linkedin-reaction-to-outreach

name

linkedin-reaction-to-outreach

description

Use when the user wants to turn LinkedIn post reactors (likes, celebrates, loves) into a paused Instantly cold email campaign, mentions a "reactions pipeline", asks to "scrape reactions and email them", or pastes a LinkedIn post URL with intent to reach the people who reacted. Orchestrates five sub-skills (reaction scraping, Airtable loading, Apollo enrichment, sequence writing, Instantly setup) and produces a paused campaign ready for review.

LinkedIn Reaction-to-Outreach Pipeline

Overview

Convert every reactor on a LinkedIn post into a paused, ready-to-launch Instantly cold email campaign by chaining five sub-skills in a single deterministic pipeline. Reactor profiles arrive richer than commenter profiles, so this orchestrator favors volume plays where company, title, and location data are already in the scrape.

When to use

• A LinkedIn post URL is provided with intent to reach the people who reacted (liked, celebrated, loved, etc.)
• The user asks for a "reactions pipeline", "outreach from reactions", or "scrape reactions and email them"
• Volume matters more than per-lead intensity (reactors are 5-10x larger than commenters)
• The user wants full LinkedIn profile data (experience, education, skills) loaded into Airtable

When NOT to use

• Use linkedin-comment-to-outreach when the target audience is commenters on a post (smaller, higher-intent list)
• Use linkedin-reaction-scraper directly when the user only wants the raw reactor data and no downstream pipeline
• Use instantly-campaign directly when leads already exist in Apollo or Airtable and only the campaign step is needed
• Skip this skill for one-off lead lookups, ABM research, or anything not anchored to a LinkedIn post URL

The Pipeline

LinkedIn Post URL
       |
       v
[1. linkedin-reaction-scraper]  -- Apify scrape, full profiles, deduplicate
       |
       v
[2. airtable-lead-loader]      -- Create table, batch load records
       |
       v
[3. apollo-enrichment]         -- Bulk email lookup, update Airtable, create contacts
       |
       v
[4. cold-email-sequence]       -- Co-write 3 emails with user (interactive)
       |
       v
[5. instantly-campaign]        -- Create campaign, load leads, configure accounts
       |
       v
Campaign ready for review (paused)

---

Why Reactions vs Comments Pipeline

Dimension	Comments Pipeline	Reactions Pipeline (this one)
Volume	Lower (commenting takes effort)	Higher (liking is easy, 5-10x more people)
Profile data from scrape	Basic: name, headline, profileUrl	Rich: full experience, education, skills, certs, location
Apollo dependency	High -- need Apollo for company, title, location	Lower -- company, title, location already in scrape data
Lead quality signal	Strong (took time to write)	Moderate (one-click action)
Best for	Smaller, high-intent lists	Broader audience capture, volume plays

---

Prerequisites

• MCP tools: Apify, Airtable, Apollo.io
• API key: Instantly API key (user provides or stored)
• Airtable base: Existing base ID (user provides or we find it)
• User input needed for Step 4: Social proof, product description, CTA preferences

---

The Workflow

Phase 1: Scrape Reactions (Skill: linkedin-reaction-scraper)

Input: LinkedIn post URL from user Output: ./output/linkedin_reactors.json

1. Validate the LinkedIn post URL (any format accepted)
2. Strip UTM parameters
3. Test scrape with maxItems: 20, profileScraperMode: "main"
4. Verify rich data fields populated: actor.name, actor.headline, actor.currentPosition, actor.location.parsed, actor.experience
5. Full scrape with maxItems: 500 (increase for viral posts)
6. Process results:
   • Remove null profiles (deleted/deactivated accounts)
   • Deduplicate by actor.linkedinUrl
   • Extract company from headline if currentPosition is null
   • Parse reaction type breakdown
7. Save to ./output/linkedin_reactors.json

Key differences from comment scraper:

• Actor: harvestapi/linkedin-post-reactions (not linkedin-post-comments)
• profileScraperMode: "main" gives full profiles -- ALWAYS use this
• Default maxItems is 10 (even lower than comment scraper's 20) -- ALWAYS override
• Data nested under actor key (not flat like comment scraper)
• reactionType field available for filtering (LIKE, PRAISE, EMPATHY, etc.)

Checkpoint: Report to user:

• "Scraped X unique reactors from [Author]'s post"
• Reaction breakdown (X likes, Y celebrates, Z loves, etc.)
• "Full profiles captured: experience, education, skills, location"
• "Proceeding to Airtable."

