LinkedIn Autopilot — Your Agent Networks 24/7
You sleep. Your LinkedIn thrives.
LinkedIn Autopilot turns your agent into a 24/7 LinkedIn manager. It schedules posts, auto-engages with target accounts, runs personalized DM sequences, and builds your network while you focus on actual work. No more "I should post more" guilt. No more missing engagement windows. No more manual connection request grinding.
What makes it different: This isn't a dumb bot — it's your agent using real browser automation with human-like behavior patterns. Random delays, natural engagement patterns, safety throttling, and intelligent targeting. Multi-day sequences with conditional logic. State tracking across sessions. Full reporting on what worked.
The Pain Points This Solves
❌ "I spend 2 hours/day on LinkedIn and have nothing to show for it"
✅ Your agent handles engagement, DMs, and connection building automatically
❌ "I post inconsistently and my reach is dying"
✅ Scheduled posts with optimal timing — your agent never forgets
❌ "I see opportunities to engage but I'm too busy"
✅ Auto-engage on target accounts' posts with personalized comments
❌ "Follow-up sequences are tedious and I drop leads"
✅ Multi-step DM sequences with conditional logic — your agent follows up
❌ "I want to build my network but connection requests feel spammy"
✅ Targeted connection campaigns with personalized notes and safety limits
Setup
- 1. Run
scripts/setup.sh to initialize config and data directories - Edit
~/.config/linkedin-autopilot/config.json with targets, sequences, and posting schedule - Store LinkedIn credentials in
~/.clawdbot/secrets.env:
LINKEDIN_EMAIL=your-email@example.com
LINKEDIN_PASSWORD=your-password
- 4. Test with: INLINECODE3
Config
Config lives at ~/.config/linkedin-autopilot/config.json. See config.example.json for full schema.
Key sections:
- - identity — Your LinkedIn profile info (for personalization)
- targets — Who/what to engage with (companies, people, keywords)
- posting — Schedule, content queue, optimal times
- engagement — Auto-like/comment rules, target post patterns
- outreach — Connection request campaigns, DM sequences
- safety — Rate limits, delays, warmup period, blackout windows
Scripts
| Script | Purpose |
|---|
| INLINECODE6 | Initialize config and data directories |
| INLINECODE7 |
Post scheduled content from queue |
|
scripts/engage.sh | Auto-engage on target posts (like, comment, share) |
|
scripts/dm-sequence.sh | Manage DM sequences (send, follow-up, track) |
|
scripts/connect.sh | Send connection requests to target profiles |
|
scripts/report.sh | Generate analytics report (engagement, growth, conversions) |
All scripts support --dry-run for testing without actually posting/engaging.
Posting Workflow
Run scripts/post.sh on schedule (cron daily at optimal times). The script:
- 1. Checks posting queue in config
- Verifies timing (respects blackout windows, rate limits)
- Logs into LinkedIn via browser automation
- Posts content with configured formatting
- Tracks post performance
- Updates queue state
Post queue example:
CODEBLOCK1
Engagement Workflow
Run scripts/engage.sh 3-4x daily. The script:
- 1. Searches for posts matching target criteria (keywords, accounts, hashtags)
- Scores relevance (content match, author influence, engagement level)
- Engages with top posts (like, thoughtful comment, or share)
- Tracks engagement to avoid repeats
- Respects rate limits (20-30 engagements per run)
Target patterns:
- - Posts from specific companies/people
- Posts with keywords/hashtags
- Posts in your feed from connections
- Trending posts in your industry
Engagement types:
- - Like: Quick signal, low friction
- Comment: Generated from templates + post context (not spammy)
- Share: With your take/commentary added
DM Sequence Workflow
Run scripts/dm-sequence.sh daily. The script:
- 1. Checks active sequences for people at each stage
- Sends next message in sequence (respects delays)
- Detects replies and advances/pauses accordingly
- Handles conditional branching (replied vs not replied)
- Reports on conversion rates
Sequence example:
CODEBLOCK2
Connection Request Workflow
Run scripts/connect.sh weekly (not daily — LinkedIn limits this). The script:
- 1. Searches for target profiles (job titles, companies, keywords)
- Filters out existing connections and pending requests
- Generates personalized connection notes
- Sends requests with safety throttling (20-30/week max)
- Tracks acceptance rate
Target criteria:
CODEBLOCK3
Safety & Rate Limits
LinkedIn Autopilot follows conservative rate limits to avoid account flags:
| Action | Limit | Timing |
|---|
| Posts | 1-2/day | Optimal hours (9am-11am, 2pm-4pm) |
| Engagements |
80-100/day | Spread across 3-4 runs |
|
Connection Requests | 20-30/week | Gradual warmup over first 2 weeks |
|
DMs | 30-50/day | Random delays 5-15min between sends |
|
Profile Views | 50-80/day | Natural browsing pattern |
Warmup Period: First 2 weeks run at 50% capacity to establish normal behavior pattern.
