Linkedin Message Writer

LinkedIn Message Writer

Research LinkedIn leads and write personalized messages for any LinkedIn message type. Takes LinkedIn URLs, researches each person using Apify (profile + recent posts), and writes messages based on what it finds.

No LinkedIn cookies. No database setup. Just LinkedIn URLs in, personalized messages out.

When to Auto-Load

Load this skill when:

• User says "write LinkedIn messages", "LinkedIn outreach", "connect with these leads on LinkedIn", "send LinkedIn messages"
• User has a list of LinkedIn URLs and wants to reach out
• User wants to write personalized connection requests, InMails, DMs, or comments

Prerequisites

Apify API Token

Required for researching LinkedIn profiles and posts. Set in .env:

APIFY_API_TOKEN=your_token_here

No LinkedIn cookies, login, or session tokens needed. Apify handles scraping without any LinkedIn credentials.

That's it. One env var. Nothing else.

LinkedIn Message Types Reference

This skill writes any text-based LinkedIn message type. Each type has different constraints.

Key Rules Per Type

Connection request (200/300 chars):

• This is the gatekeeper. If they don't accept, nothing else happens.
• Lead with the signal — what they did/said/posted that caught your attention.
• One sentence of relevance. No pitch, no CTA, no "I'd love to..."
• MUST be under the character limit. Count every character. If over, rewrite — never truncate.
• Free accounts: 200 chars. Premium/Sales Navigator: 300 chars. Ask the user which they have.

InMail (subject 200 + body 1,900 chars):

• Must work standalone — they haven't accepted your connection.
• Subject: curiosity-driven, not salesy. Not "Quick question" or "Partnership opportunity."
• Body: include context for why you're reaching out (the signal). Be specific.
• Higher commitment ask is OK here — you're using a premium credit.

DM (8,000 chars):

• Conversational. These read like DMs, not emails.
• Shorter is almost always better. A 2-sentence message outperforms a 5-sentence one.
• Good for follow-up sequences after connection accepted.
• Sequence structure: Day 0 connection → Day 3 value-first → Day 7 social proof → Day 14 breakup.

Message request (8,000 chars):

• Always reference the shared context (group name, event name, OpenToWork status).
• More casual than InMail since you have something in common.

Post comment (1,250 chars):

• Add genuine value. Share an insight, ask a smart question, build on their point.
• NOT "Great post!" or "Love this!" — that's noise.
• This is a warm-up move, not a pitch. The goal is to get noticed before connecting.

Comment reply (1,250 chars):

• Continue the conversation. Reference what they said specifically.
• Shorter than a standalone comment. 2-3 sentences max.

Workflow

Phase 0: Intake

Ask the user these questions. Skip any already answered.

Leads:

1. Where are your leads? (CSV file, paste LinkedIn URLs, database, CRM — whatever they have)
2. How many leads? (affects cost estimate and whether to use post scraper)

Message type: 3. What kind of LinkedIn message do you want to write? (connection request, InMail, DM, message request, post comment, comment reply, or a sequence of multiple types) 4. If connection request: do you have a free or premium LinkedIn account? (affects character limit: 200 vs 300)

Goal: 5. What's the objective? (book meetings, drive demo requests, get replies, build relationships, promote content, warm up before outreach) 6. What's the angle or hook? (pain-based, hiring signal, competitor displacement, event-based, content engagement, mutual connection, cold)

Tone: 7. Which tone? Present options:

• Casual Professional — Friendly, human, slightly informal. Like messaging a peer. (default)
• Thought Leader — Lead with insight or a contrarian take. Position sender as expert.
• Provocative — Challenge assumptions, pattern-interrupt. Higher risk, higher reward.
• Enterprise Formal — Polished, structured. For regulated industries or C-suite targets.
• Custom — User pastes reference messages that have worked, or describes the vibe.

8. Any reference messages that have worked well? (these override tone presets)

Context: 9. What does your company/product do? (one-liner for the AI to work with) 10. Any proof points? (customer names, metrics, case studies to reference)

Output: 11. Which LinkedIn outreach tool do you use? (Dripify / Expandi / Botdog / PhantomBuster / Just give me a CSV)

Phase 1: Load Leads

Accept leads from whatever source the user provides:

• CSV file: Read the CSV. Look for a column containing LinkedIn URLs (common names: linkedin_url, LinkedIn URL, LinkedIn, profile_url, url). If ambiguous, ask the user which column.
• Pasted URLs: User pastes LinkedIn URLs directly. Parse them.
• Pasted list: User pastes names + companies or other data. Extract what's available.
• Database/CRM: Ask the user how to access it. Use whatever tool or export they provide.

Minimum required: At least one LinkedIn URL per lead.

Present the lead count to the user and confirm before proceeding to research.

Phase 2: Research

Research each lead using two Apify actors. Both require only APIFY_API_TOKEN — no LinkedIn cookies.

Step 1: Profile Data

Use harvestapi/linkedin-profile-scraper to get profile data for all leads.

API call:

curl -X POST "https://api.apify.com/v2/acts/harvestapi~linkedin-profile-scraper/runs?token=$APIFY_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "urls": [
      {"url": "https://www.linkedin.com/in/PROFILE_1/"},
      {"url": "https://www.linkedin.com/in/PROFILE_2/"}
    ]
  }'

Cost: $0.003 per profile. 100 leads = $0.30.

