Agent skill

Sales Powermap

by LeoYeAI in LeoYeAI/openclaw-master-skills

智能销售助手:从模糊意图出发,发现目标客户、挖掘组织架构、分析决策链路、构建 Power Map 可视化关系图,输出可执行的销售攻单方案。触发词:卖、客户、power map、决策人、组织架构

MITAuto-check passed

Install Sales Powermap

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill sales-powermap -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills sales-powermap --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sales-powermap .claude/skills/sales-powermap && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sales-powermap
GitHub stars
2.2k
Token cost
~3.9k tokens
SKILL.md length
423 words
Files
2
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

智能销售助手:从模糊意图出发,发现目标客户、挖掘组织架构、分析决策链路、构建 Power Map 可视化关系图,输出可执行的销售攻单方案。触发词:卖、客户、power map、决策人、组织架构

  • Works in 6 steps: 意图解析 → 目标公司发现(仅场景A) → 组织架构挖掘 → …
  • SKILL.md covers Overview, 触发条件, 关键原则 and Workflow, plus 2 more sections
  • Reaches linkedin.com

What it does

Sales Powermap is an agent skill from LeoYeAI/openclaw-master-skills. 智能销售助手:从模糊意图出发,发现目标客户、挖掘组织架构、分析决策链路、构建 Power Map 可视化关系图,输出可执行的销售攻单方案。触发词:卖、客户、power map、决策人、组织架构

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).

The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

Example prompts

  • “/sales-powermap”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. 意图解析
  2. 目标公司发现(仅场景A)
  3. 组织架构挖掘
  4. 决策链分析
  5. 联系方式获取
  6. Power Map 生成

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, markdown, python and mermaid).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • linkedin.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Sales Powermap loads about 3.9k tokens when it runs. Until then it costs about 28 tokens; SKILL.md has 423 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~28
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 423 words, ~3,880 tokens.

Download SKILL.mdSave it as .claude/skills/sales-powermap/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
sales-powermap
description
智能销售助手:从模糊意图出发,发现目标客户、挖掘组织架构、分析决策链路、构建 Power Map 可视化关系图,输出可执行的销售攻单方案。触发词:卖、客户、power map、决策人、组织架构

Power Map 智能销售助手

Overview

帮助用户找到目标客户并构建决策者关系图(Power Map)。从用户的模糊输入(如"我想把XX卖出去")出发,自动完成意图解析、目标公司发现、组织架构挖掘、决策链分析、联系方式获取,最终生成可视化的 Power Map 和完整的销售攻单方案。

触发条件

用户输入包含以下模式时激活:

  • "我想把XX卖出去..."
  • "我要卖XX到YY公司..."
  • "帮我找XX的客户..."
  • "帮我画个power map..."
  • "分析XX公司的决策人..."

关键原则

  1. 快速响应:用户一提问就开始行动,不要过多确认
  2. 智能推断:从模糊输入中提取关键信息
  3. 结果导向:最终要给出可执行的联系方案
  4. 可视化优先:Power Map 图是核心产出物

Workflow

Step 1: 意图解析

从用户输入中提取关键信息,判断执行场景。

场景判断

场景A:只有产品,没有目标公司

识别特征:

  • 用户只提到产品/服务,没有提到具体公司
  • 使用"卖出去"、"找客户"、"找买家"等表述

示例输入:

  • "我想把MiniMax Hailuo视频模型卖出去"
  • "我有一个AI客服产品,应该卖给谁?"
  • "帮我找AI视频生成模型的客户"
  • "我想推广我们的企业级SaaS产品"

场景B:有明确的产品和目标公司

识别特征:

  • 用户同时提到产品和目标公司
  • 使用"卖到XX公司"、"找XX公司的人"等表述

示例输入:

  • "我要卖MiniMax Hailuo到Brandtech Group"
  • "帮我分析Nike的AI采购决策人"
  • "Brandtech Group,找AI接入相关的人"
  • "我想把我们的视频模型卖给腾讯"
提取维度

产品信息:

yaml
提取维度:
  product_name: "产品名称"
  product_type: "产品类型(SaaS/硬件/服务等)"
  product_category: "产品类别(AI/营销/金融等)"
  key_features: ["核心特性1", "核心特性2"]
  target_use_case: "目标使用场景"

