Enrich and Score Lead

Enrich a single prospect using any available identifier and produce a structured profile with an ICP fit score and recommended next actions.

Instructions

When a user provides a LinkedIn URL, email address, or name + company combination, enrich the person and their company, then synthesize the data into a scored profile.

Steps

1. Identify the input type provided by the user:

   • LinkedIn URL (e.g., linkedin.com/in/username)
   • Email address (e.g., jane@acme.com)
   • Name + Company (e.g., "Jane Smith at Acme Corp")
   • If insufficient info is provided, ask the user for at least one of the above.

2. Enrich the person by calling mcp__claude_ai_Amplemarket__enrich_person with the available identifiers:

   • linkedin_url if provided
   • email if provided
   • name + company_name or company_domain if provided
   • Set reveal_email to true and reveal_phone_numbers to true to get full contact details.
   • Credit note: Enabling reveal_email and reveal_phone_numbers consumes additional Amplemarket credits per reveal. Factor this in when enriching large batches.

3. Enrich the company by calling mcp__claude_ai_Amplemarket__enrich_company with:

   • domain from the person enrichment result, or
   • linkedin_url of the company if the domain is not available.

4. Calculate an ICP Fit Score (1-100) based on these weighted factors:

   • Seniority level (30 points): C-Suite/VP/Founder = 25-30, Director/Head = 18-24, Manager = 10-17, Senior/Entry = 1-9
   • Company size (25 points): Score based on how well the company size matches typical B2B SaaS buying capacity (201-5000 = highest)
   • Industry relevance (20 points): Technology, Financial Services, SaaS = highest; score other industries based on typical B2B fit
   • Data completeness (15 points): Email found = 8, Phone found = 7
   • Company signals (10 points): Growth indicators, funding, tech stack relevance

   If the user has previously stated their ICP criteria, adjust the scoring weights to match their specific requirements.

5. Format the output as a structured profile:

   Contact Card:

   • Full name, title, seniority, department
   • Email, phone (if revealed)
   • LinkedIn URL
   • Location

   Company Card:

   • Company name, domain, industry
   • Size, type, founded year
   • Headquarters location
   • Description/tagline
   • Tech stack (if available)

   ICP Fit Score:

   • Overall score (1-100) with letter grade (A/B/C/D/F)
   • Score breakdown by factor with reasoning

   Recommended Next Steps:

   • Specific outreach suggestions based on the score and profile
   • Whether to prioritize, nurture, or deprioritize

Important Notes

• Always attempt both person and company enrichment for the fullest picture.
• If the user has not defined their ICP, ask them before scoring: "What does your ideal customer look like? Industry, company size, seniority level, and any other criteria?" Only fall back to a general B2B SaaS baseline if the user explicitly asks you to proceed without specifying criteria.
• Be transparent about which data came from enrichment vs. inference.

Examples

Example 1: LinkedIn URL Enrichment

User prompt: "Enrich this lead: linkedin.com/in/johndoe-vpsales"

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__enrich_person with linkedin_url: "https://linkedin.com/in/johndoe-vpsales", reveal_email: true, reveal_phone_numbers: true.
2. Extracts company domain from the result.
3. Calls mcp__claude_ai_Amplemarket__enrich_company with the extracted domain.
4. Calculates ICP fit score.
5. Returns structured profile.

Example output:

Contact Card

Field	Value
Name	John Doe
Title	VP of Sales
Seniority	VP
Department	Revenue
Email	john.doe@techcorp.com
Phone	+1 (555) 123-4567
LinkedIn	linkedin.com/in/johndoe-vpsales
Location	San Francisco, CA

Company Card

Field	Value
Company	TechCorp
Domain	techcorp.com
Industry	Computer Software
Size	201-500 employees
Type	Privately Held
HQ	San Francisco, CA
Description	AI-powered sales automation platform

ICP Fit Score: 82/100 (A)

Factor	Score	Reasoning
Seniority	27/30	VP-level is a key decision maker
Company Size	22/25	201-500 is ideal for B2B SaaS
Industry	18/20	Software/Tech is a core ICP industry
Data Completeness	15/15	Both email and phone found
Company Signals	0/10	No specific growth signals detected

Recommended Next Steps:

• HIGH PRIORITY: This is a strong ICP fit. Proceed with personalized outreach.
• Suggested channel: Direct email to john.doe@techcorp.com
• Research TechCorp's current sales tech stack for a tailored value proposition.

Example 2: Email-Based Enrichment

User prompt: "Score this prospect: maria.garcia@finova.io"

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__enrich_person with email: "maria.garcia@finova.io", reveal_phone_numbers: true.
2. Calls mcp__claude_ai_Amplemarket__enrich_company with domain: "finova.io".
3. Calculates and returns scored profile.

Example 3: Name + Company Enrichment

User prompt: "What can you tell me about David Kim at Stripe?"

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__enrich_person with name: "David Kim", company_name: "Stripe", reveal_email: true, reveal_phone_numbers: true.
2. Calls mcp__claude_ai_Amplemarket__enrich_company with domain: "stripe.com".
3. Calculates and returns scored profile.

Customizing Your ICP Score

The default scoring weights (Seniority 30%, Company Size 25%, Industry 20%, Data Completeness 15%, Company Signals 10%) work well for mid-market B2B SaaS. Adjust them when your ICP differs:

• Selling to startups? Increase Company Signals weight (funding stage, growth rate) and decrease Company Size weight. A 20-person Series A with strong growth signals is more valuable than a 500-person company with no momentum.
• Selling to enterprise? Increase Company Size weight and add a penalty for companies under 1,000 employees. Seniority should weight Director+ more heavily since VPs are the typical entry point.
• Industry-specific product? Increase Industry Relevance to 30%+ and be strict about which industries score highly. A perfect-seniority contact at the wrong industry is a wasted touch.
• Selling contact data or enrichment? Decrease Data Completeness weight since it measures Amplemarket's coverage, not the prospect's fit.

Tell the user: "I'm using default ICP scoring weights. Want me to adjust for your specific selling motion?"

Troubleshooting

Problem	Solution
Person not found	Fallback chain: 1) Try LinkedIn URL if not already used. 2) Try company domain + full name. 3) Try search_people with company_domains + person_titles as a fuzzy match. 4) If still not found, inform user with suggestions: "Could not find this person. Try providing their LinkedIn URL or exact company domain."
Company enrichment fails	Fallback chain: 1) Try the company domain instead of name (or vice versa). 2) Try the LinkedIn company URL. 3) Try the parent company domain if this is a subsidiary. 4) If still failing, use whatever company data was returned in person enrichment and flag the gap.
Person enrichment succeeds but company enrichment fails	Use whatever company data was returned in the person enrichment (company name, domain). Present the person profile and note that company details are limited. Score with available data and flag the gap.
Company enrichment succeeds but person enrichment fails	Present the company profile and suggest alternative identifiers. Try searching for the person via search_people with company domain + title as a fallback.
Multiple people match the same name + company	Present all matches with titles and ask the user to confirm which person they mean. If LinkedIn URLs are available, show those for disambiguation.
Email not revealed	The contact may not have a verified business email. Suggest LinkedIn outreach instead.
Low data completeness	Combine multiple identifiers in the enrichment call for better match rates. If email is missing, suggest LinkedIn outreach. If phone is missing, note it in the profile and suggest email as primary channel. Always score with available data and flag which factors were impacted by missing data.
Score seems off	Ask the user to define their specific ICP criteria so scoring weights can be adjusted.