Systems Lab

Agent skill

email-waterfall

Use when a list has people (first name, last name, company domain, LinkedIn URL) but missing or unverified work emails, before anything is uploaded to Smartlead; when a list builder (Blitz, Prospeo, competitor-engagers, disco-like, Google Maps) returned rows with an empty email column; when coverage from a single provider is stuck around 40%; or when someone wants to validate emails with MillionVerifier.

activeNeeds a keyActs undeclared1,106 words

Filed under Prospecting and list building.

From automatewithuday/GTM-Skills · 59 skill entries · 1 · pushed 2026-11-01

What it does when it runs

Use when a list has people (first name, last name, company domain, LinkedIn URL) but missing or unverified work emails, before anything is uploaded to Smartlead; when a list builder (Blitz, Prospeo, competitor-engagers, disco-like, Google Maps) returned rows with an empty email column; when coverage from a single provider is stuck around 40%; or when someone wants to validate emails with MillionVerifier. Also use when someone is about to guess emails from a first.last@domain pattern, which this skill refuses to do.

Automated analysis of the skill and the 5 files bundled beside it. A skill’s own description is written to be selected by an agent, so it describes the job and not the dependencies.

Keys and connectors you must supply
  • APIFY_API_TOKEN
  • GETLEADS_API_KEY
Hosts it reaches
  • api.apify.com
  • app.getleads.io
  • code.deepline.com
  • linkedin.com
  • www.linkedin.com
Tool permissions it declares
No allowed-tools in the frontmatter. It does act, so it runs under whatever permissions your session already grants.
Actions present in the files
shellwrites filesnetwork

Ask about email-waterfall

Opens your assistant with this page's recorded context. Check the linked sources before relying on it.

Is this safe to install?ClaudeChatGPT
Adapt it to my stackClaudeChatGPT
What else do I need for it to workClaudeChatGPT
Rather ask a human? Talk to Cheetah
git clone --depth 1 --filter=blob:none --sparse https://github.com/automatewithuday/GTM-Skills.git /tmp/GTM-Skills
git -C /tmp/GTM-Skills sparse-checkout set "skills/email-waterfall"
mkdir -p ~/.claude/skills/email-waterfall
cp -R "/tmp/GTM-Skills/skills/email-waterfall/." ~/.claude/skills/email-waterfall/

Picked up without a restart. A project skill of the same name is shadowed by your personal one. For one repository only, swap ~/.claude/skills for .claude/skills. Claude Code docs ↗

Or take the whole library

This repo ships a .claude-plugin manifest, so Claude Code can install all 59 skills at once. Plugin skills are invoked as /<plugin>:<skill>, so they never collide with your own.

/plugin marketplace add automatewithuday/GTM-Skills
/plugin

The folder is the same in every client that implements the format — 46 of them — so if yours is not above, only the destination changes.

Before you install: this skill will not complete its job on a bare agent. It needs APIFY_API_TOKEN, GETLEADS_API_KEY, which you have to obtain separately.

Reproduced in full from automatewithuday/GTM-Skills/blob/873312e45703fe366c5e2bc16d6d2ebebeeb877e/skills/email-waterfall/SKILL.md, which is licensed MIT (skill frontmatter). 1,106 words, 8 headings.

Email Waterfall

Finds a verified work email for every row of a list by trying providers in order, cheapest first, and stopping at the first verified hit. It never builds an email from a name pattern, it never pays twice for the same person, and anything it cannot verify is kept with its status so it can go to a LinkedIn track instead of a live email sequence.

How to apply this skill

When asked to fill in or verify emails for a list:

  1. Gather inputs: a CSV with first_name,last_name,company_domain,linkedin_url (extra columns are kept), the per-run spend cap in USD, and whether the GetLeads daily allowance covers the row count (GetLeads MCP get_fair_use, free).
  2. Run scripts/outbound/waterfall.py from the scripts/ folder, dry run first, then for real in batches of 150. Do not hand-pick emails, do not reorder providers by habit, and do not add pattern guessing.
  3. Report: the <out>.report.md numbers (% verified, % catch_all, % unknown, % not_found, hits per provider, spend), five sample rows per status, and which rows have domain_match=no. Only verified rows may go to Smartlead.

Do not: treat a catch-all or unknown address as sendable because the count looks small, or trust a verified email at a domain that is not the company on the list.

Setup

cd skills/email-waterfall/scripts
pip install httpx python-dotenv pyyaml        # or: uv run --with httpx --with python-dotenv --with pyyaml python -m outbound.waterfall ...
python -m outbound.common --selftest          # prints OK; no network
python -m outbound.waterfall --selftest       # prints OK; no network

Keys: GETLEADS_API_KEY in the repo .env (the scripts search upward for it). Deepline: deepline auth status must show a workspace; install from https://code.deepline.com/INSTALL.md. Credit prices are constants at the top of waterfall.py and common.py (DEEPLINE_CREDIT_USD, GETLEADS_CREDIT_USD); edit them to your plans so the cap is honest.

