06 Resolution Email Person

Resolution - Email + Person

Turn {domain (required), optional person_name, optional job_title} into a verified, send-safe email.

The one principle that governs every decision here: bounce rate, not find rate, is the signal that matters. A found-but-undeliverable email is worse than no email - it burns sender reputation. So this skill optimizes for deliverable contacts at minimum credit cost, and it never spends enrichment credit without the user's explicit go-ahead.

domain is the one mandatory input across every tool. Everything else can be asked for, inherited from upstream, or derived.

Provider + auth setup

Each provider reads its API key from an environment variable - AIARK_API_KEY, PROSPEO_API_KEY, BLITZ_API_KEY, FINDYMAIL_API_KEY, ZEROBOUNCE_API_KEY - loaded however your host provides them (shell export, .env loader, secrets manager); never hardcode a key. The skill runs with as few as one key set: at startup, detect which keys are present, use only those rungs, and skip the rest (log which rungs were skipped and why). Read references/providers.md for endpoints, request shapes, billing rules, and rate limits before making any call.

• AI Ark - rung 0, conditional: runs only if AIARK_API_KEY is set. Two modes. (a) Passthrough: a row that already carries an AI Ark-verified email (source flag ai_ark/pre_verified, or the caller says the list is an AI Ark export) is accepted as-is - don't re-find what's already verified. (b) Finder: POST /people/export/single turns a LinkedIn profile URL or AI Ark person id (not name+domain) into a BounceBan-verified email - 1 credit per valid email, 0 on a miss. Strong on SMB operators where Prospeo is weak. The POST /people search bills separately (~0.5 credit per returned profile), so keep searches tightly filtered and prefer /people/export/single.
• Prospeo - title-first finder, strong on mid-market + enterprise. Charged only when a request returns results (misses are free).
• Blitz - domain-first finder, strong on SMB. Free on Blitz's Unlimited plan (verify your own plan before treating it as free). Two steps: domain-to-linkedin → employee-finder (native job_level/job_function filters); returns the person but not the email.
• Findymail - email finder by name+domain (POST /api/search/name - NOT /api/find, which 405s; see references/providers.md).
• ZeroBounce - independent email validation (GET /v2/validate). If its key isn't set, fall back to the finding provider's bundled verdict. Explicit catch-all status (no false "ok" on catch-all domains), and unknown results consume no credit.

Shape of a run

Classify the input, dedupe by domain, show the estimation gate, then waterfall, validate, and emit records. The two gates below are the only fixed stops - everything else is judgment.

Input. Three shapes arrive: a domain + job title (confirm the title and how many people they want - don't guess the count), a domain + person name (require the full name; several people sharing it → return them all, noted), or a bulk file. Bulk JSONL from an upstream skill (01–04) is the chain standard - inherit its fields and ask only for genuine gaps. CSV/Excel: read the headers, inspect the actual cell data, remap any mislabeled columns and report what you remapped - a mislabeled column wastes credit on garbage, so reason about messy data before spending anything. references/input-handling.md has the full normalization table.

A missing domain can be derived from an email (everything after @) or a URL - flag the record derived. A personal LinkedIn profile URL (linkedin.com/in/…) is itself a person identifier and bypasses the domain requirement (resolve via Blitz email-enrichment or Findymail); a LinkedIn company URL is not - extract the domain from it instead.

Dedupe. Before anything costly, normalize each domain (lowercase; strip scheme, www., paths, and sub-domains like careers.acme.com → acme.com), keep one record per domain, and report N duplicates → M unique. The waterfall runs once per unique company. Also check prior runs/ folders for an already-resolved domain/person - never pay twice for the same contact.

Gate 1 - Estimation (mandatory, before any spend)

Run cheap count/preview queries (Prospeo's search returns matches without charging on a miss) and show, before spending any enrichment credit:

1. People found for the requested titles
2. People not found
3. Closest (similar-title) people available for the not-found
4. How many have emails
5. How many have LinkedIn
6. Credit + $ cost to enrich all
7. Companies with zero contacts
8. Coverage % (found ÷ requested)

Then get an explicit yes before enriching. Never auto-proceed - this gate is what makes the skill safe to point at a 5,000-row list.

Gate 2 - Validation-retry (optional, opt-in)

After enrichment is approved, offer one more opt-in: if a found email validates as invalid, re-run resolution through a different provider to find a working one. Make clear it costs more than usual, but don't run a second estimate pass (that would itself burn time/credit). Declined → validation still runs, bad emails are just flagged. Accepted → the retry loop in the validation step below is live.

Waterfall enrichment

Order (cost-first): AI Ark → Prospeo → Blitz (local title filter) → Findymail. Stop the moment you land a valid email (found-but-invalid is not a stop condition). Skip any rung whose key is absent.

