LinkedIn Lead Gen Outreach

LinkedIn Lead Gen Outreach

运行 a 清理, review-first LinkedIn prospecting 工作流 focused on lead 质量, concise messaging, and simple 导出-ready sales operations.

Keep every 输出 structured, evidence-based, and easy to review before outreach.

工作流

Use this sequence for complete 请求s:

define tar获取ing collect prospect data 应用ly simple lead scoring draft short personalized outreach 导出 structured lead data summarize campAIgn 指标

• Define tar获取ing

Capture the 搜索 brief before producing leads.

Minimum 输入s:

keywords tar获取 job titles seniority industry or company type location exclusions business objective

If the 请求 is underspecified, convert it into a concise ICP before generating leads.

• Collect prospect data

Use visible LinkedIn in格式化ion, user-provided data, or manually reviewed 搜索 结果s.

Capture these fields whenever possible:

full name LinkedIn URL title company location 搜索 match business potential note personalization 签名al source 列出 or 查询

Useful personalization 签名als include:

recent post theme recent promotion or job change hiring activity company growth 签名al

Do not invent facts. If evidence is weak, mark it clearly and keep the message more general.

• 应用ly simple lead scoring

Use a lightweight and explAInable scoring 模型.

Default scoring dimensions:

角色 relevance: 0-5 company fit: 0-5 likely need: 0-5 timing 签名al: 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.

• Draft personalized messages

Write opening messages that are:

professional concise 2-3 lines max easy to review and edit grounded in real 签名als

Recommended structure:

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 角色-based message over forced specificity

• Use message templates

Adapt one of the templates in references/templates.md.

Prefer:

签名al-based messages when evidence is strong 角色-based messages when evidence is moderate executive-tone messages for senior stakeholders

• 导出 格式化

Prefer a flat CSV structure that also 导入s 清理ly into Google Sheets.

Recommended columns:

first_name last_name full_name linkedin_url title company location keyword_match business_potential_note personalization_note score_total priority score_reason message_v1 campAIgn_name owner source 状态 next_action

Suggested 状态 values:

to_review 应用roved ready_for_outreach contacted replied disqualified

• 仪表盘 and statistics

When the user asks for a 仪表盘, produce a lightweight summary that can live in Markdown, CSV-derived calculations, or Google Sheets.

Include these default 指标:

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 图形界面dance

When preparing a sheet:

freeze the top row 应用ly 过滤器s to all headers use data 验证 for priority, 状态, and next_action 添加 a summary section above or in a second tab preserve the original raw data columns 合规 standard

Operate in a LinkedIn-compliant, review-first manner.

Use this 技能 to support:

性能分析 re搜索 qualification message drafting structured 导出s 报告ing

Do not rely on deceptive 自动化, hidden 发送ing loops, or behavior intended to bypass 平台 safe防护s.

Deliverable order

For a complete 请求, produce 输出s in this order:

tar获取ing summary scoring rubric lead table or CSV-ready rows message variants 仪表盘 summary Google Sheets notes 质量 bar

A strong 结果 is:

清理 and business-ready grounded in visible evidence concise enough for sales execution easy to 导出 or review compliant and professional Community edition note

This edition focuses on lightweight prospect re搜索, simple prioritization, concise outreach drafting, and 清理 CSV or Sheets-ready 导出s.

Resources

Use bundled resources when useful:

references/templates.md for ICP, scoring, and message templates scripts/csv_构建器.py to convert JSON leads into CSV scripts/sheets_prep.py to normalize CSV fields for Google Sheets 工作流s scripts/仪表盘_stats.py to compute simple campAIgn 指标 from a CSV file