Job Change Signal Tracker

Job Change Signal Tracker

Track job changes for key contacts, research their new companies, and generate warm re-engagement outreach based on your prior relationship.

Instructions

When a user wants to track whether contacts have changed jobs, enrich each contact to detect role or company changes, research the new organization, and produce re-engagement materials with dynamic fields.

Steps

1. Gather tracking inputs by asking the user:

    1. "Who do you want to track?" Accept any of the following input types. Multiple contacts can be provided at once:
    - **LinkedIn URL** (e.g., `linkedin.com/in/username`)
    - **Email address** (e.g., `jane@acme.com`)
    - **Name + Company** (e.g., "Jane Smith at Acme Corp")
    - **Amplemarket lead list name** (e.g., "Q4 Closed Won Champions")
   

   - 1. "What's the context?" Determine the relationship type. This shapes the re-engagement tone and suggested opener: - Former champion - warm, familiar tone referencing shared wins - Closed-won contact - gratitude-based tone referencing successful partnership - Engaged prospect - professional tone referencing prior conversations - Customer contact - service-oriented tone referencing ongoing relationship - 1. "What was their expected company?" The company where the user last interacted with each contact. This baseline is used to detect whether a change has occurred. If a lead list is provided, the expected company is inferred from the list data.

2. Retrieve contacts from lead list (if applicable). If the user provided a lead list name, call mcp__claude_ai_Amplemarket__list_lead_lists to find the list, then call mcp__claude_ai_Amplemarket__get_lead_list with the list id to retrieve all contacts and their associated company information. Use the company stored in the list as the expected company for each contact.

3. Enrich each contact by calling mcp__claude_ai_Amplemarket__enrich_person for every contact with the best available identifier:

   • linkedin_url if provided
   • email if provided
   • name + company_name or company_domain if provided
   • Set reveal_email to true to obtain current contact details at the new company.

4. Compare current data against expected. For each contact, compare the enriched current company and role against the expected company provided in step 1. Flag a job change when:

   • The current company name or domain differs from the expected company
   • The person's title or department has significantly changed even within the same company (internal move)
   • The enrichment returns no data at the expected company (possible departure)

   Classify each contact into one of four categories:

   • Job Change Detected - contact is now at a different company
   • Internal Move Detected - same company but different role, title, or department
   • No Change - contact remains at the expected company in the expected role
   • Unable to Determine - enrichment data is inconclusive or contact not found

   When comparing company names, strip common suffixes (Inc., LLC, Ltd., Corp., Co.) and compare root domains to avoid false positives from naming variations.

5. Research new companies for contacts with detected job changes:

   • Call mcp__claude_ai_Amplemarket__enrich_company with the new company's domain to get firmographics, size, industry, and tech stack.
   • Call WebSearch to find recent news about the new company (funding rounds, product launches, leadership changes, acquisitions). Use a query like "[Company Name] news [current year]".
   • Combine both sources into a new company profile that includes:
     • Company overview (name, domain, industry, size, headquarters)
     • Growth signals (recent funding, hiring trends, product launches)
     • Relevance to the user's product or service

6. Find additional stakeholders at the new company by calling mcp__claude_ai_Amplemarket__search_people with:

   • company_domains: [new company domain]
   • person_seniorities: ["C-Suite", "VP", "Head", "Director"] or seniorities matching the user's ICP
   • full_output: true
   • page_size: 10
   • This identifies potential multi-threading targets alongside the moved contact.

7. Generate re-engagement dynamic fields and draft outreach. For each contact with a job change:

   • Populate all {{jobchange_*}} dynamic fields (see Dynamic Fields Generated below).
   • Draft a re-engagement message that follows this structure:
     • Opening: Congratulate the move and reference the past relationship
     • Bridge: Connect a recent new company event or challenge to your value proposition
     • Ask: Propose a low-commitment next step (quick call, resource share)
   • Tailor tone based on the relationship context from step 1:
     • Champion = warm and familiar, reference specific wins you achieved together
     • Closed-won contact = gratitude-based, reference the successful partnership
     • Engaged prospect = professional and helpful, reference prior conversations or interest
     • Customer contact = service-oriented, reference ongoing value delivered

8. Offer to create a lead list at the new companies. Ask the user: "Would you like me to create a lead list with these contacts at their new companies, including the job change context?" If yes:

   • Call mcp__claude_ai_Amplemarket__create_lead_list with a descriptive name (e.g., "Job Changes - Q1 2026 Champions").
   • Call mcp__claude_ai_Amplemarket__add_leads_to_lead_list to add the changed contacts using their enriched data at the new company.
   • Include any additional stakeholders found in step 6 if the user wants to multi-thread into the new organization.
   • Provide the final list URL and contact count for confirmation.

Important Notes

• Enrichment data reflects the most recent snapshot available in Amplemarket's database. Very recent job changes (within the last 1-2 weeks) may not yet be captured. If a change is suspected but not confirmed, flag it as "Unable to Determine" and suggest the user verify via LinkedIn.
• Batch enrichment consumes Amplemarket credits for each contact enriched. For large lead lists (50+ contacts), inform the user of the approximate credit cost before proceeding and suggest processing in smaller batches if needed.
• When comparing company names, normalize for common variations (e.g., "Stripe, Inc." vs "Stripe", "The New York Times" vs "NYT") to avoid false positives.
• Internal moves (same company, different role) are worth flagging separately. A contact promoted to a decision-making role can be a stronger opportunity than before.
• If a contact cannot be found via enrichment at all, they may have left the workforce, moved to a private company, or changed their public profile. Flag these as "Unable to Determine" rather than "No Change."

