Linkedin Reaction Scraper

LinkedIn Reaction Scraper

Overview

Extract every person who reacted to a LinkedIn post with full profile enrichment (experience, education, location, skills) using the Apify actor harvestapi/linkedin-post-reactions. Reactions yield 5-10x more leads than comments and ship richer profile data out of the box.

When to use

• User pastes a LinkedIn post URL and asks to scrape reactors, likers, or people who engaged with the post
• User wants broad audience capture from a viral or high-engagement post
• User needs enriched lead data (location, current company, title) without an immediate Apollo pass
• A parent skill (e.g. linkedin-reaction-to-outreach) calls this as the first stage of a pipeline

When NOT to use

• User wants commenters instead of reactors -> use linkedin-comment-scraper
• The post is private, deleted, or behind a login wall the actor cannot reach
• User only needs people who wrote a comment (engagement quality > volume)
• The target is a LinkedIn profile, company page, or search result rather than a single post

Prerequisites

• Apify MCP server connected (mcp__apify__* tools available)
• APIFY_TOKEN environment variable set on the host. See ../SETUP.md for installation and credential setup.
• A valid LinkedIn post URL (or one that can be resolved via WebFetch)

Quick Reference

• Input: LinkedIn post URL (/posts/..., /feed/update/urn:li:activity:ID, or /feed/update/urn:li:ugcPost:ID)
• Output file: ./output/linkedin_reactors.json
• Apify actor: harvestapi/linkedin-post-reactions
• Critical pitfall: maxItems defaults to 10. ALWAYS override to 500+ or you will silently truncate the dataset.
• Required mode: profileScraperMode: "main" (full profiles). "short" returns minimal data.

Why Reactions vs Comments

| Feature | Comments Scraper | Reactions Scraper | |---------|-----------------|-------------------| | Data richness | Name, headline, comment text | Full profile: experience, education, skills, certs | | Volume | Fewer (only people who commented) | More (likes are 5-10x more common than comments) | | Profile fields | Basic (name, headline, company, profileUrl) | Rich (location, experience history, education, certifications, skills, connections count, about) | | Apollo needed? | Yes -- must enrich for emails | Still needed for emails, but company/title/location already available | | Best for | Engaged leads (took time to comment) | Broad audience capture, richer lead data |

The Workflow

Step 1: Validate the LinkedIn Post URL

Accept any of these URL formats:

• https://www.linkedin.com/posts/username_slug-activity-ID-XXXX
• https://www.linkedin.com/feed/update/urn:li:activity:ID
• https://www.linkedin.com/feed/update/urn:li:ugcPost:ID

Strip UTM parameters (?utm_source=...) -- they work but are unnecessary noise.

If the user provides a shortened or redirect URL, resolve it first via WebFetch.

Step 2: Fetch Actor Details

Use: mcp__apify__fetch-actor-details
Actor: harvestapi/linkedin-post-reactions

Key input fields:

• posts (array of strings) -- LinkedIn post URLs to scrape
• maxItems (number) -- Maximum results. DEFAULTS TO 10. Always override.
• profileScraperMode (string) -- "short" (basic) or "main" (full profile). Always use "main".
• reactionTypeFilter (string) -- Filter by reaction type: LIKE, PRAISE, EMPATHY, APPRECIATION, INTEREST, ENTERTAINMENT, or ALL. Default: all types.

Step 3: Run a Test Scrape (maxItems: 20)

Always test first to validate the URL works and data structure is correct:

Use: mcp__apify__call-actor
Actor: harvestapi/linkedin-post-reactions
Input: { "posts": ["THE_URL"], "maxItems": 20, "profileScraperMode": "main" }

Verify output fields populated:

• actor.name, actor.headline, actor.linkedinUrl
• actor.location.parsed (city, state, country)
• actor.currentPosition (company, title)
• actor.experience (full history)
• reactionType (LIKE, PRAISE, etc.)

Step 4: Run Full Scrape (maxItems: 500+)

Once test passes, scrape everything:

Use: mcp__apify__call-actor
Actor: harvestapi/linkedin-post-reactions
Input: { "posts": ["THE_URL"], "maxItems": 500, "profileScraperMode": "main" }

CRITICAL: The default maxItems is 10. Posts with 100+ reactions WILL be truncated unless you explicitly set this higher. Always set to at least 500.