提取示例:

输入:"我想把MiniMax Hailuo视频模型卖出去"

提取:
  product_name: "MiniMax Hailuo"
  product_type: "AI模型/API服务"
  product_category: "AI视频生成"
  key_features: ["文生视频", "AI生成", "视频内容创作"]
  target_use_case: "内容创作、广告制作、营销素材"

目标公司(如有):

yaml
提取维度:
  company_name: "公司名称"
  company_aliases: ["别名1", "别名2"]
  industry_hint: "行业提示(从上下文推断)"

关联领域: 根据产品特性,推断与目标公司的关联点:

yaml
focus_area: "AI视频生成接入"
decision_makers_hint:
  - "CTO / VP Engineering"
  - "Head of AI"
  - "VP Product"
  - "Creative Director"
输出格式
yaml
intent_analysis:
  scenario: "A"  # 或 "B"
  confidence: 0.95

  product:
    name: "MiniMax Hailuo"
    type: "AI模型/API服务"
    category: "AI视频生成"
    features:
      - "文生视频"
      - "高质量AI生成"
      - "API接入"
    use_cases:
      - "广告视频制作"
      - "营销内容创作"
      - "社交媒体内容"

  target_company:
    name: "Brandtech Group"  # 场景A: null
    aliases: ["Brandtech"]
    industry: "广告科技/MarTech"

  focus_area: "AI视频生成模型接入"

  target_personas:
    - title: "CTO"
      relevance: "技术采购决策"
    - title: "Head of AI"
      relevance: "AI战略负责人"
    - title: "VP Product"
      relevance: "产品集成决策"
    - title: "Creative Director"
      relevance: "创意工具采购"

  next_step: "target-finder"  # 场景A
  # next_step: "org-miner"    # 场景B
场景A输出示例
📋 意图分析完成

产品:MiniMax Hailuo AI视频生成模型
类型:AI模型/API服务
应用场景:广告视频制作、营销内容创作

⚠️ 未检测到明确目标公司

正在为你搜索潜在目标客户...
场景B输出示例
📋 意图分析完成

产品:MiniMax Hailuo AI视频生成模型
目标公司:Brandtech Group
关联领域:AI视频生成接入

需要找的决策人类型:
  ✓ CTO / VP Engineering
  ✓ Head of AI
  ✓ VP Product
  ✓ Creative Director

正在搜索 Brandtech Group 的组织架构...
边界情况处理
情况处理方式
产品描述不清询问用户补充产品信息
公司名拼写错误尝试纠正并确认
多个目标公司列出并让用户选择优先级
非B2B产品提示本工具主要用于B2B销售场景

使用的工具:无(纯语义理解和推理)


Step 2: 目标公司发现(仅场景A)

如果用户没有明确目标公司,分析产品特性并联网搜索潜在客户。

2.1 分析目标客户画像

根据产品类型推断目标客户:

yaml
target_customer_profile:
  industries:
    - "广告/营销科技公司"
    - "MCN/内容创作平台"
    - "品牌方"
    - "影视/游戏公司"
    - "电商平台"

  company_characteristics:
    - "已有AI/技术投入"
    - "内容生产需求大"
    - "追求效率提升"
    - "有创新文化"

  decision_maker_types:
    - "CTO / VP Engineering"
    - "Head of AI"
    - "Creative Director"
    - "VP Marketing"
2.2 联网搜索潜在公司

执行多轮搜索:

搜索1: 行业领先者
  "[产品类别] customers"
  "[产品类别] enterprise clients"
  "companies using [产品类别]"

搜索2: 竞品客户
  "[竞品名] customers case studies"
  "[竞品名] partners"

搜索3: 行业新闻
  "[行业] AI adoption 2024 2025"
  "brands using generative AI [产品类别]"

搜索4: 按行业搜索
  "top [行业] AI"
  "[行业] companies AI transformation"
2.3 筛选和评估

对搜索到的公司进行评估:

维度权重评估标准
需求匹配度30%是否有明确的产品使用场景
公司规模20%是否有采购能力和预算
AI成熟度20%是否已有AI投入,易于接受新技术
可触达性15%决策人是否容易找到
竞争情况15%是否已在用竞品
不同产品类型的搜索策略
产品类型搜索关键词目标行业
AI视频生成video generation, AI content creation广告、媒体、品牌
AI客服customer service automation, chatbot电商、金融、SaaS
BI工具business intelligence, data analytics企业、金融、零售
营销自动化marketing automation, lead generationB2B、SaaS、电商
2.4 输出推荐列表
markdown
📋 发现了以下潜在目标公司

基于你的产品【{产品名}】,推荐以下目标:

---

### 🏢 广告/营销科技公司(推荐优先)
有大量视频内容生产需求,AI投入积极

| # | 公司 | 优先级 | 推荐理由 |
|---|------|--------|----------|
| 1 | Brandtech Group | 🔥 P0 | 全球领先MarTech,已有AI平台Pencil,AI战略激进 |
| 2 | WPP | ⭐ P1 | 全球最大广告集团,积极探索AI转型 |
| 3 | Publicis Groupe | ⭐ P1 | 大型广告集团,数字化转型中 |

---

### 🏢 内容平台
大规模内容生产需求

| # | 公司 | 优先级 | 推荐理由 |
|---|------|--------|----------|
| 4 | Netflix | ⭐ P1 | 内容生产规模大,技术投入高 |
| 5 | TikTok/ByteDance | ⭐ P1 | 短视频平台,创作工具需求 |

---

### 🏢 品牌方
营销视频内容需求大

| # | 公司 | 优先级 | 推荐理由 |
|---|------|--------|----------|
| 6 | Nike | 📌 P2 | 数字营销领先品牌 |
| 7 | Coca-Cola | 📌 P2 | 全球营销投入大 |

---

🎯 **推荐首选**:{最佳目标公司}({推荐理由})

请选择要深入分析的公司:
- 输入序号(如:1)
- 输入公司名(如:Brandtech Group)
- 输入"全部"分析 Top 3

使用的工具:

  • batch_web_search - 批量搜索潜在客户
  • extract_content_from_websites - 提取公司信息

Step 3: 组织架构挖掘

确定目标公司后,深度搜索公司组织架构和关键人物。

3.1 公司基础信息搜索
搜索目标:
  - 公司官网
  - LinkedIn公司页面
  - Wikipedia/Crunchbase

提取信息:
  - 公司简介
  - 总部位置
  - 员工规模
  - 子公司/产品线
3.2 Leadership 团队搜索
搜索语法:
  - "[公司名] leadership team"
  - "[公司名] executive team"
  - "[公司名] management team 2024 2025"
  - "site:[公司官网] about leadership"

搜索平台:
  - 公司官网 About/Team 页面
  - LinkedIn
  - 新闻报道
3.3 关联领域负责人搜索

根据产品关联领域,定向搜索:

AI相关:
  - "[公司名] CTO"
  - "[公司名] Chief Technology Officer"
  - "[公司名] Head of AI"
  - "[公司名] VP Engineering"
  - "site:linkedin.com [公司名] AI"

产品相关:
  - "[公司名] VP Product"
  - "[公司名] Chief Product Officer"
  - "[公司名] Head of Product"

创意/内容相关:
  - "[公司名] Creative Director"
  - "[公司名] Head of Creative"
  - "[公司名] Chief Creative Officer"
3.4 子公司/产品线负责人

如果公司有相关子公司或产品线:

示例(Brandtech → Pencil):
  - "Pencil AI CEO"
  - "Pencil leadership team"
  - "[子公司名] founder"
3.5 对外沟通人员(切入点)
搜索语法:
  - "[公司名] Director of Communications"
  - "[公司名] PR contact"
  - "[公司名] Head of Marketing"
3.6 新闻/采访验证

通过新闻验证人物角色和重要性:

搜索语法:
  - "[人名] [公司名] interview"
  - "[人名] [公司名] announcement"
  - "[公司名] AI partnership 2024"
质量标准
  • 找到CEO/Founder
  • 找到与关联领域直接相关的负责人
  • 每个人有LinkedIn链接
  • 验证人员当前在职
  • 记录信息来源
  • 找到至少一个切入点