The waterfall

Default order, one line to change (PROVIDERS in waterfall.py, or --providers):

StepProviderInput it getsCostNotes
1GetLeads enrich/from-linkedinLinkedIn URL1 credit per hit, misses freerows without a LinkedIn URL skip it
2LeadMagic (via Deepline)first name, last name, domain0.34 Deepline credits per resultskipped for rows with no domain
3Prospeo (via Deepline)LinkedIn URL + name + domain0.55 credits per resultalso fills a blank company domain
4FullEnrich (via Deepline, bulk async)all fourvariable, ~0.35 credits observedpolled with the free get_result call

Verification: a provider that already returns valid/VERIFIED skips the verifier; everything else goes to MillionVerifier (0.06 credits), and a catch-all answer is re-checked once with BounceBan.

Stop rule per row, in this order:

verified   -> keep, stop
catch_all  -> keep with status, stop (a domain property: the next provider returns the same domain)
unknown    -> keep with status, stop
discard    -> drop the candidate (invalid, disposable, spam trap), try the next provider
exhausted  -> not_found

Output columns added to every input row: email, email_status, email_provider, verifier, mx_class, domain_match, miss_reason. Rows are never dropped; dedupe happens before any spend (LinkedIn URL first, then name + domain).

domain_match is yes when the email's domain is the company domain or shares its main label (clorox.com and thecloroxcompany.com match), no otherwise. A verifier proves the mailbox exists, not that it is your prospect; no rows are usually a namesake at another company or a person who has moved. Review them by hand. Observed on a real 10-row run: 2 of 9 hits before the explicit provider order fixed it, 1 of 10 after.

Steps

  1. get_fair_use on the GetLeads MCP. Stop and say so if the allowance cannot cover the rows.
  2. Plan: python -m outbound.waterfall ../../../leads.csv --out out/leads_enriched.csv --dry-run. A dry run writes only <out>.dryrun.csv and <out>.dryrun.report.md, never the real output, and spends nothing.
  3. Test on 10 rows for real, read the output, then continue.
  4. Real run: python -m outbound.waterfall <csv> --out out/leads_enriched.csv --batch 150 --cap-usd N. The cap is a total for the run; Deepline plays get a hard --max-credits derived from the remaining budget. A cap abort keeps every processed row and marks the rest cap_aborted. Re-runs replay cached calls at $0 (.cache/calls.jsonl, git-ignored, contains emails; delete it when a project ends).
  5. Read out/leads_enriched.csv.report.md. If not_found is above 30%, say so and ask before adding deepline-play (Deepline's own prebuilt waterfall, which picks its own vendors) as a last leg of --providers.
  6. Split: verified is the only set eligible for Smartlead. catch_all, unknown and not_found go to a LinkedIn-only track.
  7. Show the table and wait for approval before any upload (/cold-email-starter-kit or /smartlead-campaign-upload-public).

Quick reference

FlagMeaningDefault
--out PATHoutput CSV; report lands at <out>.report.mdrequired
--dry-runprint planned calls and estimate, spend nothing, write <out>.dryrun.* onlyoff
--cap-usd Ntotal spend cap for the run; aborts before the call that would exceed itrows × $0.15
--batch Nrows per batch150
--providers a,b,cwaterfall order from getleads,leadmagic,prospeo,fullenrich,deepline-playgetleads,leadmagic,prospeo,fullenrich
--selftestoffline self-check, prints OK

Measured on 10 real rows (Oct 2026): 10/10 verified, 7 by LeadMagic, 2 by GetLeads, 1 by Prospeo, $0.33 total; the earlier Deepline-picks-vendors run found 9/10 for $1.22 with two namesakes. The hits per provider line is the data for reordering.

Common mistakes

  • Guessing emails from a name pattern — the script refuses to; do not add it by hand.
  • Reordering providers by habit instead of by the hits per provider line — the provider that carries your market should run first.
  • Trusting domain_match=no rows — FullEnrich and Prospeo return a real mailbox for the person's current employer, which is not always the company on your list.
  • Skipping dedupe, so the same person is paid for twice.
  • Sending catch_all or unknown rows to Smartlead.
  • Running the full list before looking at the first 10 rows.
  • Setting --cap-usd from the estimate alone: real Deepline rows cost 1 to 5 credits each, and the hard cap now follows the remaining budget, so give the run the budget you actually mean.

Related skills

Files bundled with it

These load only when the skill asks for them, so they cost nothing until it runs.

Other skills for the same job

Different authors, same problem. Matched on the words in the skill name, across every library in the catalogue except this one.

Need help setting it up?

This page tells you what email-waterfall does and what it needs. Cheetah builds the agent setup it runs inside: data, CRM, sequencing and the guardrails.

Book a call →

The directory stays free. There is nothing gated behind this.

Build my GTM engine

Tell us what you want to improve. Fedor reviews the workflow and replies by email when there is a useful fit.

Fedor reviews every request personally. If there is a useful fit, he will reply by email.