Apollo Lead Finder

Apollo Lead Finder

Two-phase prospecting that keeps the free-search / paid-enrich boundary strict: discover broadly for free, enrich narrowly for credits. Apollo is optional: with a key you get the recommended, higher-quality path; without one the skill degrades to a keyless LinkedIn-profile discovery + email pattern-guess and still produces leads.

When to use

• "Find VPs of Sales at US mid-market SaaS companies and get their emails."
• Any prospecting where Apollo's filter taxonomy (titles, seniority, employee range, geo, industry tags) expresses the ICP.

Workflow

Phase 1 — discover (free, no credits)

python3 ${SKILL_DIR}/scripts/apollo_search.py \
  --titles "VP of Sales,Head of Sales" \
  --seniorities vp,director \
  --employee-ranges "51,200" "201,500" \
  --locations "United States" \
  --keyword-tags saas \
  --exclude-titles "assistant,intern" \
  --num-results 1000 \
  --existing ${WORKSPACE}/known_contacts.csv \
  --output ${WORKSPACE}/discovery.json

Returns name, title, company, LinkedIn URL, location. Emails are not revealed yet. --existing dedups against a CSV of contacts you already have (by normalized LinkedIn URL) so you never spend credits re-enriching known people.

Keyless degrade (no APOLLO_API_KEY): run serp_search.py instead — it discovers LinkedIn profiles via a keyless web search and returns the same record shape (name/title/ company/linkedin_url, email blank). Lower precision/coverage than Apollo; the agent resolves name/company from result_title/snippet.

python3 ${SKILL_DIR}/scripts/serp_search.py \
  --titles "VP of Sales,Head of Sales" --keyword-tags saas \
  --locations "United States" --num-results 50 \
  --existing ${WORKSPACE}/known_contacts.csv --output ${WORKSPACE}/discovery.json

Step 2 — rank & select (you, the agent)

Read discovery.json and select only the best-fit winners to enrich — score by title match, seniority, company fit, and ICP signals. Write the winners to winners.json. This selection is the credit-saving step; do not enrich the whole set.

Phase 2 — enrich winners (costs credits)

python3 ${SKILL_DIR}/scripts/apollo_enrich.py \
  --input ${WORKSPACE}/winners.json \
  --limit 50 \
  --verify \
  --output ${WORKSPACE}/enriched.json

Reveals verified email/phone for the selected subset. Keyless degrade (no APOLLO_API_KEY): apollo_enrich.py pattern-guesses an email from name + company domain (first.last@domain) and MX-checks it — email_status: guessed, no phone, lower confidence. --verify uses MillionVerifier when MILLIONVERIFIER_API_KEY is set, else a keyless syntax + MX-record check (ok_syntax_mx / no_mx / invalid_syntax).

Step 4 — export

Deliver enriched.json as a table to the user and persist it (workspace file or Agent Teams channel attachment; optionally a contact-cache/Airtable/Supabase store).

Outputs

• discovery.json — full free discovery set.
• enriched.json — selected subset with email, phone, email_status, and (if --verify) email_verification.

Credentials / env

• Required: none. The skill runs keyless via serp_search.py (discovery) + apollo_enrich.py's pattern-guess (email).
• Optional:
  • APOLLO_API_KEY — if set → Apollo People Search + verified People Match enrichment (better quality/coverage, verified email + phone). If not → keyless serp discovery + email pattern-guess (default).
  • MILLIONVERIFIER_API_KEY — if set → MillionVerifier deliverability. If not → keyless syntax + MX-record check.
  • DROPCONTACT_API_KEY — email-finding fallback for contacts Apollo can't email.

Notes & edge cases

• Strict two-phase split: never enrich the full discovery set — only ranked winners.
• Dedup (Phase 1 --existing) before enrich so credits are never spent on known contacts.
• Apollo paginates 100/page; the script caps at --num-results and backs off on 429.
• Contacts Apollo can't email come back with email: "" (LinkedIn URL preserved), not dropped.
• Keyless path: pattern-guessed emails are best-effort (mark them guessed); verify before sending, and prefer the Apollo path when deliverability matters.