Returns per lead:

• firstName, lastName, headline
• jobTitle, companyName, companySize, companyIndustry
• Full work history (positions array with title, company, description, duration)
• Education (schools, degrees)
• Skills (with endorsement counts)
• Location, followerCount, connectionsCount
• isCreator, isPremium, isVerified flags

Polling for results:

# Check run status
curl "https://api.apify.com/v2/acts/harvestapi~linkedin-profile-scraper/runs/{RUN_ID}?token=$APIFY_API_TOKEN"

# When status is SUCCEEDED, fetch results
curl "https://api.apify.com/v2/datasets/{DATASET_ID}/items?token=$APIFY_API_TOKEN"

Step 2: Recent Posts (Optional)

Use harvestapi/linkedin-profile-posts to get recent posts. Run this when:

• User asks for deep personalization
• Lead count is small (under 50) and budget allows
• User explicitly wants to reference what leads are posting about

Skip this when:

• Lead count is large (100+) and user wants speed over depth
• User says basic personalization is fine

API call:

curl -X POST "https://api.apify.com/v2/acts/harvestapi~linkedin-profile-posts/runs?token=$APIFY_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "profileUrls": [
      "https://www.linkedin.com/in/PROFILE_1/",
      "https://www.linkedin.com/in/PROFILE_2/"
    ]
  }'

Cost: $0.002 per post. ~20 posts per profile = ~$0.04 per lead. 50 leads = $2.00.

Returns per post:

• content (full post text)
• engagement (likes, comments, shares, reaction breakdown)
• postedAt (timestamp)
• postImages (if any)
• author info (name, headline)

Polling: Same pattern as Step 1.

Step 3: Present Research Summary

After research completes, present a summary table:

Leads researched: {count}
Profile data: {count} profiles retrieved
Posts scraped: {count} posts from {count} leads (or "skipped")
Research cost: ~${total}

Sample leads:
| Name | Title | Company | Recent Post Topic | Personalization Angle |
|------|-------|---------|-------------------|----------------------|
| Jane Smith | VP Sales | Acme Corp | Posted about AI in sales | Reference her AI post |
| ... | ... | ... | ... | ... |

If the user asked to filter/qualify leads, do that now based on profile data (title, company, industry, etc.) and present which leads made the cut.

Phase 3: Write Messages

Generate personalized messages for each lead based on the research.

Personalization Hierarchy

Use the best available signal for each lead. In order of strength:

1. Recent post content — Reference a specific post they wrote. Strongest signal.
2. Work history details — Reference a specific achievement from their profile (e.g., "scaled from 0 to $8M GMV" is better than "you're a Co-founder").
3. Creator topics/hashtags — Reference what they post about broadly.
4. Current role + company — Reference their current position and what the company does.
5. Education/background — Mutual school, shared background. Weakest but still personal.

If the user provided reference messages that have worked, analyze those for tone, length, structure, and vocabulary. Use them as the template — don't override with defaults.

Writing Process

1. Generate samples first. Write messages for 3-5 leads with different signal richness levels. Present to user.
2. Iterate. User reviews, gives feedback. Adjust tone/approach. Max 3 rounds.
3. Batch generate. After approval, write messages for all remaining leads.

Character Limit Enforcement

After generating any message, count the characters. If over the limit:

• Rewrite from scratch. Do NOT truncate.
• Truncated messages look broken and unprofessional.
• For connection requests (200/300 chars), every character matters. Be ruthless.

Phase 4: Export

Universal CSV Format

Generate a CSV with these columns:

linkedin_url, first_name, last_name, company, title, message_type, message_subject, message_body

For sequence-based campaigns (connection + follow-ups), use:

linkedin_url, first_name, last_name, company, title, connection_request, followup_1, followup_2, followup_3, inmail_subject, inmail_body

Tool-Specific Formatting

Dripify:

• Columns: Profile URL, Note, Message 1, Message 2, Message 3
• One row per lead with all messages in separate columns

Expandi:

• Columns: LinkedIn URL, Connection message, Follow-up #1, Follow-up #2, Follow-up #3, InMail subject, InMail message

Botdog:

• Columns: linkedin_profile_url, connection_note, message_1, message_2, message_3

PhantomBuster:

• Columns: profileUrl, message
• PhantomBuster typically handles one action at a time — may need separate CSVs for connection + follow-ups

Generic CSV / Other:

• Use the universal format
• Ask the user what their tool expects and adjust if needed

Save Files

Save to the current working directory:

{campaign-name}-{YYYY-MM-DD}.csv

Phase 5: Review & Deliver

Present final summary:

Campaign: {name}
Message type: {type}
Leads: {count}
Tool: {dripify/expandi/etc.}
Personalization: {profile-only / profile+posts}
Research cost: ~${amount}
Export file: {file_path}

Show 3-5 sample messages from the export for final review.

Do NOT mark as done without explicit user confirmation. Ask: "Messages look good? Anything to adjust before you import?"

After confirmation:

• Provide the file path
• Give tool-specific import instructions
• Remind user to verify the first few messages after import

Cost Estimates

Profile scraper: $0.003/profile. Post scraper: ~$0.04/lead (20 posts × $0.002).

Error Handling