apollo-lead-finder

name	apollo-lead-finder
description	Two-phase Apollo.io prospecting: free People Search to discover ICP-matching leads, then selective enrichment to reveal emails/phones (credits per contact). Creates Apollo lists. Deduplicates against Supabase.
tags	["lead-generation"]

Apollo Lead Finder

Two-phase Apollo.io prospecting: free People Search for lead discovery, then selective paid enrichment to reveal emails and phone numbers. Creates Apollo lists and contacts. Deduplicates against Supabase.

Key advantage over CrustData: Apollo People Search is free (no credits consumed). Credits are only spent when enriching contacts to reveal email/phone. This lets you search tens of thousands of leads at zero cost, review results, then selectively enrich only the best matches.

Prerequisites (One-Time Setup)

1. Apollo API Key

Get your API key from Apollo.io Settings > Integrations > API. Add to .env:

APOLLO_API_KEY=your-api-key-here

2. Supabase Project

Same Supabase project used by crustdata-supabase. The full schema lives in tools/supabase/schema.sql. This skill writes to the people table (dedup by linkedin_url UNIQUE constraint). Run python3 tools/supabase/setup_database.py if setting up fresh.

3. Verify Environment

Ensure .env has all three keys:

APOLLO_API_KEY=...
SUPABASE_URL=https://xxx.supabase.co
SUPABASE_SERVICE_ROLE_KEY=eyJ...

Phase 0: Intake

Ask the user these questions to build the Apollo filter config:

ICP Criteria

1. What job titles are you targeting? (e.g., "VP of Sales", "Head of Growth")
2. What seniority levels? Options: owner, founder, c_suite, partner, vp, director, manager, senior, entry
3. Company size (employee range)? Format: "51,200" "201,500" "501,1000" "1001,5000"
4. Geographic regions? (e.g., "United States", "San Francisco, California")
5. Industry/keyword tags? (e.g., "SaaS", "Software", "FinTech")
6. Any titles to exclude? (e.g., "intern", "assistant")
7. Should we create an Apollo list with these contacts? (default: yes)
8. How many results do you want? (test: 100, standard: 5,000, full: 50,000)

Map Answers to Config

Build the config JSON with Apollo's filter format:

{
  "client_name": "example-client",
  "search_config_name": "vp-sales-us-midmarket",
  "icp_segment": "sales-leaders",
  "apollo_filters": {
    "person_titles": ["VP of Sales", "Head of Sales", "Director of Sales"],
    "person_seniority": ["vp", "director"],
    "person_locations": ["United States"],
    "organization_num_employees_ranges": ["51,200", "201,500", "501,1000"],
    "q_organization_keyword_tags": ["SaaS", "Software"]
  },
  "enrichment_filters": {
    "exclude_titles_containing": ["intern", "assistant"]
  },
  "apollo_list_name_prefix": "example-sales-leaders",
  "create_apollo_list": true,
  "mode": "standard",
  "max_pages": 50
}

Available Apollo search filters:

• person_titles — job title keywords (array of strings)
• person_seniority — seniority levels: owner, founder, c_suite, partner, vp, director, manager, senior, entry
• person_locations — geographic locations (array of strings)
• organization_num_employees_ranges — employee count ranges, format "min,max" (e.g., "51,200")
• q_organization_keyword_tags — company keyword tags (e.g., "SaaS", "Software")
• person_not_titles — titles to exclude (array of strings)
• q_organization_name — organization name search
• organization_locations — company HQ locations

Save config:

skills/apollo-lead-finder/configs/{client-name}.json

Phase 1: Search (FREE)

python3 skills/apollo-lead-finder/scripts/apollo_lead_finder.py \
  --config skills/apollo-lead-finder/configs/{client}.json \
  --phase search \
  [--test] [--yes] [--dry-run]

What the free search returns

Apollo's api_search endpoint returns limited preview data: Apollo person ID, first name, obfuscated last name, title, company name, and boolean flags (has_email, has_phone). No LinkedIn URLs, emails, or full names — those require enrichment.

Pipeline Steps

Step 1: Build Apollo search payload — Map config filters to Apollo People Search format.

Step 2: Search page 1 — Get first 100 results + total_entries for total count.

Step 3: Paginate — Fetch remaining pages (100 per page, up to mode cap). Apply title filters.

Step 4: Save manifest — Write Apollo person IDs + preview data to a JSON manifest file for the enrich phase.

Step 5: Export preview CSV — Write search results to output/{client-name}-search-{timestamp}.csv (limited data — full data after enrichment).

Mode Caps

Parameter	Test	Standard	Full
Max pages	1	50	500
Max results	100	5,000	50,000
Search credits	0	0	0

Cost: FREE. People Search does not consume Apollo credits.