Rung 0 - AI Ark (only if AIARK_API_KEY is set): passthrough rows return valid with email_source: ai_ark and skip the paid rungs entirely; otherwise resolve via /people/export/single with a LinkedIn profile URL (often already on the row, or free from Blitz's employee-finder) or person id. If you must find the person with AI Ark's search, pin it tight (domain + full name) - search bills per returned profile - and respect the per-run credit cap in references/providers.md. Worth trying early for SMB-operator targets.

Exact first, then broaden - capped at 3 attempts per company

Try the exact title strictly first; broaden to similar titles only on a miss. Misses are free on Prospeo, so trying is cheap - the cap keeps big lists from crawling.

| Attempt | Prospeo match_mode | Titles to send | Label a hit as | |---------|----------------------|----------------|----------------| | 1 | EXACT (optionally smart_intensity: STRICT) | the exact title (bundle all spellings in the include array - one call) | exact | | 2 | SIMILAR | + same-seniority synonyms | closest | | 3 | CONTAINS (optionally smart_intensity: LOOSE) | a wider same-seniority set | closest |

Use EXACT only on attempt 1. person_job_title.include takes an array, so all format variants of one logical title ("VP Marketing", "VP of Marketing", "Vice President, Marketing") go in a single call - they don't cost separate attempts.

The same-seniority guard (this is the crux of "closest")

When you broaden, the search can hand back a different-seniority role. Pin the requested band at query time - both finders have a native lever: Prospeo's person_seniority (bands: Founder/Owner, C-Suite, Partner, Vice President, Head, Director, Manager, Senior, Entry, Intern) and Blitz's job_level (C-Team/VP/Director/…) - so out-of-band people never come back.

• ✅ same person: "Business Development Director" ≈ "Head of Business Development" (both Director/Head band)
• ❌ different person: "CTO" (C-Suite) ≠ "Director of Technology" (Director) - different band, rejected at query time

Only for results that arrive without a seniority field (a local title refine, or Findymail) do you apply the band check manually, dropping any out-of-band person before accepting them as closest. This guard is why broadening stays safe: you never email a junior when they asked for a chief, even though a looser title match might offer one.

Per-tool shaping

• Prospeo is title-first - send company.websites + person_job_title + person_seniority together (free on a miss).
• Blitz is domain-first but keys on the company LinkedIn URL: domain-to-linkedin → employee-finder with job_level + job_function filters. It returns the person + LinkedIn but not the email - resolve the email separately (Blitz email-enrichment by LinkedIn, or Prospeo/Findymail by name+domain).
• Findymail resolves the email by name+domain.
• If they asked for 1 and a rung returns several: prefer exact title → most senior → highest confidence.

Validate, then be honest about the verdict

Validate every obtained email (ZeroBounce, or the provider's bundled verdict) - validation always runs, and no email is ever presented as confirmed without it.

• valid → done. Return it.
• catch_all / risky → accept but flag as catch_all (catch-alls are often the real address; don't throw away a likely-good contact - and don't upgrade the flag to valid).
• spamtrap / abuse / donotmail (ZeroBounce-specific) → never email; treat as hard invalid and record the sub_status in note - hitting a spamtrap is worse than a bounce.
• hard invalid → if Gate 2 was accepted, re-run resolution excluding the provider that produced the bad email (naturally capped at the number of providers). Otherwise return it flagged invalid - never pass it off as usable.
• all providers exhausted, still invalid → return no_email. Do not invent a generic info@/owner@.

Accuracy boosters

• Email-pattern inference: one verified email at a domain teaches you the company's pattern (first.last@, flast@). Generate candidates from it for the people the providers missed, and validate them - "not found" becomes "found + verified" cheaply. Label the source pattern_inferred.
• LinkedIn freshness check: before trusting a closest match, sanity-check the person's current LinkedIn title against the requested one (finder caches go stale). Role changed → flag it in note rather than emailing the wrong person.

Output contract

Return one record per contact:

| Field | Values | |-------|--------| | company, input_domain | - | | person_name, linkedin_url | - | | requested_title, matched_title | - | | match_type | exact \| closest | | email | resolved address (empty if no_email) | | email_source | ai_ark \| prospeo \| blitz \| findymail \| pattern_inferred | | verification_status | valid \| catch_all \| invalid \| no_email | | confidence | 0–100 (combine verdict + match_type + freshness) | | note | optional flags - stale title, derived domain, etc. |

When this skill runs in the chain, add verification_status - do not overwrite the upstream confidence field.

Write results as records.jsonl under runs// per _shared/CONVENTIONS.md (CSV is an optional, derived human export). End with a run summary: companies in · unique after dedupe · processed · found / not found · coverage % · breakdown by match_type and email_source · any rows skipped, with the reason - a row you couldn't process is reported, never silently dropped.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Zevenue
• Source: Zevenue/headless-gtm
• License: MIT
• Homepage: https://zevenue.com

Install and usage instructions live in the source repository linked above.