Blackout Windows: No activity during nights/weekends (configurable).
Random Delays: 3-8 seconds between actions, 5-15 minutes between campaigns.
Human-Like Patterns: Varied engagement times, occasional skips, natural language variance.
State Tracking
All activity is logged and tracked:
CODEBLOCK4
Reporting
INLINECODE17 generates performance reports:
Weekly Summary:
- - Posts published (reach, engagement rate)
- Engagements performed (breakdown by type)
- Connection requests (sent, accepted, pending)
- DM sequences (active, replied, converted)
- Growth metrics (followers, connections, profile views)
Lead Conversion Tracking:
- - DM replies → qualified leads
- Connection acceptances → engaged conversations
- Post engagement → inbound interest
Example Workflows
1. Thought Leader Building
- - Post 1x/day on schedule (industry insights, lessons learned)
- Auto-engage with 20-30 posts daily from influencers in your space
- Share top posts with your commentary
- Track which content types drive the most profile views
2. Outbound Lead Gen
- - Connect with 20-30 target profiles weekly (ICP: CTOs at Series A startups)
- Run DM sequence on new connections (intro → value prop → call booking)
- Auto-engage with prospects' posts before sending sequence
- Report on reply rate and meeting bookings
3. Network Maintenance
- - Like posts from existing connections (stay top of mind)
- Comment thoughtfully on key accounts' updates
- Share relevant content to your feed
- Periodic check-ins via DM (birthday, work anniversary, post milestone)
LinkedIn TOS Compliance
Important: LinkedIn's ToS prohibits automation. This tool is designed for:
- 1. Personal use with human oversight (you review/approve actions)
- Agent-assisted workflows (agent suggests, human approves)
- Batch scheduling (compose in bulk, post on schedule)
Recommended approach:
- - Use
--dry-run mode to preview actions - Review queued posts/messages before enabling auto-send
- Set conservative rate limits
- Monitor for account warnings
- Always have a human in the loop for sensitive actions
This tool is provided as-is for educational purposes. Use responsibly.
Data Files
CODEBLOCK5
Browser Automation
Uses Clawdbot's built-in browser control:
- - Snapshot → Act → Verify pattern
- Handles login, 2FA prompts, session management
- Retries on rate limit detection
- Graceful handling of LinkedIn UI changes
Advanced Features
A/B Testing: Test post variants, measure which performs better
Smart Scheduling: ML-based optimal posting time suggestion
Reply Detection: Pauses DM sequences when prospect replies
Sentiment Analysis: Adjusts engagement strategy based on post sentiment
Network Mapping: Tracks who engages with your content (potential advocates)
Troubleshooting
"LinkedIn security check triggered"
→ Reduce rate limits in config, extend delays, complete security verification manually
"Posts not publishing"
→ Check activity-log.json for errors, verify LinkedIn session still valid
"DM sequences not advancing"
→ Verify reply detection is working, check conversation state in INLINECODE20
"Connection requests rejected frequently"
→ Improve note personalization, target better ICP matches, reduce volume
Contributing
Want to add features? See references/linkedin-api.md for browser automation patterns and references/sequence-engine.md for DM workflow logic.