使用的工具:

  • batch_web_search - 多轮搜索
  • extract_content_from_websites - 提取网页内容
  • twitter_get_user_info - 获取Twitter信息(可选)

Step 4: 决策链分析

将挖掘到的人物进行分层、分析汇报关系、识别最佳切入点、设计包抄策略。

4.1 人物层级分类
层级标识典型职位作用
🔴 最终决策层难度最高CEO, Founder, President最终拍板,一票否决
🟠 战略决策层难度高CSO, CFO, COO战略审批,预算控制
🟡 关键影响者⭐推荐突破CTO, VP Product, Head of AI技术评估,采购推动
🟢 切入点最易接触PR, Communications建立初步联系
分类规则
python
def classify_person(person):
    title = person.title.lower()

    # 🔴 最终决策层
    if any(keyword in title for keyword in ['ceo', 'founder', 'president', 'owner']):
        return 'final_decision'

    # 🟠 战略决策层
    if any(keyword in title for keyword in ['cfo', 'cso', 'coo', 'chief strategy', 'chief financial']):
        return 'strategic'

    # 🟡 关键影响者
    if any(keyword in title for keyword in ['cto', 'vp', 'head of', 'director of', 'chief technology', 'chief product']):
        if is_relevant_to_focus_area(person, focus_area):
            return 'key_influencer'

    # 🟢 切入点
    if any(keyword in title for keyword in ['communications', 'pr', 'public relations', 'marketing manager']):
        return 'entry_point'

    return 'other'
4.2 汇报关系推断

根据以下信息推断汇报关系:

  1. 职位层级:VP → C-Level
  2. 部门归属:Head of AI → CTO 或 CPO
  3. 子公司关系:子公司 CEO → 母公司 CEO
  4. 公开信息:新闻、LinkedIn描述
4.3 识别关键突破点

评估每个人的突破价值:

维度权重说明
领域相关性40%与产品关联领域的直接相关程度
决策权限30%是否有技术采购或合作决策权
可触达性20%是否容易建立联系
向上影响力10%能否影响最终决策者

推荐标记规则:

  • 领域高度相关 + 有决策权 → ⭐ 推荐突破
  • 可触达性高 + 能引荐 → 作为切入点
Show full SKILL.md (167 more words)Show less
4.4 设计包抄策略

设计主路径和备选路径:

yaml
strategy:
  primary_path:
    name: "标准路径"
    steps:
      - target: "{切入点人物}"
        layer: "entry_point"
        action: "通过PR渠道建立初步联系"
        goal: "获取内部会议/活动机会"

      - target: "{技术守门人}"
        layer: "key_influencer"
        action: "技术演示,获得技术背书"
        goal: "通过技术守门人评估"

      - target: "{AI平台/产品负责人}"
        layer: "key_influencer"
        action: "业务合作讨论"
        goal: "推动平台集成"

      - target: "{CEO/Founder}"
        layer: "final_decision"
        action: "战略合作确认"
        goal: "最终审批"

  alternative_paths:
    - name: "合作伙伴引荐"
      description: "通过现有合作伙伴引荐"
      suitable_when: "有共同合作伙伴"

    - name: "行业活动接触"
      description: "在行业活动中接触关键人物"
      suitable_when: "有参加同类活动的机会"

    - name: "投资人/董事会引荐"
      description: "通过共同投资人引荐"
      suitable_when: "有共同投资背景"

使用的工具:无(纯推理分析)


Step 5: 联系方式获取

补全每个关键人物的联系方式。

5.1 LinkedIn URL 确认
搜索语法:
  - "[姓名] [公司名] LinkedIn"
  - "site:linkedin.com/in [姓名] [公司名]"
  - "[姓名] [职位] LinkedIn"

验证要点:
  - 公司名匹配
  - 职位匹配
  - 账号活跃(有近期动态)

输出格式:
  - 完整URL: https://linkedin.com/in/username
5.2 Twitter/X 账号搜索
搜索语法:
  - "[姓名] [公司名] Twitter"
  - "[姓名] @"
  - 从LinkedIn页面查找Twitter链接

