LinkedIn Lead Gen Outreach
Run a clean, review-first LinkedIn prospecting workflow focused on lead quality, concise messaging, and simple export-ready sales operations.
Keep every output structured, evidence-based, and easy to review before outreach.
Workflow
Use this sequence for complete requests:
- 1. define targeting
- collect prospect data
- apply simple lead scoring
- draft short personalized outreach
- export structured lead data
- summarize campaign metrics
1. Define targeting
Capture the search brief before producing leads.
Minimum inputs:
- - keywords
- target job titles
- seniority
- industry or company type
- location
- exclusions
- business objective
If the request is underspecified, convert it into a concise ICP before generating leads.
2. Collect prospect data
Use visible LinkedIn information, user-provided data, or manually reviewed search results.
Capture these fields whenever possible:
- - full name
- LinkedIn URL
- title
- company
- location
- search match
- business potential note
- personalization signal
- source list or query
Useful personalization signals include:
- - recent post theme
- recent promotion or job change
- hiring activity
- company growth signal
Do not invent facts. If evidence is weak, mark it clearly and keep the message more general.
3. Apply simple lead scoring
Use a lightweight and explainable scoring model.
Default scoring dimensions:
- - role relevance: 0-5
- company fit: 0-5
- likely need: 0-5
- timing signal: 0-5
- personalization depth: 0-5
Total score bands:
- - 20-25: high priority
- 12-19: medium priority
- 0-11: low priority
Always include a one-line explanation.
4. Draft personalized messages
Write opening messages that are:
- - professional
- concise
- 2-3 lines max
- easy to review and edit
- grounded in real signals
Recommended structure:
- 1. relevant opener
- business relevance
- soft CTA
Rules:
- - keep messages short and polished
- avoid hype, pressure, or artificial urgency
- avoid unsupported claims
- if personalization is weak, prefer a role-based message over forced specificity
5. Use message templates
Adapt one of the templates in references/templates.md.
Prefer:
- - signal-based messages when evidence is strong
- role-based messages when evidence is moderate
- executive-tone messages for senior stakeholders
6. Export format
Prefer a flat CSV structure that also imports cleanly into Google Sheets.
Recommended columns:
- - firstname
- lastname
- fullname
- linkedinurl
- title
- company
- location
- keywordmatch
- businesspotentialnote
- personalizationnote
- scoretotal
- priority
- scorereason
- messagev1
- campaignname
- owner
- source
- status
- next_action
Suggested status values:
- - toreview
- approved
- readyfor_outreach
- contacted
- replied
- disqualified
7. Dashboard and statistics
When the user asks for a dashboard, produce a lightweight summary that can live in Markdown, CSV-derived calculations, or Google Sheets.
Include these default metrics:
- - total leads
- high / medium / low priority counts
- leads by title
- leads by geography
- personalization coverage
- leads ready for outreach
Keep it simple and executive-friendly.
Google Sheets guidance
When preparing a sheet:
- - freeze the top row
- apply filters to all headers
- use data validation for
priority, status, and INLINECODE3 - add a summary section above or in a second tab
- preserve the original raw data columns
Compliance standard
Operate in a LinkedIn-compliant, review-first manner.
Use this skill to support:
- - profile research
- qualification
- message drafting
- structured exports
- reporting
Do not rely on deceptive automation, hidden sending loops, or behavior intended to bypass platform safeguards.
Deliverable order
For a complete request, produce outputs in this order:
- 1. targeting summary
- scoring rubric
- lead table or CSV-ready rows
- message variants
- dashboard summary
- Google Sheets notes
Quality bar
A strong result is:
- - clean and business-ready
- grounded in visible evidence
- concise enough for sales execution
- easy to export or review
- compliant and professional
Community edition note
This edition focuses on lightweight prospect research, simple prioritization, concise outreach drafting, and clean CSV or Sheets-ready exports.
