LinkedIn Autopilot

Your agent builds your LinkedIn presence while you sleep. Schedule posts, auto-engage with target accounts, run personalized DM sequences, and never miss an engagement opportunity. Handles connection requests, profile visiting campaigns, post engagement, and follow-up sequences with safety throttling and human-like behavior patterns. Configure your targets, define engagement rules, and let your agent network 24/7. Use when setting up LinkedIn automation, managing posting schedules, running engagement campaigns, or building agent-driven LinkedIn lead generation workflows.

Security Scan

The skill's files, env requirements, and runtime instructions are consistent with a LinkedIn browser-automation tool, with no evidence of unrelated credentials, remote downloads, or hidden endpoints — but there are a few small inconsistencies and operational risks you should consider before installing.

Purpose & Capability

The name/description (LinkedIn automation) matches the delivered artifacts: shell scripts that perform browser automation, a config example for targeting/posts/sequences, and explicit instructions to provide LinkedIn credentials. Requested env vars (LINKEDIN_EMAIL, LINKEDIN_PASSWORD) are appropriate for browser logins.

Instruction Scope

SKILL.md and the scripts instruct the agent to read/write user config and state under $HOME (e.g., ~/.config/linkedin-autopilot and ~/.clawdbot/secrets.env) and to perform browser actions (login, send DMs, send connection requests). That is expected for this purpose, but note the guidance to store plain credentials in a secrets.env file and the use of platform-specific 'clawd' browser/message actions — these are powerful (they drive your account) and should be used only with credentials you trust to automate.

Install Mechanism

No install spec or external downloads. All code is included as shell scripts. No URLs, package installs, or extracted archives are present, so installation risk is low.

Credentials

Only LINKEDIN_EMAIL and LINKEDIN_PASSWORD are required, which is proportionate. Small inconsistency: SKILL.md/metadata declare env vars, but setup instructions point users to put credentials in ~/.clawdbot/secrets.env rather than describing how that file is loaded into environment — the scripts expect environment variables at runtime. No other unrelated credentials are requested.

Persistence & Privilege

The skill persists state under ~/.config/linkedin-autopilot and suggests storing credentials in ~/.clawdbot/secrets.env. It does not request always:true and does not modify other skills. Allowing autonomous invocation (default) means the agent can run these scripts using your credentials — this is normal for skills but increases operational risk (account actions while unattended).

Assessment

This package is coherent with its stated purpose, but take these precautions before installing: - Credential handling: The skill requires your LinkedIn email and password. Prefer storing them in a secure secret store and ensure ~/.clawdbot/secrets.env (if used) has tight permissions (chmod 600). Understand that providing a password allows the skill to log in and act as you. - Account risk: Automated actions (DMs, connection requests, mass engagement) can trigger LinkedIn security checks or ToS violations and may result in account restrictions. Start in dry-run mode, use low rate limits, and test with a low-risk account first. - 2FA and session handling: The config mentions 2FA handling as 'manual_prompt' — be prepared to handle prompts. Confirm how the platform will surface 2FA challenges. - Reporting channels: The example reporting channel is 'telegram' but no Telegram credentials are provided; configure reporting destinations securely if you enable alerts. - Autonomy: The skill can be invoked autonomously by the agent (normal default). If you do not want the agent to run unsupervised, disable autonomous invocation or restrict when it can run. - Review and sandbox: Scripts are mostly placeholders that call platform-specific browser actions ('clawd browser ...'). Review any real automation commands that would be added, and consider running in a controlled sandbox or with a test LinkedIn account first. If you want further assurance, provide details on how credentials are loaded (how secrets.env is ingested into environment) or request that the skill be adapted to use a safer auth flow (OAuth or ephemeral sessions) if available.

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
2. Edit ~/.config/linkedin-autopilot/config.json with targets, sequences, and posting schedule
3. Store LinkedIn credentials in ~/.clawdbot/secrets.env:

   LINKEDIN_EMAIL=[e-mail]
   LINKEDIN_PASSWORD=your-password
   

4. Test with: scripts/engage.sh --dry-run

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
scripts/setup.sh	Initialize config and data directories
scripts/post.sh	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
2. Verifies timing (respects blackout windows, rate limits)
3. Logs into LinkedIn via browser automation
4. Posts content with configured formatting
5. Tracks post performance
6. Updates queue state

Post queue example:

"posts": [
  {
    "content": "5 lessons from building AI agents in production:\n\n1. ...",
    "scheduled_time": "2024-01-28T09:00:00Z",
    "status": "pending",
    "media": null
  }
]

Engagement Workflow

Run scripts/engage.sh 3-4x daily. The script:

1. Searches for posts matching target criteria (keywords, accounts, hashtags)
2. Scores relevance (content match, author influence, engagement level)
3. Engages with top posts (like, thoughtful comment, or share)
4. Tracks engagement to avoid repeats
5. 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
2. Sends next message in sequence (respects delays)
3. Detects replies and advances/pauses accordingly
4. Handles conditional branching (replied vs not replied)
5. Reports on conversion rates

Sequence example:

{
  "name": "consulting-intro",
  "trigger": "new_connection",
  "steps": [
    {
      "delay_hours": 24,
      "message": "Hey {first_name}! Thanks for connecting. I help {title}s with {pain_point}. Are you currently working on anything in this space?",
      "condition": null
    },
    {
      "delay_hours": 72,
      "message": "Following up — I saw your post about {topic}. Would love to chat about {offering}. Free for a quick call this week?",
      "condition": "no_reply"
    }
  ]
}

Connection Request Workflow

Run scripts/connect.sh weekly (not daily — LinkedIn limits this). The script:

1. Searches for target profiles (job titles, companies, keywords)
2. Filters out existing connections and pending requests
3. Generates personalized connection notes
4. Sends requests with safety throttling (20-30/week max)
5. Tracks acceptance rate

Target criteria:

"connection_targets": [
  {
    "query": "AI consultant OR automation specialist",
    "companies": ["Microsoft", "Google", "OpenAI"],
    "exclude_titles": ["Recruiter"],
    "note_template": "Hey {first_name}, I'm building AI tools for {industry} and saw your work at {company}. Would love to connect!"
  }
]

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:

~/.config/linkedin-autopilot/
├── config.json              # User configuration
├── posts-queue.json         # Scheduled posts
├── engagement-history.json  # Posts engaged with (dedup)
├── dm-sequences.json        # Active DM threads
├── connections.json         # Connection requests + status
├── analytics.json           # Performance metrics
└── activity-log.json        # Full audit trail

Reporting

scripts/report.sh 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)
2. Agent-assisted workflows (agent suggests, human approves)
3. 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

~/.config/linkedin-autopilot/
├── config.json              # Main configuration
├── posts-queue.json         # Scheduled content
├── engagement-history.json  # Activity dedup
├── dm-sequences.json        # Active conversations
├── connections.json         # Network building state
├── analytics.json           # Performance tracking
└── activity-log.json        # Full audit trail

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 dm-sequences.json

"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.

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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.