验证要点:
  - 确认是正确的人
  - 账号活跃
5.3 Email 搜索

方法优先级:

  1. 公司官网查找 — About/Team 页面、Press/Contact 页面
  2. Email格式推断
    常见格式:
    - firstname@company.com
    - firstname.lastname@company.com
    - f.lastname@company.com
  3. 搜索验证 — "[姓名] [公司] email" / "[姓名]@[公司域名]"
  4. 工具辅助 — Hunter.io、Apollo.io
5.4 其他联系方式

根据人物类型搜索:

  • 技术人员:GitHub, Medium, 个人博客
  • 创意人员:Behance, Dribbble
  • 高管:公开演讲、播客出演
5.5 最佳联系方式推荐

为每个人推荐最佳联系方式:

yaml
contact_recommendation:
  - name: "Will Hanschell"
    best_channel: "LinkedIn InMail"
    reason: "CEO级别,LinkedIn最专业"
    alternative: "行业活动接触"

  - name: "Rachel Barnes"
    best_channel: "LinkedIn + Twitter"
    reason: "PR角色,社交媒体活跃"
    alternative: "公司PR邮箱"

使用的工具:

  • batch_web_search - 搜索联系方式
  • extract_content_from_websites - 提取网页信息
  • twitter_get_user_info - 获取Twitter信息

Step 6: Power Map 生成

整合所有信息,生成可视化输出和完整报告。

6.1 生成 Mermaid 图表

使用 render_mermaid 工具,按以下模板生成决策链关系图:

mermaid
graph TD
    subgraph "🔴 最终决策层"
        A["David Jones<br/>Founder & CEO<br/>🔗 linkedin.com/in/davidjonesoyw<br/>🐦 @DavidJonesOYW"]
        B["Matthieu Bucaille<br/>CFO"]
    end

    subgraph "🟠 战略决策层"
        C["Angela Tangas<br/>Oliver CEO + CSO"]
    end

    subgraph "🟡 关键影响者 ⭐"
        D["Will Hanschell ⭐<br/>Pencil CEO<br/>🔗 linkedin.com/in/willhanschell"]
        E["Rebecca Sykes ⭐<br/>Head of Emerging Tech<br/>🔗 linkedin.com/in/rebeccalsykes"]
        F["Sumukh Avadhani<br/>Pencil CTO"]
        G["James Dow<br/>Gen AI Creative Dir"]
    end

    subgraph "🟢 切入点"
        H["Rachel Barnes<br/>Dir. Communications<br/>🔗 linkedin.com/in/rachelbarnes1<br/>🐦 @rachelmrbarnes"]
    end

    H --> E
    E --> D
    D --> C
    C --> A
    F --> D
    G --> D
    B --> A

    style A fill:#ff6b6b,color:#fff
    style B fill:#ff6b6b,color:#fff
    style C fill:#ffa94d,color:#fff
    style D fill:#ffd43b,color:#000
    style E fill:#ffd43b,color:#000
    style F fill:#ffd43b,color:#000
    style G fill:#ffd43b,color:#000
    style H fill:#69db7c,color:#000
层级颜色参考
层级颜色代码文字颜色
🔴 最终决策层#ff6b6b#fff
🟠 战略决策层#ffa94d#fff
🟡 关键影响者#ffd43b#000
🟢 切入点#69db7c#000
6.2 生成 Power Map 信息图

使用 gen_images 工具生成专业信息图。

Prompt 模板:

Create a professional Power Map infographic for B2B sales strategy.