Remember: Your agent is a force multiplier, not a replacement for authentic networking. Use it to handle the tedious parts so you can focus on the conversations that matter.
LinkedIn Autopilot — 你的智能代理网络,全天候在线
你安心入睡。你的LinkedIn持续成长。
LinkedIn Autopilot将你的智能代理转变为全天候的LinkedIn管理者。它能安排帖子发布、自动与目标账号互动、运行个性化私信序列,并在你专注于实际工作时构建你的社交网络。告别我应该多发帖的内疚感,不再错过互动时机,也无需手动发送好友请求。
独特之处: 这不是一个愚蠢的机器人——它是你的智能代理,使用真实的浏览器自动化,模拟人类行为模式。随机延迟、自然的互动模式、安全限速和智能定位。支持多天序列与条件逻辑,跨会话状态追踪,以及完整的成效报告。
解决的核心痛点
❌ 我每天花2小时在LinkedIn上,却毫无成效
✅ 你的智能代理自动处理互动、私信和连接建立
❌ 我发帖不规律,曝光度持续下降
✅ 按最优时间安排帖子发布——你的智能代理永不遗忘
❌ 我看到互动机会但太忙了
✅ 自动在目标账号的帖子下进行个性化评论互动
❌ 跟进序列繁琐,我丢失了潜在客户
✅ 多步骤私信序列配合条件逻辑——你的智能代理自动跟进
❌ 我想建立社交网络,但好友请求显得像垃圾信息
✅ 定向连接活动,附带个性化备注和安全限制
设置
- 1. 运行 scripts/setup.sh 初始化配置和数据目录
- 编辑 ~/.config/linkedin-autopilot/config.json,设置目标、序列和发帖计划
- 将LinkedIn凭据存储在 ~/.clawdbot/secrets.env:
bash
LINKEDIN_EMAIL=your-email@example.com
LINKEDIN_PASSWORD=your-password
- 4. 使用以下命令测试:scripts/engage.sh --dry-run
配置
配置文件位于 ~/.config/linkedin-autopilot/config.json。完整架构请参见 config.example.json。
关键部分:
- - identity — 你的LinkedIn个人资料信息(用于个性化)
- targets — 互动对象/内容(公司、人物、关键词)
- posting — 发布计划、内容队列、最佳时间
- engagement — 自动点赞/评论规则、目标帖子模式
- outreach — 好友请求活动、私信序列
- safety — 速率限制、延迟、预热期、停用时段
脚本
| 脚本 | 用途 |
|---|
| scripts/setup.sh | 初始化配置和数据目录 |
| scripts/post.sh |
从队列中发布预定内容 |
| scripts/engage.sh | 自动互动目标帖子(点赞、评论、分享) |
| scripts/dm-sequence.sh | 管理私信序列(发送、跟进、追踪) |
| scripts/connect.sh | 向目标个人资料发送好友请求 |
| scripts/report.sh | 生成分析报告(互动、增长、转化) |
所有脚本均支持 --dry-run 参数,用于测试而不实际发布/互动。
发帖工作流程
按计划运行 scripts/post.sh(每天在最佳时间通过cron执行)。该脚本:
- 1. 检查配置中的发帖队列
- 验证时间(遵守停用时段、速率限制)
- 通过浏览器自动化登录LinkedIn
- 按配置格式发布内容
- 追踪帖子表现
- 更新队列状态
发帖队列示例:
json
posts: [
{
content: 构建AI智能代理的5个经验教训:\n\n1. ...,
scheduled_time: 2024-01-28T09:00:00Z,
status: pending,
media: null
}
]
互动工作流程
每天运行 scripts/engage.sh 3-4次。该脚本:
- 1. 搜索符合目标条件的帖子(关键词、账号、话题标签)