---

Phase 2: Store (Skill: airtable-lead-loader)

Input: ./output/linkedin_reactors.json, Airtable Base ID Output: Airtable table populated, record IDs saved

1. List Airtable bases, confirm with user
2. Create table: "LinkedIn Reactors - [Author Name]"
3. Expanded 22-field schema -- captures ALL rich data from the reactions scraper:

#	Field	Airtable Type	Source	Notes
1	Full Name	Single line text	actor.name
2	First Name	Single line text	actor.firstName
3	Last Name	Single line text	actor.lastName
4	Headline	Single line text	actor.headline
5	LinkedIn URL	URL	actor.linkedinUrl	Canonical /in/slug URL
6	Company	Single line text	actor.currentPosition[0].companyName	Fallback: parse from headline
7	Company LinkedIn URL	URL	actor.currentPosition[0].companyLinkedinUrl	May be null
8	Job Title	Single line text	actor.currentPosition[0].position	Fallback: first segment of headline
9	City	Single line text	actor.location.parsed.city
10	State	Single line text	actor.location.parsed.state
11	Country	Single line text	actor.location.parsed.country
12	About	Long text	actor.about	Full LinkedIn bio
13	Experience	Long text	actor.experience	Formatted (see below)
14	Education	Long text	actor.education	Formatted (see below)
15	Top Skills	Single line text	actor.topSkills or actor.skills	Comma-separated list
16	Connections	Number	actor.connectionsCount
17	Followers	Number	actor.followerCount
18	Reaction Type	Single line text	reactionType	LIKE, PRAISE, EMPATHY, APPRECIATION, INTEREST, ENTERTAINMENT
19	Email	Email	(empty)	Filled by Apollo in Phase 3
20	Apollo Match	Checkbox	(false)	Set by Phase 3
21	Source	Single line text	Auto	"LinkedIn Reactions - [Post URL slug]"
22	Profile JSON	Long text	Full actor object	Raw JSON backup for data not in other fields

Formatting Complex Fields

Experience -- flatten the array into readable text:

Senior Accountant @ Deloitte (Jan 2020 - Present, 4 yrs 3 mos)
Staff Accountant @ PwC (Jun 2017 - Dec 2019, 2 yrs 7 mos)

Pattern: {position} @ {companyName} ({startDate.text} - {endDate.text}, {duration}) One line per role. Most recent first (array is already sorted).

Education -- same approach:

Master of Science, Accounting @ Colorado State University (2019 - 2022)
Bachelor's, Finance @ Monroe University (2008 - 2013)

Pattern: {degree} @ {schoolName} ({period})

Top Skills -- comma-separated string:

Tax Planning, Auditing, Financial Reporting

Source: actor.skills[].name array. If actor.topSkills exists, use that instead.

Profile JSON -- store JSON.stringify(actor) as a backup. This preserves certifications, volunteering, recommendations, publications, and any other fields not mapped to dedicated columns. Useful for future analysis without re-scraping.

Field Mapping Code

Walk every reactor item, pull actor plus the first currentPosition and location.parsed, format experience and education arrays into newline-joined strings, fall back to parsing the headline for company when currentPosition is empty, then emit the 22-field record.

See the airtable-lead-loader skill for the full mapping logic and reference implementation.

4. Batch load via parallel agents (4 agents, 10 records per create_record call)
5. Verify load count matches scrape count

Checkpoint: Report:

• "Loaded X records into Airtable with full profile data"
• "Y have company data, Z have location, W have bios"
• "Experience and education captured for all profiles"
• "Starting Apollo enrichment for emails."

---

Phase 3: Enrich (Skill: apollo-enrichment)

Input: Airtable Base ID, Table ID Output: Airtable updated with emails, Apollo contacts created

1. Pull all records from Airtable
2. Batch into groups of 10 for Apollo apollo_people_bulk_match
3. Run enrichment via parallel agents (4 agents)
4. Update Airtable records with:
   • Email (primary purpose of this step)
   • Company (Apollo override if scrape data was parsed from headline)
   • Title (Apollo override if more specific)
   • City, State, Country (Apollo override if scrape had null location)
5. Set Apollo Match = true for matched records
6. Create Apollo contacts under label: "LinkedIn Reactions - [Author] Post"
7. Re-fetch records with emails, save to ./output/apollo_contacts.json

Note: Since reactions scraper already provides company/title/location, Apollo enrichment here is primarily for EMAIL addresses. Match rates may be slightly different than comment scraper since we have richer input data for matching.