Dynamic Fields Generated

These fields are populated for each contact where a job change is detected. Use them in outreach templates or pass them to sequence tools.

Usage note: Fields are left blank for contacts classified as "No Change" or "Unable to Determine." For internal moves, {{jobchange_new_company}} and {{jobchange_previous_company}} will be the same, while {{jobchange_new_role}} and {{jobchange_previous_role}} will differ.

Examples

Example 1: Single Champion Tracking

User prompt: "Check if Sarah Chen changed jobs. She was our champion at Notion. Here's her LinkedIn: linkedin.com/in/sarahchen-sales"

What the skill does:

1. Identifies input: LinkedIn URL, context = champion, expected company = Notion.
2. Calls mcp__claude_ai_Amplemarket__enrich_person with linkedin_url: "https://linkedin.com/in/sarahchen-sales", reveal_email: true.
3. Enrichment returns: Sarah Chen is now VP of Revenue at Rippling (domain: rippling.com). Job change detected.
4. Calls mcp__claude_ai_Amplemarket__enrich_company with domain: "rippling.com".
5. Calls WebSearch with query "Rippling news 2026" to find recent company developments.
6. Calls mcp__claude_ai_Amplemarket__search_people with company_domains: ["rippling.com"], person_seniorities: ["C-Suite", "VP", "Director"], full_output: true.
7. Generates dynamic fields and re-engagement draft.

Example output:

JOB CHANGE DETECTED

New Company Profile

Additional Stakeholders at Rippling

Suggested Re-Engagement:

"Hi Sarah, congratulations on the move to Rippling. VP of Revenue is a well-deserved step up! I loved working with you at Notion and saw that Rippling just expanded global payroll to 12 new markets. As you scale the revenue org, I'd love to catch up on how we might support the growth. Worth a quick chat?"

Dynamic Fields:

• {{jobchange_person_name}} = Sarah Chen
• {{jobchange_previous_company}} = Notion
• {{jobchange_previous_role}} = Director of Sales
• {{jobchange_new_company}} = Rippling
• {{jobchange_new_role}} = VP of Revenue
• {{jobchange_new_company_size}} = 1001-5000
• {{jobchange_new_company_industry}} = Human Resources Technology
• {{jobchange_new_company_domain}} = rippling.com
• {{jobchange_tenure_at_previous}} = ~2 years 4 months
• {{jobchange_time_at_new_company}} = ~3 months
• {{jobchange_relationship_context}} = Champion
• {{jobchange_new_company_news}} = Raised $200M Series E (Jan 2026); Global payroll expansion to 12 new markets
• {{jobchange_suggested_opener}} = Congratulations on the move to Rippling - VP of Revenue is a well-deserved step up!

Example 2: Batch Tracking from Lead List

User prompt: "Track job changes for everyone in my 'Q4 Closed Won Champions' lead list"

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__list_lead_lists to find the list named "Q4 Closed Won Champions".
2. Calls mcp__claude_ai_Amplemarket__get_lead_list with the list id to retrieve all contacts.
3. Enriches each contact via mcp__claude_ai_Amplemarket__enrich_person.
4. Compares enrichment results against the company stored in the lead list for each contact.
5. For contacts with changes: enriches new companies, searches for stakeholders, generates dynamic fields.
6. Returns a summary table and offers to create a new lead list.

Example output (abbreviated):

JOB CHANGE TRACKING SUMMARY - Q4 Closed Won Champions (8 contacts)

Summary: 3 job changes detected, 1 internal move, 3 no change, 1 unable to determine.

Credit usage: 8 person enrichments + 3 company enrichments = 11 credits consumed.

Detailed job change profiles and re-engagement drafts generated for Sarah Chen, Michael Torres, and Rachel Green. James Obi flagged for internal promotion follow-up.

"Would you like me to create a lead list with the 3 changed contacts at their new companies?"

Example 3: Contact Still at Same Company

User prompt: "Did Alex Rivera move companies? He was VP of Sales at Datadog. Email: alex.rivera@datadog.com"

What the skill does:

1. Identifies input: email + name, context not specified (defaults to general), expected company = Datadog.
2. Calls mcp__claude_ai_Amplemarket__enrich_person with email: "alex.rivera@datadog.com", name: "Alex Rivera", reveal_email: true.
3. Enrichment returns: Alex Rivera, VP of Sales at Datadog (datadog.com). No change detected.
4. Skips new company research and stakeholder search.

Example output:

NO JOB CHANGE DETECTED

Alex Rivera is still at Datadog as VP of Sales. No job change detected since your last interaction.

Next Steps:

• No re-engagement dynamic fields generated (no change occurred).
• If you want to re-engage Alex at Datadog, consider the outreach-personalization-research skill to build a tailored approach based on current company signals.
• To be notified of future changes, add Alex to a tracking lead list and re-run this skill periodically (monthly or quarterly recommended).

Troubleshooting