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Apify Verified Email Finder

Organization
apify
apify-verified-email-finder

Builds a list of verified business emails from Google Maps, Google SERPs, or a user-supplied URL list. Verification happens inside the same Apify run — no third-party verifier needed. Use when user asks to find verified emails, build a leads list, scrape emails from Maps or SERP, verify emails for a URL list, or find an Apollo / Hunter alternative.

Overview

Publisherapify
Repositoryawesome-skills
Skill nameapify-verified-email-finder
Stars
281
Forks
92
Bundled files
8
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by apify on GitHub. Read the source before you install it.

Installation

Install the Apify Verified Email Finder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/apify/awesome-skills/tree/main/skills/apify-verified-email-finder
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/apify/awesome-skills.git /tmp/awesome-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-skills/skills/apify-verified-email-finder .claude/skills/apify-verified-email-finder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Apify Verified Email Finder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Apify Verified Email Finder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Apify Verified Email Finder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Verified Email Finder

Return a list of verified business emails by routing the user's input to the right Apify Actor and turning on the leads enrichment + email verification add-ons in a single run. No third-party verifier (Hunter, NeverBounce, Apollo) needed — verification happens inside the same Actor run.

Prerequisites

(No need to check it upfront)

The skill supports two execution paths. Pick the one that matches your environment — Steps 4 and 5 show commands for both.

MCP path (default in Claude sessions, recommended). If the Apify MCP server is connected, no setup is needed — auth runs through the user's Apify account. Use the call-actor and get-dataset-items MCP tools.

Script path (CLI / scheduled / non-Claude execution). Requires:

  • .env file with APIFY_TOKEN
  • Node.js 20.6+ (for native --env-file support)

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Collect the six required anchor inputs
- [ ] Step 2: Route to the correct Actor (confirm if ambiguous)
- [ ] Step 3: Build the Actor input (verification always ON)
- [ ] Step 4: Run the Actor and wait
- [ ] Step 5: Apply the result-scope filter, deduplicate, and render

Step 1: Collect the Six Required Anchor Inputs

Ask all six as one block before any Actor call. Don't bundle Actor-specific optional fields (country code, language, max pages) into this round — surface those as follow-ups.

  1. What do you have to start with? — location query / SERP keyword / URL list. This drives the routing decision.
  2. The actual input — the location string, the keyword(s), or the URLs themselves.
  3. Department filter — one or more of: c_suite, product, engineering_technical, design, education, finance, human_resources, information_technology, legal, marketing, medical_health, operations, sales, consulting. Default is any (leave the array empty), but ask every time.
  4. Max contacts per domain / business — passed as maximumLeadsEnrichmentRecords. Default 3, but ask every time.
  5. Output format — CSV or JSON. Ask every time.
  6. Result scope — which leads to keep in the deliverable. The Actor always runs the same way (verification always on); this only controls post-run filtering. Pick one:
    • verified-only (default) — only leads with emailVerification.result == "ok". Safest for cold email.
    • verified-plus-catchall — ok plus catch_all. Catch-all is often deliverable but unprovable.
    • all-emails — any lead with a non-empty email, regardless of verification.
    • with-phone — any lead with a non-empty phone number, regardless of email status. Use for call campaigns.
    • everything — every lead the Actor returned, even incomplete ones.

Step 2: Route to the Correct Actor

Inspect anchor #1 and pick the Actor.

User has to start withActor IDUse when
Location + business type ("dentists in Berlin")compass/crawler-google-placesLocal leads list from Maps listings; best when user wants address / phone / hours too
Keyword / search query ("best CRM software")apify/google-search-scraperContacts from whichever sites Google ranks for a topic
Pre-existing URL list (pasted, file path)vdrmota/contact-info-scraperUser already has domains; cheapest route since no discovery step

All three Actors share the same three add-on fields, so verification behavior is identical across routes.

Decision examples

User saysRoute
"Dentists in Munich" / "Lawyers in Prague"Maps
"Marketing contacts at the top results for 'AI agent builder'"Search
"Find emails for these 5 URLs: acme-co.example, demo-co.example..."URL list
"Find HR contacts at Fortune 500 companies"Ask: SERP for "Fortune 500 HR" or a URL list?
"Find contacts at SaaS companies in Berlin"Ask: Maps for "SaaS companies in Berlin" or SERP for "SaaS companies Berlin"? Maps works best when businesses are Google-Maps-listed.
(User pastes both a SERP keyword AND a URL list)Ask: run one route, the other, or both as separate deliverables?

Ambiguity rule: if anchor #1 is unclear, ask one follow-up before running. Never burn Actor compute on a guessed route.

Mixed deliverables: if the user explicitly asks for two routes in one deliverable, run both Actors and concatenate. The Source column makes the mix clear; dedupe by email across the combined output.

Step 3: Build the Actor Input

Always set these three fields, regardless of which Actor is selected.