- 评分相关性(内容匹配、作者影响力、互动水平)
- 与顶级帖子互动(点赞、有深度的评论或分享)
- 追踪互动以避免重复
- 遵守速率限制(每次运行20-30次互动)
目标模式:
- - 来自特定公司/人物的帖子
- 包含关键词/话题标签的帖子
- 信息流中来自好友的帖子
- 你所在行业的热门帖子
互动类型:
- - 点赞: 快速信号,低摩擦
- 评论: 基于模板+帖子上下文生成(非垃圾信息)
- 分享: 附带你的观点/评论
私信序列工作流程
每天运行 scripts/dm-sequence.sh。该脚本:
- 1. 检查每个阶段活跃序列中的人员
- 发送序列中的下一条消息(遵守延迟)
- 检测回复并相应推进/暂停
- 处理条件分支(已回复 vs 未回复)
- 报告转化率
序列示例:
json
{
name: 咨询介绍,
trigger: new_connection,
steps: [
{
delay_hours: 24,
message: 你好{firstname}!感谢添加好友。我帮助{title}解决{painpoint}问题。你目前在这个领域有相关工作吗?,
condition: null
},
{
delay_hours: 72,
message: 跟进一下——我看到你关于{topic}的帖子。很乐意聊聊{offering}。这周有空快速通话吗?,
condition: no_reply
}
]
}
好友请求工作流程
每周运行 scripts/connect.sh(非每天——LinkedIn对此有限制)。该脚本:
- 1. 搜索目标个人资料(职位、公司、关键词)
- 过滤掉现有好友和待处理请求
- 生成个性化好友备注
- 发送请求并遵守安全限速(每周最多20-30个)
- 追踪接受率
目标条件:
json
connection_targets: [
{
query: AI顾问 OR 自动化专家,
companies: [微软, 谷歌, OpenAI],
exclude_titles: [招聘人员],
notetemplate: 你好{firstname},我正在为{industry}构建AI工具,看到你在{company}的工作。很乐意添加好友!
}
]
安全与速率限制
LinkedIn Autopilot遵循保守的速率限制,以避免账号被标记:
| 操作 | 限制 | 时间 |
|---|
| 帖子 | 每天1-2篇 | 最佳时段(上午9-11点,下午2-4点) |
| 互动 |
每天80-100次 | 分3-4次运行 |
|
好友请求 | 每周20-30个 | 前两周逐步预热 |
|
私信 | 每天30-50条 | 发送间隔随机延迟5-15分钟 |
|
个人资料浏览 | 每天50-80次 | 自然浏览模式 |
预热期: 前两周以50%容量运行,建立正常行为模式。
停用时段: 夜间/周末无活动(可配置)。
随机延迟: 操作间3-8秒,活动间5-15分钟。
类人模式: 多样化的互动时间、偶尔跳过、自然语言变化。
状态追踪
所有活动均被记录和追踪:
~/.config/linkedin-autopilot/
├── config.json # 用户配置
├── posts-queue.json # 预定帖子
├── engagement-history.json # 已互动的帖子(去重)
├── dm-sequences.json # 活跃私信线程
├── connections.json # 好友请求及状态
├── analytics.json # 性能指标
└── activity-log.json # 完整审计追踪
报告
scripts/report.sh 生成性能报告:
每周摘要:
- - 已发布帖子(曝光量、互动率)
- 已执行互动(按类型细分)
- 好友请求(已发送、已接受、待处理)
- 私信序列(活跃、已回复、已转化)
- 增长指标(粉丝、好友、个人资料浏览量)
潜在客户转化追踪:
- - 私信回复 → 合格潜在客户
- 好友接受 → 活跃对话
- 帖子互动 → 入站兴趣
示例工作流程
1. 思想领袖建设
- - 每天按计划发布1篇帖子(行业见解、经验教训)
- 每天自动与20-30篇来自领域内影响者的帖子互动
- 分享顶级帖子并附上你的评论
- 追踪哪些内容类型带来最多个人资料浏览量
2. 外向潜在客户开发