Design requirements:
- Style: Modern business, dark blue primary color
- Layout: Pyramid/ladder structure, bottom to top = easy to hard contact
- Size: 1920x1080px, suitable for sharing

Content structure (top to bottom):

TOP LAYER - Red (#ff6b6b): Final Decision Makers
- {人物1} | {职位} | {LinkedIn} | {Twitter}

SECOND LAYER - Orange (#ffa94d): Strategic Decision
- {人物} | {职位}

THIRD LAYER - Yellow (#ffd43b): Key Influencers ⭐ (Recommended Breakthrough)
- {人物1} ⭐ | {职位} | {LinkedIn}
- {人物2} ⭐ | {职位} | {LinkedIn}

BOTTOM LAYER - Green (#69db7c): Entry Points
- {人物} | {职位} | {LinkedIn} | {Twitter}

Right side:
- Recommended path arrows: {切入点} → {影响者} → {决策者}
- Difficulty indicator (Low → High)

Bottom:
- Key partners: {合作伙伴列表}
- Company: {公司名}
- Focus: {关联领域}
6.3 生成完整报告

使用的工具:

  • render_mermaid - 渲染决策链图表
  • gen_images - 生成 Power Map 信息图
  • convert - 格式转换(PNG → PDF)
  • bash - 文件操作

输出格式

场景A 输出(找客户 + Power Map)
markdown
# 销售方案:{产品名}

## 📊 目标客户分析

基于【{产品名}】的特点,推荐以下目标客户:

| 优先级 | 公司 | 行业 | 推荐理由 |
|--------|------|------|----------|
| 🔥 P1 | Brandtech Group | MarTech | AI战略激进,有现有AI平台 |
| ⭐ P2 | WPP | 广告 | 规模大,探索AI转型 |
| ... | ... | ... | ... |

---

## 🗺️ Power Map: {选中的公司}

[Mermaid 图表]

[PNG 信息图]

### 关键人物

[按层级的人物表格,含联系方式]

### 🎯 包抄策略

[推荐路径和执行要点]

---

## 📋 下一步行动

1. 🔥 优先联系:[最佳切入人物] - [LinkedIn链接]
2. 准备材料:针对 [关键影响者] 的技术演示
3. 关注活动:[相关行业活动]
场景B 输出(直接 Power Map)
markdown
# {公司名} Power Map - {关联领域}

## 🗺️ 决策者关系图

[Mermaid 图表]

[PNG 信息图]

---

## 📋 关键人物分析

### 🔴 最终决策层(难度最高)

| 人物 | 职位 | 角色定位 | LinkedIn | Twitter |
|------|------|----------|----------|---------|
| {姓名} | {职位} | {角色} | [链接](...) | @... |

### 🟠 战略决策层(难度高)

| 人物 | 职位 | 角色定位 | LinkedIn |
|------|------|----------|----------|
| {姓名} | {职位} | {角色} | [链接](...) |

### 🟡 关键影响者(⭐推荐重点突破)

| 人物 | 职位 | 角色定位 | LinkedIn | 为什么推荐 |
|------|------|----------|----------|-----------|
| {姓名} ⭐ | {职位} | {角色} | [链接](...) | {推荐理由} |

### 🟢 切入点(最易接触)

| 人物 | 职位 | 角色定位 | LinkedIn | Twitter |
|------|------|----------|----------|---------|
| {姓名} | {职位} | {角色} | [链接](...) | @... |

---

## 🎯 包抄策略

### 推荐路径

{切入点} → {技术守门人} → {核心决策人} → {最终决策者} ↓ ↓ ↓ ↓ 建立联系 技术验证背书 采购推动 最终审批


### 执行要点

1. **Step 1: 接触 {切入点}**
   - 渠道:{最佳联系渠道}
   - 目标:建立初步联系,获取内部机会
   - 话术建议:以行业话题切入,不要直接推销

2. **Step 2: 争取 {技术守门人} 技术背书**
   - 渠道:LinkedIn InMail
   - 目标:获得技术评估机会
   - 准备:技术演示、合规说明

3. **Step 3: 推动 {核心决策人} 业务合作**
   - 渠道:LinkedIn InMail / 正式会议
   - 目标:探讨平台集成可能性
   - 准备:ROI 案例、竞品对比

4. **Step 4: 最终获得 {最终决策者} 审批**
   - 渠道:通过内部引荐 / 战略合作提案
   - 目标:最终决策
   - 准备:战略价值提案

### 备选策略

| 策略 | 说明 | 适用场景 |
|------|------|----------|
| 合作伙伴引荐 | 通过共同合作伙伴引荐 | 有共同合作伙伴 |
| 行业活动接触 | 在行业活动中接触 | 有参加机会 |
| 投资人引荐 | 通过共同投资人引荐 | 有共同投资背景 |

