modelerICP 建模器

Agent Skill

icp-modeler 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:icp-modeler(ICP 建模器)
来源仓库:https://github.com/drivenautoplex1/icp-modeler
安装命令:
openclaw skills install icp-modeler
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install icp-modeler

简介

为抵押贷款和房地产产品设计理想客户档案,提供买家角色与广告定位参数。

  • 适用于市场研究与精准广告投放场景,支持 Meta 和 Google 平台定向优化。
  • 通过关键词或任务需求触发,返回结构化用户画像与定位建议。
  • 安装前需确认权限范围、维护状态及是否涉及联网或文件操作。
  • 建议结合原始 README 核验具体用法与数据源准确性。

SKILL.md

name
icp-modeler
description
Generate research-backed Ideal Customer Profiles (ICPs) for mortgage and real estate products. Returns full buyer personas, Meta/Google ad targeting parameters, trigger phrases, content tone, and platform routing — no generic demographics, only actionable targeting intelligence.
version
1.0.2
author
drivenautoplex1
price
0
tags
metadata
openclaw
requires
env
anyBins
primaryEnv
ANTHROPIC_API_KEY
emoji
🎯
homepage
https://github.com/drivenautoplex1/openclaw-skills
install
package
anthropic
bins
[]

ICP Modeler Skill

Know exactly who you're talking to before you write a single word of copy. The ICP Modeler generates battle-tested buyer profiles for mortgage and real estate products — with specific ad targeting parameters, trigger phrases, pain points, and platform routing built in.

Free vs Premium

Free tier (no API key needed):

  • --demo — full crypto-mortgage ICP profile, zero API calls, shows complete output format
  • --list — see all 5 available ICPs
  • --product <name> — display any pre-built ICP profile (works offline)
  • --output json — export any ICP as structured JSON for your own workflows

Premium tier (ANTHROPIC_API_KEY):

  • --generate-content "3 facebook posts" — LLM writes content specifically tuned to the ICP's triggers, tone, and platform presence
  • --generate-content "30s video script" — ICP-targeted video script
  • --generate-content "email subject lines" — subject lines optimized for this buyer's psychology
  • Unlimited content generation via Claude Haiku (~$0.001 per call)

The pre-built profiles alone are worth installing — most "targeting" is just age/income. This gives you psychology.

What this skill does

For each product, generates a complete buyer intelligence package:

  1. Full buyer persona — age, income, location, occupation, pain points, dream outcome
  2. Trigger phrases — exact words and phrases this buyer types into Google and says out loud
  3. Content tone guide — how to speak to this buyer without triggering skepticism
  4. Platform routing — where this ICP actually hangs out (X, Reddit, LinkedIn, Facebook groups)
  5. Meta ad targeting — ages, interests, placements, custom audience strategy
  6. Google Ads targeting — keywords, match types, negative keywords, audience layers

Available ICPs

ProductHeadline
crypto-mortgageThe Crypto Holder Who Won't Sell
credit-repairThe Almost-Ready Buyer
va-loanThe Veteran Who Doesn't Know What They Have
realtor-partnerThe Agent Who Needs a Lender They Can Trust
first-time-buyerThe Overwhelmed First-Timer

Aliases work: crypto, va, credit, realtor, first-time

Usage

# See the crypto-mortgage ICP with zero setup
python3 icp_modeler.py --demo

# List all available ICPs
python3 icp_modeler.py --list

# Pull a full ICP profile
python3 icp_modeler.py --product "crypto mortgage"
python3 icp_modeler.py --product va-loan
python3 icp_modeler.py --product first-time-buyer

# Export as JSON (pipe into other tools)
python3 icp_modeler.py --product credit-repair --output json

# Generate ICP-tuned content (requires ANTHROPIC_API_KEY)
python3 icp_modeler.py --product crypto --generate-content "3 facebook posts"
python3 icp_modeler.py --product va-loan --generate-content "30s video script"
python3 icp_modeler.py --product first-time --generate-content "email subject lines"

# Version
python3 icp_modeler.py --version

Example output (crypto-mortgage ICP)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
ICP: Crypto-Backed Mortgage (Fannie Mae / Coinbase / Better)
     "The Crypto Holder Who Won't Sell"
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

DEMOGRAPHICS
  age                  30-48
  income               $120K-$400K household
  location             Target metro, key submarkets and growth corridors
  occupation           Software engineer, finance, entrepreneur, executive

PAIN POINTS
  • Doesn't want to sell crypto and trigger $50K-$500K capital gains tax event
  • Doesn't qualify for traditional mortgage because crypto income isn't W2
  • Feels stuck — 'I have the wealth but can't access it for real estate'

TRIGGER PHRASES
  "don't sell your crypto to buy a house"
  "pledge crypto as collateral"
  "no capital gains event"
  "Fannie Mae crypto"

META AD TARGETING
  Ages:      28-50
  Interests: Cryptocurrency, Bitcoin, Ethereum, XRP, DeFi
  Placement: Facebook Feed, Instagram Feed, Instagram Stories
  Income:    Top 25%

Connecting to other skills

Pipe ICP JSON into the content scorer and content calendar:

# Generate ICP → score the content it suggests → build a calendar
python3 icp_modeler.py --product crypto --output json > icp.json
python3 icp_modeler.py --product crypto --generate-content "5 linkedin posts" | \
  python3 ../content-scorer/score_content.py --stdin

Multi-vertical use

The ICP framework applies to:

  • Any mortgage vertical (FHA, VA, USDA, jumbo, crypto-backed, HELOC)
  • Real estate agents targeting specific buyer profiles
  • Credit repair services targeting pre-approval candidates
  • Financial services with segmented buyer journeys
  • Coaching/consulting with defined client personas

Extend by adding your own ICP dict to the ICPS dictionary in icp_modeler.py.

适合场景

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用户想查找某类 Agent Skill 时

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能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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