linkedin-automation

name	linkedin-automation
description	Automate LinkedIn outreach safely with compliance-first practices. Handles connection requests, personalized messaging, and follow-up sequences while respecting LinkedIn's rate limits and detection systems. Triggers: 'message LinkedIn people', 'LinkedIn automation', 'send connection requests on LinkedIn', 'LinkedIn outreach', 'LinkedIn bot', 'message people on LinkedIn'.

LinkedIn Automation Skill

Safe, compliant LinkedIn outreach automation that respects LinkedIn's terms of service and minimizes account risk.

Before Starting

Check for context first:

• Is the user logged into LinkedIn?
• Do they have the OpenCode Chrome extension connected?
• Get the list of profile URLs from the user
• Get the message template they want to send

Ask one clarifying question if needed: "Should I personalize each message with profile-specific details, or use the same template for all?"

CSV Data Input (Optional)

If the user has a CSV file with prospect data:

1. Ask for the CSV file path — e.g., leads.csv
2. Read the CSV and extract column headers
3. Map columns to variables — Use {column_name} syntax in message template
4. Validate — Ensure all required columns exist

Example CSV format:

first_name,company,role,linkedin_url,recent_activity,job_change
John,Acme Corp,VP Sales,linkedin.com/in/johndoe,hiring 3 new AEs,joined 2 months ago
Sarah,Tech Inc,CTO,linkedin.com/in/sarahsmith,launched new API,promoted last month

Message template:

Hi {first_name}, saw you're now {role} at {company}. {recent_activity} — impressive timing. Would love to connect.

Renders to:

"Hi John, saw you're now VP Sales at Acme Corp. hiring 3 new AEs — impressive timing. Would love to connect."

"Hi Sarah, saw you're now CTO at Tech Inc. launched new API — impressive timing. Would love to connect."

Requirements:

• CSV must have linkedin_url column
• All {variable} in template must match a column header
• Empty cells: Use fallback or skip personalization for that row

How This Skill Works

Mode 1: Pre-Flight Check

Verify safe conditions before starting:

• Chrome with OpenCode extension is running
• User is logged into LinkedIn
• Confirm rate limits with user (default to conservative)

Mode 2: Safe Execution

Execute outreach with anti-detection measures:

• Open each profile in LinkedIn
• Hover before clicking (mimic human behavior)
• Type message character-by-character with delays
• 2-5 second pauses between actions
• Spread activity throughout the day

Mode 3: Post-Monitoring

After execution:

• Watch for LinkedIn warnings
• Advise user to monitor account health
• Track metrics manually

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Safe Rate Limits

These limits are based on industry best practices and community findings to avoid account restrictions:

Action	Daily Limit	Weekly Limit	Notes
Connection Requests	40-50	300-500	Start lower for new accounts
New Messages	20-50	100-250	Spread throughout day
Profile Views	50-100	300-500	Reduced effectiveness in 2026
Likes/Comments	20-50	—	Must be genuinely relevant

Account Age Adjustments

• New accounts (< 3 months): Start at 50% of limits, ramp up over 4-6 weeks
• Established accounts: Can use full limits
• High SSI scores (75+): Slightly more lenient

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Anti-Detection Measures

Timing & Pacing

Technique	Implementation
Action delays	2-5 seconds between each action
Character typing	Type character-by-character, not instant fill
Randomized intervals	Vary delays so actions aren't rhythmic
Session length	Limit to 30-45 minutes per session
Weekend reduction	Reduce activity by 50% on weekends

Behavioral Patterns

• Hover before click: Briefly hover over elements before clicking
• Natural scrolling: Occasionally scroll naturally between actions
• Mix activities: Alternate between viewing profiles, sending messages, engaging with content
• Time zone consistency: Avoid activity at unnatural hours (e.g., 3 AM)

Message Personalization (Critical)

Every message MUST include unique personalization:

• Reference specific profile details (recent post, job change, company news)
• Use 10+ dynamic variables, not just [First Name]
• Vary message structure and length
• If message could be sent to 50 people without modification, it's too generic

Example of good personalization:

"Hi Sarah, saw you just joined Acme Corp as VP Sales. Congrats on the move. I noticed you're hiring 3 new AEs based on your LinkedIn posts about scaling the team..."

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Compliance Checklist

Before starting any automation:

• Using approved tools only (Chrome DevTools, official API integrations)
• Personalizing all outreach messages
• Staying within rate limits
• Mixing automated + manual engagement
• Monitoring account health metrics
• Prepared to pause if warnings appear
• Understanding GDPR/CCPA requirements if applicable

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Account Health Monitoring

Key Metrics to Track

Metric	Healthy	Warning	Action Needed
Connection Acceptance Rate	30-40%+	15-20%	Pause & review targeting
Message Response Rate	2-5%+	<1%	Pause & improve messaging
Connection Requests Pending	Normal	Much higher than usual	May be rate limited

Warning Signs to Pause Immediately

• LinkedIn asks you to verify identity
• "Temporary restriction" notification
• Messages not delivering
• Unusual number of "I don't know this person" rejections
• Reduced profile/post visibility

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Consequences of Violations

Violation	Consequence	Recovery Time
First warning	Warning popup	Immediate
Rate limit exceeded	24-72 hour restriction	1-3 days
Repeat/spam reports	30-90 day suspension	1-12 weeks
Severe violation	Permanent ban	Nearly impossible

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LinkedIn's Detection Methods