---

## ⚠️ 注意事项

[公司特有的注意事项,如已有合作伙伴、前任高管变动、公司文化特点等]

---

## 📞 联系方式汇总

| 层级 | 姓名 | 职位 | LinkedIn | Twitter | Email |
|------|------|------|----------|---------|-------|
| 🔴 | ... | ... | ... | ... | ... |
| 🟠 | ... | ... | ... | ... | ... |
| 🟡⭐ | ... | ... | ... | ... | ... |
| 🟢 | ... | ... | ... | ... | ... |

---

## 📁 输出文件

| 文件类型 | 说明 |
|----------|------|
| Power Map PNG | 可分享的信息图 |
| Power Map PDF | 可打印的报告 |
| Mermaid 源码 | 可编辑的流程图 |

---

## 🚀 下一步行动

1. **今天**:发送 LinkedIn Connection Request 给 {切入点人物}
2. **本周**:准备针对 {技术守门人} 的技术演示材料
3. **关注**:{相关行业活动},寻找接触机会

---

*报告生成时间:{完整时间戳}*
*工具:Power Map 智能销售助手*

工具依赖汇总

工具用途使用步骤
batch_web_search搜索目标公司、组织架构、联系方式Step 2, 3, 5
extract_content_from_websites提取网页内容(公司官网、LinkedIn等)Step 2, 3, 5
twitter_get_user_info获取Twitter账号信息Step 3, 5(可选)
render_mermaid渲染决策链关系图Step 6
gen_images生成 Power Map 信息图Step 6
convertPNG 转 PDFStep 6(可选)

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/sales-powermap of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Sales Powermap next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Sales Powermap compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Sales Powermap this skillLeoYeAI/openclaw-master-skills2.2k—~3.9kAutomated safety check: PassMIT
Power Mapitseffi/agentic-os114—~474Automated safety check: PassMIT
Token Mapnexu-io/open-design100k—~1.4kAutomated safety check: PassApache-2.0
Maps Geographyasgeirtj/system_prompts_leaks69k—~717Automated safety check: PassCC0-1.0
Stakeholder Mapphuryn/pm-skills27k—~625Automated safety check: PassMIT
Feature Maponyx-dot-app/onyx32k—~459Automated safety check: PassCustom licence

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Questions about Sales Powermap

What does Sales Powermap do?

智能销售助手:从模糊意图出发,发现目标客户、挖掘组织架构、分析决策链路、构建 Power Map 可视化关系图,输出可执行的销售攻单方案。触发词:卖、客户、power map、决策人、组织架构. Sales Powermap is an agent skill from LeoYeAI/openclaw-master-skills.

How do I install Sales Powermap in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill sales-powermap -a claude-code`. Or copy the skill folder (skills/sales-powermap in LeoYeAI/openclaw-master-skills) into .claude/skills/sales-powermap in your project. Claude Code loads it when a task matches its description.

How do I install Sales Powermap in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill sales-powermap -a codex`. Or copy the skill folder (skills/sales-powermap in LeoYeAI/openclaw-master-skills) into .agents/skills/sales-powermap in your project. Codex loads it when a task matches its description.

Can I use Sales Powermap in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill sales-powermap -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sales-powermap, .gemini/skills/sales-powermap, .github/skills/sales-powermap and .opencode/skills/sales-powermap in your project.

What does Sales Powermap need to run?

SKILL.md names no scripts, command-line tools or credentials: Sales Powermap is instructions for the agent only. Our summary lists: Python 3.

Does Sales Powermap access the network?

SKILL.md names 1 domain. In commands or code: linkedin.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Sales Powermap safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Sales Powermap use?

Sales Powermap is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sales Powermap use?

About 3.9k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Sales Powermap?

Skills that share tags, products or a category with Sales Powermap: Power Map (itseffi/agentic-os, 114 stars), Token Map (nexu-io/open-design, 100k stars), Maps Geography (asgeirtj/system_prompts_leaks, 69k stars) and Stakeholder Map (phuryn/pm-skills, 27k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sales Powermap?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.