Resources
Use bundled resources when useful:
- -
references/templates.md for ICP, scoring, and message templates - INLINECODE5 to convert JSON leads into CSV
- INLINECODE6 to normalize CSV fields for Google Sheets workflows
- INLINECODE7 to compute simple campaign metrics from a CSV file
LinkedIn 潜在客户开发外联
运行一个干净、以审核为先的 LinkedIn 潜在客户开发工作流程,专注于潜在客户质量、简洁消息传递以及简单的可导出销售运营。
确保每个输出结构清晰、有据可查,并在外联前易于审核。
工作流程
对于完整请求,请按以下顺序操作:
- 1. 定义目标定位
- 收集潜在客户数据
- 应用简单的潜在客户评分
- 起草简短个性化外联信息
- 导出结构化潜在客户数据
- 汇总活动指标
1. 定义目标定位
在生成潜在客户之前,先捕获搜索简报。
最低输入要求:
- - 关键词
- 目标职位名称
- 资历级别
- 行业或公司类型
- 地理位置
- 排除项
- 业务目标
如果请求信息不足,在生成潜在客户前先将其转化为简洁的理想客户画像(ICP)。
2. 收集潜在客户数据
使用可见的 LinkedIn 信息、用户提供的数据或人工审核的搜索结果。
尽可能捕获以下字段:
- - 全名
- LinkedIn 链接
- 职位名称
- 公司
- 地理位置
- 搜索匹配项
- 商业潜力备注
- 个性化信号
- 来源列表或查询
有用的个性化信号包括:
- - 近期帖子主题
- 近期晋升或工作变动
- 招聘活动
- 公司增长信号
不要编造事实。如果证据不足,请明确标注,并保持信息更加通用。
3. 应用简单的潜在客户评分
使用轻量级且可解释的评分模型。
默认评分维度:
- - 角色相关性:0-5
- 公司匹配度:0-5
- 潜在需求:0-5
- 时机信号:0-5
- 个性化深度:0-5
总分区间:
- - 20-25:高优先级
- 12-19:中优先级
- 0-11:低优先级
始终包含一行解释说明。
4. 起草个性化消息
撰写开场消息时需做到:
- - 专业
- 简洁
- 最多 2-3 行
- 易于审核和编辑
- 基于真实信号
推荐结构:
- 1. 相关开场白
- 业务相关性
- 温和的行动号召
规则:
- - 保持消息简短精炼
- 避免炒作、施压或人为制造紧迫感
- 避免无依据的主张
- 如果个性化信息不足,优先使用基于角色的消息,而非强行具体化
5. 使用消息模板
调整 references/templates.md 中的模板之一。
优先选择:
- - 证据充分时使用基于信号的消息
- 证据一般时使用基于角色的消息
- 面向高级利益相关者时使用高管语气消息
6. 导出格式
优先使用扁平 CSV 结构,同时能干净地导入 Google Sheets。
推荐列:
- - 名字
- 姓氏
- 全名
- LinkedIn 链接
- 职位名称
- 公司
- 地理位置
- 关键词匹配
- 商业潜力备注
- 个性化备注
- 总分
- 优先级
- 评分原因
- 消息版本一
- 活动名称
- 负责人
- 来源
- 状态
- 下一步行动
建议的状态值:
7. 仪表盘与统计
当用户要求仪表盘时,生成一个轻量级摘要,可存在于 Markdown、CSV 衍生计算或 Google Sheets 中。
包含以下默认指标:
- - 潜在客户总数
- 高/中/低优先级数量
- 按职位分类的潜在客户
- 按地域分类的潜在客户
- 个性化覆盖率
- 可外联的潜在客户
保持简洁且对高管友好。
Google Sheets 指南
准备表格时:
- - 冻结首行
- 为所有表头应用筛选器
- 对 优先级、状态 和 下一步行动 使用数据验证
- 在顶部或第二个标签页添加摘要部分
- 保留原始数据列
合规标准
以符合 LinkedIn 规定、以审核为先的方式操作。
使用此技能支持:
不依赖欺骗性自动化、隐藏发送循环或旨在绕过平台保护措施的行为。
交付物顺序
对于完整请求,按以下顺序生成输出:
- 1. 目标定位摘要
- 评分标准
- 潜在客户表格或 CSV 就绪行
- 消息变体
- 仪表盘摘要
- Google Sheets 备注
质量标准
优秀的结果应具备:
- - 干净且业务就绪
- 基于可见证据
- 足够简洁以支持销售执行
- 易于导出或审核
- 合规且专业
社区版说明
本版本专注于轻量级潜在客户研究、简单优先级排序、简洁外联起草以及干净的 CSV 或 Sheets 就绪导出。
资源
在需要时使用捆绑资源:
- - references/templates.md:用于 ICP、评分和消息模板
- scripts/csvbuilder.py:将 JSON 潜在客户转换为 CSV
- scripts/sheetsprep.py:规范化 CSV 字段以适配 Google Sheets 工作流程
- scripts/dashboard_stats.py:从 CSV 文件计算简单的活动指标