For posts with 500+ reactions: Set maxItems to the expected count. Viral posts can have thousands of reactions. Ask the user if they want all or a cap.

Step 5: Process and Deduplicate

1. Parse the Apify output dataset (use get-actor-output if needed for pagination)
2. Remove null profiles -- entries with null/empty actor.linkedinUrl are deleted accounts
3. Deduplicate by actor.linkedinUrl -- same person can react to reposts
4. Normalize and extract fields:

{
    "fullName": item["actor"]["name"],
    "firstName": item["actor"]["firstName"],
    "lastName": item["actor"]["lastName"],
    "headline": item["actor"]["headline"],
    "profileUrl": item["actor"]["linkedinUrl"],
    "companyName": item["actor"]["currentPosition"][0]["companyName"] if item["actor"].get("currentPosition") else None,
    "jobTitle": item["actor"]["currentPosition"][0].get("position") if item["actor"].get("currentPosition") else item["actor"].get("position"),
    "city": item["actor"]["location"]["parsed"]["city"] if item["actor"].get("location", {}).get("parsed") else None,
    "state": item["actor"]["location"]["parsed"]["state"] if item["actor"].get("location", {}).get("parsed") else None,
    "country": item["actor"]["location"]["parsed"]["country"] if item["actor"].get("location", {}).get("parsed") else None,
    "connectionsCount": item["actor"].get("connectionsCount"),
    "about": item["actor"].get("about"),
    "reactionType": item["reactionType"],
    "verified": item["actor"].get("verified", False),
    "premium": item["actor"].get("premium", False),
    "openToWork": item["actor"].get("openToWork", False)
}

1. Extract company from headline if currentPosition is null:

• "CPA at Smith Advisory" -> companyName: "Smith Advisory"
• "Senior Accountant | Deloitte" -> companyName: "Deloitte"

Step 6: Save and Report

Save processed data to ./output/linkedin_reactors.json.

Report to user:

• Total reactions scraped (raw)
• Unique reactors (after dedup)
• Records removed (null profiles)
• Reaction type breakdown (X likes, Y celebrates, etc.)
• Sample of first 5 names with titles
• Note: Full profile data available (experience, education, skills) -- richer than comment scraper

Data Schema (Full Output from Actor)

Each reactor record contains:

{
  "reactionType": "LIKE",
  "postId": "urn:li:ugcPost:...",
  "actor": {
    "name": "Jane Doe",
    "firstName": "Jane",
    "lastName": "Doe",
    "headline": "Example Role | Example Company",
    "linkedinUrl": "https://www.linkedin.com/in/example-profile",
    "publicIdentifier": "example-profile",
    "location": {
      "linkedinText": "Example City, Example State, United States",
      "parsed": {
        "city": "Example City",
        "state": "Example State",
        "country": "United States",
        "countryCode": "US"
      }
    },
    "currentPosition": [
      { "companyName": "Example Company LLC", "dateRange": { "start": { "month": 1, "year": 2024 } } }
    ],
    "experience": [ ],
    "education": [ ],
    "certifications": [ ],
    "skills": [ { "name": "Tax planning & preparation" } ],
    "connectionsCount": 239,
    "followerCount": 251,
    "about": "...",
    "verified": true,
    "premium": false,
    "openToWork": false
  }
}

Composio Fallback (Optional Path)

If the founder is on Composio.dev MCP instead of the direct Apify MCP, route this skill through Composio. Same Apify actor, same input — only the transport changes.

