Contact Research — Vibe Prospecting Deep Profile
Given a list of people, match and enrich each one with Vibe Prospecting, then have Claude write a professional synopsis — who they are, what they do, their career trajectory, and what's relevant to know before engaging with them.
What you'll end up with:
- Verified contact info (work email, LinkedIn, direct phone) for each person
- A full professional profile: current role, work history, seniority, department, company context
- Recent career signals: job changes, promotions
- A 4–6 sentence professional synopsis per person, written by Claude in plain English
Good for: pre-call research, account intelligence, investor diligence on founders, recruiting, conference prep.
Step 0 — Credential Check (Claude: always do this first)
Cowork mode: confirm mcp__*__fetch-entities is in your tool list.
- ✅ Found → proceed
- ❌ Missing → tell the user: "Please connect the Vibe Prospecting connector from the connector store, then let me know." Wait. Do not continue.
Claude Code / terminal:
bashnpx @vibeprospecting/vpai@latest --help
- ✅ Lists tools → proceed
- ❌ Error → run:
npx @vibeprospecting/vpai@latest config --api-key "YOUR_VP_API_KEY"
Phase 1 — Prepare Your People List
Ask the user how they want to provide their list:
"How would you like to provide the people? You can: (A) paste names and companies directly in chat, (B) share a CSV file, or (C) describe who you're targeting and I'll find them from Vibe Prospecting's database."
Option A — Paste in chat
Claude converts the input into a structured CSV. Minimum needed per person: full_name + one of company_name, email, or linkedin.
Option B — CSV file
Accepted columns (use whatever you have — more is better for matching):
| Column | Description |
|---|---|
full_name | Full name (or first_name + last_name separately) |
email | Work or personal email |
linkedin | LinkedIn profile URL |
company_name | Current company name |
business_id | Vibe Prospecting business ID (skip match step if you have this) |
Save the file as people-input.csv.
Option C — Find them from scratch
If the user describes a persona (e.g. "CFOs at fintech companies in the US"), use fetch-entities with entity_type: prospects to build the list, then proceed from Phase 2. Sample 5 first before scaling.
bashnpx @vibeprospecting/vpai@latest fetch-entities \ --args '{ "entity_type": "prospects", "filters": { "job_level": {"values": ["c_suite"]}, "job_department": {"values": ["finance"]}, "linkedin_category": {"values": ["Financial Services"]}, "company_country_code": {"values": ["US"]}, "has_email": true } }' \ --number-of-results 5 \ --tool-reasoning '<user request verbatim>'
Phase 2 — Match to Vibe Prospecting IDs
Resolve each person to an Explorium prospect ID. This unlocks all downstream enrichment.
bashnpx @vibeprospecting/vpai@latest match-prospects --all-parameters # From CSV npx @vibeprospecting/vpai@latest match-prospects \ --file-path people-input.csv \ --schema '{"full_name":"full_name","email":"email","linkedin":"linkedin","company_name":"company_name"}' \ --tool-reasoning 'Match people list for deep contact research'
Note the session_id and table_name from the output.
On low match confidence: drop records with confidence < 0.7 and flag them for the user — they may need a cleaner input (e.g. adding LinkedIn URL or email helps significantly).
Phase 3 — Enrich Profiles and Contact Info
Pull the full professional profile for each matched person, plus their verified contact details.
bashnpx @vibeprospecting/vpai@latest enrich-prospects --all-parameters npx @vibeprospecting/vpai@latest enrich-prospects \ --args '{ "enrichments": ["contacts", "profiles"] }' \ --session-id <SESSION_ID> \ --table-name <TABLE_NAME_FROM_MATCH_STEP> \ --tool-reasoning 'Enrich profiles and contact info for research' \ --csv
What "profiles" gives you:
- Current job title, department, seniority level
- Full work history (company, title, dates)
- Education history
- LinkedIn URL
- Skills and areas of expertise
What "contacts" gives you:
- Verified work email
- Direct phone / mobile (when available)
- LinkedIn URL (confirmed)
Phase 4 — Fetch Career Signals
Pull recent career events for each person — job changes and promotions are the most useful signals for understanding where someone is and why they might be receptive to a conversation.
bashnpx @vibeprospecting/vpai@latest fetch-prospects-events --all-parameters npx @vibeprospecting/vpai@latest fetch-prospects-events \ --args '{ "event_types": ["job_change", "promotion"], "timestamp_from": "<12 months ago, format YYYY-MM-DD>" }' \ --session-id <SESSION_ID> \ --table-name <TABLE_NAME_FROM_ENRICH_STEP> \ --tool-reasoning 'Fetch career events for contact research' \ --csv
Save the combined enriched + events data as people-enriched.csv.