Database Write Policy

CRITICAL: Never upsert leads to Supabase without explicit user approval.

This skill writes to the people table during the enrich phase. The search phase is free and produces no database writes — but enrichment both costs credits AND writes to the database.

Required flow:

1. Run --phase search first (free) — review the search results and preview CSV
2. Present search results to the user: total matches, sample leads, title distribution
3. Get explicit user approval before running enrich phase
4. After enrichment, present the enriched results to the user before upserting to Supabase
5. Only upsert after the user confirms the results look good

The agent must NEVER pass --yes on a first run. The --yes flag is only for pre-approved automated runs.

If the user hasn't approved the upsert: Export the CSV and show sample results. Let the user review. Only proceed to upsert after they confirm.

Phase 2: Enrich (COSTS CREDITS)

python3 skills/apollo-lead-finder/scripts/apollo_lead_finder.py \
  --config skills/apollo-lead-finder/configs/{client}.json \
  --phase enrich \
  [--test] [--yes] [--limit N]

Pipeline Steps

Step 1: Load search manifest — Read the manifest JSON saved by the search phase. Contains Apollo person IDs.

Step 2: Connect to Supabase — Fetch existing LinkedIn URLs for dedup.

Step 3: Confirm credits — Display lead count and credit cost estimate. Wait for confirmation.

Step 4: Bulk enrich — Call /people/bulk_match with Apollo person IDs in batches of 10. Each match costs 1 credit. Returns full data: email, phone, LinkedIn URL, full name, location, company details.

Step 5: Dedup against Supabase — Filter out leads whose LinkedIn URLs already exist in Supabase.

Step 6: Present results to user — Show enriched sample leads (names, titles, companies, email coverage) and ask for explicit approval before writing to the database.

Step 7: Upsert to Supabase — Only after user approval. Insert net-new people with source='apollo', enrichment_status='complete'.

Step 8: Export CSV — Write enriched leads to output/{client-name}-enriched-{timestamp}.csv. Update manifest to remove enriched people.

Mode Caps

Parameter	Test	Standard	Full
Max enrichments	10	500	2,500
Credits used	10	500	2,500

Cost: 1 credit per contact enriched. Always run search first, review results, then selectively enrich.

Phase 3: Review & Refine

Present results:

• Total matching — how many profiles match the filters in Apollo
• New leads found — net-new profiles (after Supabase dedup)
• Apollo list — name and link to the list in Apollo
• Enriched — how many have emails revealed
• Email coverage — percentage of enriched leads with valid emails
• Top 10 leads — name, title, company preview

Common adjustments:

• Too broad — add more filters (seniority, employee range, keyword tags)
• Too narrow — broaden title list, remove location filters
• Low email coverage — some contacts genuinely have no known email; try enriching more leads
• Wrong ICP — adjust title include/exclude lists

Example Usage

Trigger phrases:

• "Search Apollo for [titles] at [industries]"
• "Find leads in Apollo matching my ICP"
• "Run Apollo search for [client]"
• "Enrich the Apollo leads from last search"

Test search (free, no DB writes):

python3 skills/apollo-lead-finder/scripts/apollo_lead_finder.py \
  --config skills/apollo-lead-finder/configs/example.json --phase search --test

Dry run (no API calls):

python3 skills/apollo-lead-finder/scripts/apollo_lead_finder.py \
  --config skills/apollo-lead-finder/configs/example.json --phase search --dry-run

Enrich only (after reviewing search results and getting user approval):

python3 skills/apollo-lead-finder/scripts/apollo_lead_finder.py \
  --config skills/apollo-lead-finder/configs/example.json --phase enrich --limit 100

Full search + enrich (only for pre-approved repeat runs):

python3 skills/apollo-lead-finder/scripts/apollo_lead_finder.py \
  --config skills/apollo-lead-finder/configs/example.json --phase both

Apollo API Reference

• People Search: POST https://api.apollo.io/api/v1/mixed_people/api_search — FREE, returns Apollo IDs + preview data (first name, title, org name, boolean flags). No LinkedIn URLs or emails.
• People Match (enrich): POST https://api.apollo.io/api/v1/people/match — 1 credit, reveals email/phone
• Bulk People Match: POST https://api.apollo.io/api/v1/people/bulk_match — up to 10 per request, 1 credit each
• Create List: POST https://api.apollo.io/api/v1/labels — create a named list
• Create Contact: POST https://api.apollo.io/api/v1/contacts — add person to Apollo CRM + optional list
• Auth: x-api-key: {APOLLO_API_KEY} header on all requests
• Rate limit: Varies by plan. Handle 429 with Retry-After header.
• Search Pagination: page param (1-indexed), per_page max 100