Warning: Apollo has a stale cache bug. After bulk match, always re-fetch from Airtable to get the latest data rather than using the match response directly.

Checkpoint: Report: "Found emails for X out of Y people (Z% match rate). X contacts ready for outreach."

---

Phase 4: Write (Skill: cold-email-sequence)

Input: User context (product, social proof, trigger), contact count Output: {campaign-name}-sequence.md

This is the interactive step. Work with the user one email at a time.

1. Gather inputs from user:

   • Trigger: What the LinkedIn post was about (the hook)
   • Product/offer: What we're selling
   • Social proof: Results, case studies, numbers
   • CTA: What's the ask (usually "I'm in" reply)
   • Sender name: Who it's from

2. Draft Email 1 (Day 0 -- Opener)

   • Present to user, refine, lock

3. Draft Email 2 (Day 3 -- Follow-up)

   • Present to user, refine, lock

4. Draft Email 3 (Day 7 -- Breakup)

   • Present to user, refine, lock

5. Save complete sequence to {campaign-name}-sequence.md

James Shields Framework Rules:

• Personalize SUBJECT (reference the post), not the body
• 3 sentences max + PS line
• Low-friction CTA ("just reply 'I'm in'")
• Irresistible offer in PS
• No em dashes (use commas or periods)
• No exclamation points
• No bold/italic/formatting
• No "Hi [Name]" opener -- jump straight in
• NEW social proof per email (never repeat across emails)
• Subject line references the specific post/author they reacted to

Checkpoint: "Sequence locked. Ready to load into Instantly."

---

Phase 5: Launch (Skill: instantly-campaign)

Input: Sequence file, ./output/apollo_contacts.json, Instantly API key Output: Paused Instantly campaign with all leads loaded

1. Get/confirm Instantly API key from the INSTANTLY_API_KEY environment variable (see ../SETUP.md for how to configure). Never hardcode the key in this file.
2. List connected email accounts via GET /api/v2/accounts
3. Create campaign:
   • Name: "[Author] Post Reactions - [Product] Outreach"
   • Timezone: America/Vancouver (Pacific w/ DST). Instantly rejects America/Los_Angeles; Vancouver is the safe substitute. See the instantly-campaign skill for the full timezone workaround table.
   • Schedule: Mon-Fri, 8:00 AM - 5:00 PM
   • Daily limit: 25 per account
4. Add 3 email steps with correct delay logic:
   • Email 1: Day 0 (delay: 0)
   • Email 2: Day 3 (delay: 3 -- relative to Email 1)
   • Email 3: Day 7 (delay: 4 -- relative to Email 2, NOT 7)
5. CRITICAL: Sanitize all email bodies -- replace every & with + or "and"
   • The Instantly API silently drops the entire body if it contains &
   • No error returned -- body just becomes empty string
   • Test by reading back campaign after creation to verify bodies are non-empty
6. Bulk load leads (up to 1000 per call)
   • Map from ./output/apollo_contacts.json:
     • email → email
     • firstName → first_name
     • lastName → last_name
     • companyName → company_name
7. Attach all sending accounts to the campaign
8. NEVER auto-activate. Leave campaign status = 0 (paused)

Final report to user:

• Campaign name and ID
• Status: PAUSED
• Number of leads loaded
• Sending accounts attached (list them)
• Schedule: Mon-Fri, 8-5 Pacific
• "Review in your Instantly dashboard. Say 'activate' when ready to launch."

---

Common Mistakes

Phase	Common Error	Recovery
1. Scrape	Only 10 results returned	Default maxItems is 10. Always set 500+.
1. Scrape	Post returns 0 results	Post may be private/deleted. Try urn:li:activity:ID format.
1. Scrape	profileScraperMode was "short"	Re-run with "main". Short mode misses experience/education.
2. Store	Airtable batch limit exceeded	Split into chunks of 10 records per create_record call.
2. Store	Token limit on list_records	Save to file, parse with Python.
3. Enrich	Low match rate (<40%)	Normal for some audiences. Proceed with what's available.
3. Enrich	Stale cache missing emails	Re-fetch from Airtable after enrichment, not from Apollo response.
4. Write	User wants major rewrite	Start email from scratch. Don't patch.
5. Launch	Empty body in Instantly	Ampersand bug. Remove ALL & characters from bodies.
5. Launch	Timezone rejected	Use America/Dawson (Pacific) or America/Detroit (Eastern).
5. Launch	v1 endpoint auth failure	Use v2 endpoints only (/api/v2/... with Bearer token).

