LinkedIn Post Engagers

Related Skills

post-engagers → email-search → email-generation → campaign-sending

email-search

email-generation

Extruct API Operations

extruct-api

skills/extruct-api/SKILL.md

Inputs

LinkedIn Scraping Providers

get_linkedin_post_comments

get_linkedin_post_reactions

get_linkedin_post_reposts

X-RapidAPI-Key

APIFY_API_TOKEN

curious_coder/linkedin-post-commentors

supreme_coder/linkedin-post-likers

PHANTOMBUSTER_API_KEY

• Anysite MCP is the simplest if they have it connected — no extra credentials needed
• Apify is a good general choice with pay-per-use pricing
• RapidAPI has multiple scrapers with free tiers

Workflow

Step 1: Collect post URLs and choose provider

1. Get the LinkedIn post URL(s) from the user. Accept one or multiple.
2. Ask which engagement types to scrape: comments, reactions, reposts, or all three.
3. Ask which LinkedIn scraping provider they want to use (see table above).
4. If the provider requires credentials, confirm they're available.

activity-

ugcPost-

activity:7433261939285385217

Step 2: Scrape engagers

• Comments:

  mcp__claude_ai_Anysite__get_linkedin_post_comments

  with

  urn: "activity:{id}"

  ,

  count: 1500

• Reactions:

  mcp__claude_ai_Anysite__get_linkedin_post_reactions

  with

  urn: "activity:{id}"

  ,

  count: 1500

• Reposts:

  mcp__claude_ai_Anysite__get_linkedin_post_reposts

  with

  urn: "activity:{id}"

  ,

  count: 1500

• Read or fetch the provider's API documentation
• Identify the endpoint, input format, and response structure
• Implement the scraping calls accordingly

{
    "full_name": "...",
    "linkedin_url": "...",        # profile URL
    "headline": "...",            # job title / headline
    "engagement_type": "...",     # comment / reaction / repost
    "post_url": "...",            # which post they engaged with
}

post_url

Step 3: Deduplicate and classify

1. Deduplicate by

   linkedin_url

   across all posts and engagement types. If someone both commented and reacted, keep both engagement types as a comma-separated value.
2. Apply the segment classifier to job titles (first match wins):

founder|co-founder|ceo|owner

cto|vp.*eng|head of eng|director.*eng

cmo|vp.*market|head of market|director.*market

cro|vp.*sales|head of sales|director.*sales|head of revenue

director|vp|vice president|head of|chief

revops|revenue ops|sales ops|growth ops|gtm ops|gtm eng

product manag|head of product|product lead

data scien|machine learn|ml eng|ai eng|data eng

account exec|sdr|bdr|sales dev|business dev|sales rep

market|content|brand|growth|demand gen|copywrite

sales|commercial|partnerships|revenue

ai|automat|gpt|llm|agent|no.?code

consult|freelanc|advisor|coach|mentor|agenc

engineer|develop|software|fullstack|backend|frontend

1. Present a segment breakdown to the user before proceeding:

Engager Summary:
- Total unique engagers: N
- Comments: N | Reactions: N | Reposts: N

Segment Breakdown:
  Founders / CEOs:         N (X%)
  Sales Leadership:        N (X%)
  Marketing Leadership:    N (X%)
  ...
  Other:                   N (X%)

1. Ask the user: "Want to filter to specific segments before uploading? (e.g. only Founders + Leadership)"

Step 4: Upload to Extruct people table

{
  "name": "{user-provided name or 'Post Engagers - {date}'}",
  "kind": "generic",
  "column_configs": [
    {"kind": "input", "name": "Full Name", "key": "full_name"},
    {"kind": "input", "name": "LinkedIn URL", "key": "linkedin_url"},
    {"kind": "input", "name": "Job Title", "key": "job_title"},
    {"kind": "input", "name": "Segment", "key": "segment"},
    {"kind": "input", "name": "Engagement Type", "key": "engagement_type"},
    {"kind": "input", "name": "Source Post", "key": "source_post"},
    {"kind": "input", "name": "Company", "key": "company"},
    {"kind": "input", "name": "Domain", "key": "domain"}
  ]
}

• Fetch existing rows to deduplicate against current

  linkedin_url

  values
• Upload only new engagers

Step 5: Review and next steps

Upload Complete:
- Engagers uploaded: N
- Table: {table_name}
- URL: https://app.extruct.ai/tables/{table_id}

Segment Breakdown (uploaded):
  Founders / CEOs:     N
  Sales Leadership:    N
  ...

• "Get emails" → run

  email-search

  on the people table to enrich with verified emails
• "Enrich companies" → run

  list-enrichment

  to add company data (industry, size, funding)
• "Draft outreach" → run

  email-generation

  after emails are found
• "Monitor more posts" → re-run with additional post URLs and deduplicate against this table

Tips

• Multiple posts = richer list. Scrape 3-5 recent posts from the same account to build a larger pool. Engagers across multiple posts are highly engaged — flag them.
• Repeat engagers are warmer leads. If someone engaged on 2+ posts, note that in the data — they're more likely to respond to outreach.
• Filter aggressively. Not all engagers are prospects. Use segment filtering to focus on decision makers and skip students, recruiters, etc.
• Respect rate limits. LinkedIn scraping providers have varying rate limits. Don't hammer the API — space out requests if scraping many posts.

Output

https://app.extruct.ai/tables/{table_id}

claude-code-gtm/csv/input/{campaign}/post_engagers.csv