FieldValue
maximumLeadsEnrichmentRecordsanchor #4 (default 3, min 1)
leadsEnrichmentDepartmentsanchor #3 as array, or [] if "any"
verifyLeadsEnrichmentEmailstrue (always) — guard rail, never set to false

Full per-Actor input parameters and example payloads are in reference/apify-actor-usage.md.

URL-list pre-validation: before submitting URLs to vdrmota/contact-info-scraper, parse each one and check it is http/https and parseable. Skipped entries must appear in the output as skipped — invalid URL, never silently dropped.

Step 4: Run the Actor

Maps and SERP runs with leads enrichment can take several minutes per query. Raise the timeout for large jobs.

MCP path (default in Claude sessions):

Call the call-actor tool:

  • actor: one of the three Actor IDs (compass/crawler-google-places, apify/google-search-scraper, vdrmota/contact-info-scraper)
  • input: the JSON payload from Step 3
  • callOptions: {"timeout": 1800, "memory": 4096} for a generous budget

The tool returns runId and datasetId. If status is still RUNNING, poll with get-actor-run (waitSecs up to 45) until SUCCEEDED. Capture both IDs for the run_metadata.json sidecar.

Script path (CLI / scheduled use):

bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_verified-emails.csv \
  --format csv \
  --timeout 900

Use --format json for JSON. The script writes the raw dataset to disk; Step 5 still applies the spurious-match + scope filters on top.

Step 5: Filter, Deduplicate, and Render

Pull the dataset:

  • MCP path: call get-dataset-items with the datasetId from Step 4. Use the fields parameter (e.g., title,searchString,countryCode,city,address,phone,website,leadsEnrichment) and clean: true to keep the response small. For datasets that still exceed the response cap, fetch directly via curl https://api.apify.com/v2/datasets/<id>/items?fields=...&clean=true and pipe through jq.
  • Script path: the raw dataset is already on disk in the file from Step 4.

Each record contains business fields plus a leadsEnrichment array (Maps, SERP) or top-level lead fields (URL list). Each lead has a departments array, a companyWebsite, and an emailVerification object with result (ok / invalid / disposable / catch_all / unknown / error) and quality (good / risky / bad).

  • Spurious-match filter (mandatory, always on). Apply this first, before any other filter. The lead-enrichment service can return global-fallback leads when no local match exists (real case observed: a single US-zoo CFO whose companyWebsite=zoo.org was attributed to 8 unrelated Polish zoos because the matcher latched onto the zoo substring). Drop any lead whose companyWebsite hostname doesn't equal the source URL's hostname (strip https?://, leading www., anything after /; lowercase). Count drops in run_metadata.json and call them out in the deliverable header if non-zero.

  • Filter by result scope (anchor #6). Applied second.

    ScopeRow-keep logic
    verified-onlyemailVerification.result == "ok"
    verified-plus-catchallemailVerification.result in {"ok", "catch_all"}
    all-emailsemail is non-empty (any result, including missing verification)
    with-phonephone (or company phone) is non-empty (regardless of email)
    everythingkeep every lead, no filter
  • Dedupe: group by lowercased email; keep the first occurrence and merge Source Query or URL if the same email appears from multiple sources. For with-phone rows that have no email, dedupe by lowercased phone instead.

  • Empty-result surfacing: if the department filter (anchor #3) produces zero leads for a given domain, include a row for that domain with Email = "" and Email Verification Status = "no leads matched filter". Do not silently drop it. This is separate from the result-scope filter above — empty-domain rows are inserted before scope-filtering and always shown.

Output row schema (16 columns, including Departments) and per-format rendering details are in reference/output-formats.md.

Worked Examples

Quality Rules (always enforce)

  • Guard rail: never submit a run with verifyLeadsEnrichmentEmails: false.
  • Provenance & traceability: populate the Source column on every row; carry Apify runId + datasetId in run_metadata.json.
  • No fabrication: missing dataset fields stay blank.
  • Deliverable header transparency: state the active result scope and the spurious-match drop count; offer to re-render under a different scope.
  • Ambiguity confirm: if anchor #1 is unclear, ask before running.

Cost & Pricing

Email verification is charged only for decisive results (ok / invalid / disposable); catch_all / unknown / error are free. Leads enrichment is charged per successfully extracted lead. Check the Apify console for live rates (they vary by subscription tier and change over time).

Error Handling

See reference/troubleshooting.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Apify Verified Email Finder AI skill do?

Builds a list of verified business emails from Google Maps, Google SERPs, or a user-supplied URL list. Verification happens inside the same Apify run — no third-party verifier needed. Use when user asks to find verified emails, build a leads list, scrape emails from Maps or SERP, verify emails for a URL list, or find an Apollo / Hunter alternative.

Why use Apify Verified Email Finder on TypingMind?

Because you install it once and use it with any model. Apify Verified Email Finder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Apify Verified Email Finder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/apify/awesome-skills/tree/main/skills/apify-verified-email-finder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Apify Verified Email Finder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Apify Verified Email Finder?

As many as you like. As long as a model supports skills, you can use Apify Verified Email Finder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Apify Verified Email Finder AI skill free?

Yes. It is published on GitHub by apify under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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