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Timing Expectations

Phase	Duration	Notes
1. Scrape	3-8 minutes	profileScraperMode: "main" takes longer than comment scraping
2. Store	3-10 minutes	Parallel agent loading, depends on record count
3. Enrich	5-15 minutes	Apollo calls + Airtable updates
4. Write	10-30 minutes	Interactive with user -- cannot be rushed
5. Launch	2-5 minutes	API calls to Instantly
Total	~30-70 minutes	Slightly longer than comments pipeline due to richer scrape

---

Data Flow

LinkedIn Post URL
    -> Apify: harvestapi/linkedin-post-reactions (profileScraperMode: "main")
    -> ./output/linkedin_reactors.json (all reactors with full profiles)
    -> Airtable: new table "LinkedIn Reactors - [Author]" (22 fields)
         (pre-filled: name, headline, company, title, city, state, country,
          about, experience, education, skills, connections, followers,
          reaction type, company LinkedIn URL, profile JSON backup)
    -> Apollo: bulk people match (batches of 10) -- primarily for EMAILS
    -> Airtable: updated with emails + Apollo Match flag
    -> Apollo: contacts created with label "LinkedIn Reactions - [Author] Post"
    -> ./output/apollo_contacts.json (email-verified leads)
    -> Sequence file: {campaign-name}-sequence.md (3 emails, James Shields framework)
    -> Instantly: campaign + leads + accounts (API v2)
    -> PAUSED campaign ready for user review

---

Output

At completion, the user has:

1. Airtable table -- all reactors with full profile data + email enrichment
2. Apollo contacts -- under named label, ready for CRM workflows
3. Email sequence file -- 3 locked emails in workspace
4. Instantly campaign (PAUSED) with:
   • 3 email steps loaded (bodies verified non-empty)
   • All leads with emails loaded
   • All sending accounts attached
   • Mon-Fri 8-5 Pacific schedule
   • Ready to activate on user command

---

Quick Reference

Phase	Sub-skill	Input	Output
1. Scrape	linkedin-reaction-scraper	LinkedIn post URL	./output/linkedin_reactors.json
2. Store	airtable-lead-loader	Reactors JSON + Base ID	Airtable table populated (22 fields)
3. Enrich	apollo-enrichment	Airtable Base ID + Table ID	Emails added, ./output/apollo_contacts.json
4. Write	cold-email-sequence	User context (offer, proof, CTA)	{campaign-name}-sequence.md
5. Launch	instantly-campaign	Sequence file + Apollo contacts JSON	Paused Instantly campaign

---

Example

Input: https://www.linkedin.com/posts/jane-doe_tax-strategy-activity-1234567890

Walkthrough:

1. Scrape -- run linkedin-reaction-scraper with maxItems: 500, profileScraperMode: "main". Result: 287 unique reactors saved to ./output/linkedin_reactors.json with full experience, education, skills, and location.
2. Store -- create Airtable table LinkedIn Reactors - Jane Doe with 22 fields. Batch-load via 4 parallel agents. Result: 287 records, 268 with company data, 251 with location.
3. Enrich -- run Apollo bulk match in batches of 10. Result: 184 emails found (64% match rate), Apollo contacts created under label LinkedIn Reactions - Jane Doe Post, refreshed records exported to ./output/apollo_contacts.json.
4. Write -- collect inputs (trigger: tax strategy post, offer: outsourced tax ops, proof: "saved Acme 18 hrs/week"), co-author Email 1, 2, 3 with the user, save to jane-doe-reactions-sequence.md.
5. Launch -- create Instantly campaign Jane Doe Post Reactions - Tax Ops Outreach, timezone America/Vancouver, schedule Mon-Fri 8am-5pm, daily limit 25/account, sanitize bodies (strip &), bulk-load 184 leads, attach all sending accounts, leave status = 0.

Final report: "Campaign Jane Doe Post Reactions - Tax Ops Outreach (cmp_abc123) is PAUSED with 184 leads loaded across 3 sending accounts. Mon-Fri 8-5 Pacific. Review in Instantly and say activate when ready."