Detection:

• mcp__composio__COMPOSIO_SEARCH_TOOLS is available in the session
• The direct mcp__apify__* tools are missing or error on first call

Fallback workflow:

1. Discover slugs and check connection: call mcp__composio__COMPOSIO_SEARCH_TOOLS with use_case: "scrape reactions/likes from a LinkedIn post URL using an Apify actor" and session: { generate_id: true }. Save the session_id.
2. If the apify toolkit isn't connected, call mcp__composio__COMPOSIO_MANAGE_CONNECTIONS with toolkits: ["apify"] and surface the redirect_url as a clickable link.
3. Poll with mcp__composio__COMPOSIO_WAIT_FOR_CONNECTIONS until Active.
4. Run the equivalent of Steps 3 and 4 using the Composio slug:

• Slug: APIFY_RUN_ACTOR_SYNC
• Input: { "actor_id": "harvestapi/linkedin-post-reactions", "input": { "posts": ["THE_URL"], "maxItems": 500, "profileScraperMode": "main" } }

1. Continue with Steps 5-6 of the standard workflow as written.

Tool slug equivalents:

• mcp__apify__fetch-actor-details -> APIFY_GET_ACTOR
• mcp__apify__call-actor -> APIFY_RUN_ACTOR_SYNC
• mcp__apify__get-actor-output -> APIFY_ACTOR_RUN_GET

Output difference: APIFY_RUN_ACTOR_SYNC returns dataset items inline. The shape of each actor.* field is identical to the direct path — the parser in Step 5 works unchanged.

Long-run note: Composio's sync wrapper has a 300-second timeout. If a viral post with 1000+ reactions exceeds that, switch to async: call APIFY_RUN_ACTOR (no sync), capture the run ID, then poll APIFY_ACTOR_RUN_GET until status is SUCCEEDED, then read the dataset.

Common Mistakes

| Mistake | Fix | |---------|-----| | Only 10 results returned | Default maxItems is 10. Always set to 500+. | | Null LinkedIn URLs in output | Filter out. These are deleted/deactivated accounts. | | currentPosition is null | Parse company from headline field. | | Some posts return 0 results | Post may be private, deleted, or too old. Try urn:li:activity:ID format. | | Using profileScraperMode: "short" | Returns minimal data. Always use "main" for full profiles. | | Surprise on long runs | 500+ reactions with "main" mode = 3-5 minutes. Normal. | | Duplicate reactors across reposts | Same person reacts to original + repost. Deduplicate by actor.linkedinUrl. | | UTM parameters in URL | Strip them. They work but add noise. | | Hardcoding /tmp/... paths | Use ./output/linkedin_reactors.json so it works on Windows and CI. |

Reaction Types

| Type | LinkedIn Icon | Meaning | |------|--------------|---------| | LIKE | Thumbs up | Standard like | | PRAISE | Clapping hands | Celebrate | | EMPATHY | Heart | Love | | APPRECIATION | Lightbulb | Insightful | | INTEREST | Thinking face | Curious | | ENTERTAINMENT | Laughing face | Funny |

Use reactionTypeFilter to target specific engagement types.

Example

User pastes: https://www.linkedin.com/posts/example-user_launch-activity-7100000000000000000-AbCd?utm_source=share

1. Strip the ?utm_source=share -> clean URL.
2. Call mcp__apify__fetch-actor-details for harvestapi/linkedin-post-reactions to confirm the input schema.
3. Test run with { "posts": [""], "maxItems": 20, "profileScraperMode": "main" }. Inspect a record like:

• actor.name: "Jane Doe"
• actor.currentPosition[0].companyName: "Example Company LLC"
• actor.location.parsed.city: "Example City"

1. Full run with { "posts": [""], "maxItems": 500, "profileScraperMode": "main" }.
2. Filter null linkedinUrl, dedupe by linkedinUrl, normalize fields, save to ./output/linkedin_reactors.json.
3. Report: "Scraped 412 raw reactions -> 387 unique reactors after dedup. 312 LIKE, 48 PRAISE, 27 EMPATHY. Saved to ./output/linkedin_reactors.json. Ready to hand off to airtable-lead-loader."

Output

• File: ./output/linkedin_reactors.json -- all reactors with enriched profiles
• Ready for airtable-lead-loader skill (map reactor fields to Airtable schema)
• Richer data than comment scraper -- may reduce Apollo enrichment needs for company/title/location
• Emails still require Apollo enrichment

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: felipesalinasr
• Source: felipesalinasr/fsr-stack
• License: MIT

Install and usage instructions live in the source repository linked above.