LinkedIn uses multiple methods to detect automation:

1. Behavioral fingerprinting — Analyzes timing, patterns, and consistency
2. Rate limiting — Built-in anomaly detection
3. User reports — Spam complaints trigger investigation
4. Content analysis — Identifies identical/mass-sent messages
5. IP analysis — Multiple accounts from same IP acting similarly

What Triggers Flags

• Same message sent 50+ times
• 100+ connection requests in 2 hours
• Activity at 3 AM across time zones
• Sudden spike after dormant period
• Low acceptance rates (spam signal)

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Content-First Sequencing (Recommended)

For better results and lower risk:

1. Days 1-5: Engage with prospect's content (likes, thoughtful comments)
2. Day 6: Send connection request with personalization
3. Day 7-10: Send follow-up message after acceptance
4. Day 14: Share relevant content/insight
5. Day 21: Direct meeting request

This approach has 3x higher acceptance rates because prospects recognize your name before the connection request.

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Engagement Workflows

Mode A: Content Engagement (Warm-Up)

Purpose: Get prospect familiar with your name before outreach

Actions:

1. Find prospect's recent posts (last 5-10 posts)
2. Like 1-2 posts (genuine engagement, not mass liking)
3. Comment something thoughtful (not generic "Great post!")

Comment templates (rotate between):

Post Type	Comment
Industry opinion	"Interesting take. We saw something similar with {company} clients..."
Product launch	"This is exactly what the market needs. Curious about the dev story..."
Hiring news	"Team growth is exciting. What's your hiring focus for Q3?"
Data/Stats	"That {percentage} shift is huge. Are you seeing it in retention too?"
Question	"I've wondered the same thing. Our experience: {insight}"

Timing:

• Engage with 10-15 prospects per day
• Space out: 5-10 minutes between each
• Limit: 20-30 likes + 5-10 comments per day

Mode B: Profile Warming

Purpose: Get on prospect's "viewed your profile" notification

Actions:

1. View prospect's profile (full view, not quick glance)
2. Check their "About" section
3. Look at their featured content

Note: Profile views have reduced effectiveness in 2026. Use sparingly.

Mode C: Full Sequence (Recommended)

Complete workflow for each prospect:

Day 1:   Like 2 of their posts + 1 thoughtful comment
Day 2:   View their profile (full)
Day 3-5:  (Active pause - let it sink in)
Day 6:   Send connection request with personalization
Day 7:   (If accepted) Send follow-up message
Day 10:  Share relevant content/insight
Day 14:  Meeting request or re-engagement

Mode D: Comment-First Outreach

Purpose: Comment on their post, then connect in the same visit

Workflow:

1. Navigate to their recent post
2. Leave genuine comment referencing a specific point
3. Immediately send connection request (they'll recognize you)

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Enhanced Engagement Templates

Connection Request (After Engagement)

Hi {first_name}, saw your thoughts on {post_topic} - really resonated.
{follow_up_personalization}
Always great to connect with others in {industry} space.

Follow-Up (Post-Acceptance)

Thanks for connecting, {first_name}! 
{refer_to_engagement}: "I saw your updates on {topic} - made me think about {insight}."
{value_proposition}
{specific_cta}

Re-Engagement (Day 14)

{first_name}, came across your {recent_activity} update. 
{new_personalization_angle}
Would love to chat about {topic} when you have 15 mins.

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Practical Execution

If user wants multi-step engagement:

1. Get prospect list with their recent post URLs or topics
2. Select engagement type: likes-only, comments, profile views, full sequence
3. Generate engagement comments using templates
4. Execute with delays (same anti-detection measures apply)
5. Track what was engaged vs. what needs follow-up

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Common Mistakes to Avoid

1. Ignoring rate limits — Don't try to max out daily limits
2. Generic messaging — Every message needs unique personalization
3. "Set and forget" — Monitor account health daily
4. New account automation — Warm up new accounts for 2-4 weeks first
5. Using prohibited tools — Chrome extensions are higher risk than cloud tools

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First-Time Setup (If Not Configured)

1. Install OpenCode Chrome extension from Chrome Web Store
2. Log into LinkedIn manually in Chrome
3. Verify extension is connected — Check for OpenCode icon
4. Start with conservative limits — 20 connections/day for first week
5. Monitor metrics closely — Adjust based on account health

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Proactive Triggers

Flag these without being asked:

• Rate limit warning — If user wants to send >50 messages/day, warn about risks
• Generic template detected — If message has no personalization variables, flag it
• New account — If account is <3 months old, recommend starting at 50% limits
• Warning signs — If any restrictions appear during execution, pause and advise

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Communication

All output follows the structured standard:

• Bottom line first — Answer before explanation
• What + Why + How — Every finding includes all three
• Risk warnings — Flag any potentially unsafe actions
• Confidence tagging — 🟢 verified / 🟡 medium / 🔴 high risk

When executing: State each action before doing it. "Opening profile 1 of 5... Typing message with 3-second delay between characters..."

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Related Skills

• content-humanizer: Use to make LinkedIn outreach messages sound more human and personalized
• workspace-guide: Use for onboarding new users to OpenCode before LinkedIn automation

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Sources

• Cykel AI: The Ultimate Guide to AI LinkedIn Automation
• Fuzzy AI: The Ultimate Complete Guide to LinkedIn Bot Automation
• Gojiberry: LinkedIn Automation Compliance Guide