Phase 5 — Write Professional Synopses
Read people-enriched.csv and write a professional brief for each person. Each synopsis should be in plain, natural English — like a colleague briefing you before a meeting, not a LinkedIn bio.
For each person, write a 4–6 sentence synopsis covering:
- Who they are right now — current role, company, seniority. What they're responsible for.
- How they got here — 1–2 relevant prior roles that show their trajectory or expertise.
- What they know — domain expertise, industries they've worked in, notable skills or patterns across their career.
- What's changed recently — any job change or promotion in the last 12 months. If nothing changed, note their tenure in the current role.
- What's worth knowing — one insight that would be useful before engaging: a shared background, a relevant company pivot, a pattern in their career, or a company context signal (e.g. their company just raised, or is in a growth stage).
Tone: confident, specific, no filler. Avoid phrases like "passionate about", "seasoned professional", or "proven track record." Use the actual data.
Example synopsis:
Sarah Chen is the VP of Revenue Operations at Lattice, where she owns the full RevOps stack — CRM, pipeline reporting, territory planning, and sales tool evaluation. Before Lattice she spent four years at Salesforce, first as a sales ops analyst and then as a senior manager, which explains her strong bias toward CRM-native workflows. Her career has stayed tightly in the revenue/GTM ops lane, moving from execution roles into leadership over the last three years. She joined Lattice 14 months ago, likely as part of a post-Series D scaling push. Worth knowing: Lattice runs HubSpot, not Salesforce — so she made a deliberate switch when she moved, which suggests she's not locked into any one platform.
Phase 6 — Deliver the Output
Present results in two formats:
1. In-chat briefing (for small lists, ≤ 10 people)
Show each person as a card:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 👤 [Full Name] — [Title] at [Company] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📧 [work_email] 🔗 [linkedin_url] 📱 [phone if available] [Synopsis paragraph] Career signals: [any job change/promotion in last 12 months, or "No recent changes — [X] years in current role"] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
2. CSV export (for larger lists or when the user wants to save/share)
Write people-research.csv with columns:
| Column | Description |
|---|---|
full_name | Full name |
job_title | Current title |
company_name | Current company |
work_email | Verified work email |
linkedin_url | LinkedIn URL |
phone | Direct phone (if available) |
synopsis | Claude's professional synopsis |
career_signal | Most recent job change or promotion (if any, last 12 months) |
years_in_role | Approximate tenure in current role |
Full Checklist
[ ] Step 0 — Vibe Prospecting connected [ ] Phase 1 — People list prepared (pasted, CSV, or fetched) [ ] Phase 2 — Matched to Vibe Prospecting prospect IDs [ ] Phase 3 — Profiles and contact info enriched [ ] Phase 4 — Career signals fetched (job changes, promotions) [ ] Phase 5 — Professional synopses written by Claude [ ] Phase 6 — Delivered as in-chat cards and/or CSV export
Troubleshooting
| Problem | Solution |
|---|---|
| Vibe Prospecting tools missing | Connect from connector store; wait for mcp__*__fetch-entities to appear |
| Low match rate | Add LinkedIn URLs or emails to input — they're the strongest match signals. Names alone match poorly for common names |
| Profile data sparse | Not all profiles are equally populated. Flag thin records to the user and note what's missing |
| No contact info returned | Some people have limited public contact data. Note it in the synopsis card rather than leaving it blank |
| Career events empty | Person may not have changed jobs recently — note their tenure in current role instead |
| Synopsis feels generic | Claude needs data to work with — if enrichment returned little, flag the record rather than inventing details |
