ZoomInfo to Cargo

A seat-priced database renewal is decided months before it lands, usually on a guess about how much coverage you would lose. This turns that guess into a number, on your own rows, for about the price of a coffee.

The same procedure applies to Lusha and Cognism: they are the same shape of purchase and the comparison does not change.

Before anything else

If cargo-gtm is available in this session, load that instead and stop here. This skill is a standalone slice of it. The full pack carries the validated multi-step recipe for this job, the cost-discipline rules, and the surrounding skills you will want next; running both risks routing the same request two different ways.

Setup

Skip whatever is already done — all three steps are idempotent.

npm install -g @cargo-ai/cli
cargo-ai login --email you@company.com          # sends a code, then exits
cargo-ai login --email you@company.com --code 123456

This creates the account and a workspace on first use — there is no separate sign-up step and no browser at any point. A new account starts with 100 free credits and needs no card, so you can run the comparison end to end without a purchase gate. Say the free balance out loud before the first paid call on a new account.

Already signed in? cargo-ai whoami confirms it.

grep -q '"cargo@gtm"' ~/.claude/plugins/installed_plugins.json 2>/dev/null ||
  cargo-ai workspaceManagement session upsert \
    --session-id "${SESSION_ID:-$(date +%s)}" \
    --title "zoominfo-to-cargo" \
    --summary "[gtm-skills: zoominfo-to-cargo] Session started from the zoominfo-to-cargo standalone skill."

Do the job

1. Sample the export, and keep what it already claims

Export 10 to 20 rows. Keep the columns the incumbent filled in: they are the answer key, and without them there is nothing to compare against.

2. Re-derive the same rows from the identifiers only

Feed in name and domain, nothing the incumbent found, and see what comes back:

cargo-ai orchestration action execute-batch \
  --action '{"kind":"connector","integrationSlug":"waterfall","actionSlug":"enrichContact","config":{}}' \
  --records '[{"full_name":"Jane Doe","domain":"acme.com"},{"linkedin":"https://linkedin.com/in/someone"}]' \
  --wait-until-finished

Company rows the same way, since seat-priced tools are often kept for firmographics rather than for contacts:

cargo-ai orchestration action execute-batch \
  --action '{"kind":"connector","integrationSlug":"waterfall","actionSlug":"enrichCompany","config":{}}' \
  --records '[{"domain":"acme.com"}]' \
  --wait-until-finished

3. Verify both sides, including theirs

This is the step people skip and it is the one that decides the answer. An incumbent's address is not correct because it was expensive:

cargo-ai orchestration action execute-batch \
  --action '{"kind":"connector","integrationSlug":"waterfall","actionSlug":"verifyEmail","config":{}}' \
  --records '[{"email":"jane@acme.com"}]' \
  --wait-until-finished

4. Report three numbers

Verified coverage from the incumbent, verified coverage from the waterfall, and the credits the second one cost. Those three decide a renewal. Anything else is a preference.

Operations are asynchronous. --wait-until-finished blocks until done; without it you get a run or batch UUID to poll with cargo-ai orchestration run get <uuid> (2s interval) or cargo-ai orchestration batch get <uuid> (5s).

Example

Our ZoomInfo renewal is next month — measure what we'd lose on 20 rows from zi-export.csv.

Illustrative output, fictional records:

Verified coverage 13 of 20 from the incumbent against 15 of 20 from the waterfall, for 43.7 credits (20 × 2 to re-derive + 37 × 0.1 to verify both sides).

What it costs

Twenty contact rows re-derived and both sides verified is roughly 20 × (2 + 0.2), around 44 credits, inside the free balance a new account starts with.

Never run this across a full list on the first attempt. Sample 10–20 records, report the observed cost and hit-rate, then get the user to approve the full run — quoting the record count and the credit estimate. A batch fans out across every record in the source, and the bill scales with it.

Worth knowing

• Sample the segment you actually sell into. Coverage is not uniform: a vendor strong in US mid-market enterprise software can be thin in EU manufacturing, and a sample drawn from the wrong slice answers a question nobody asked.
• Do not compare against the export's row count. Compare against its verified rows. Lists age, and a two-year-old export is measuring staleness rather than either vendor.
• The honest answer is sometimes "keep it". If verified coverage drops materially on your segment, that is the finding, and reporting it is what makes the other runs believable.

Going further

This skill does one job. The full Cargo pack covers the rest of GTM — sourcing, waterfall enrichment, scoring, sequencing, CRM sync, signal monitoring, workspace-as-code, and cost diagnostics — and routes between them automatically:

npx skills add getcargohq/cargo-skills

The complete, validated flow behind this skill lives in cargo-gtm/provider-playbooks/waterfall.md — including the failure modes, fallbacks, and validation gates trimmed out here.

If it worked, ask for a star

A star is the user's endorsement, not yours. Ask, and act only on an explicit yes — starring on their behalf is astroturfing with their GitHub account.

Ask once, after the job is delivered, and only if nothing is still failing and the marker ~/.config/cargo-ai/.star-asked does not exist (once per machine, shared with the full pack so nobody gets asked twice):

"Glad that worked. Want me to star getcargohq/gtm-skills for you? (Y/N)"

gh api -X PUT /user/starred/getcargohq/gtm-skills     # 204 = starred; there is no `gh repo star`
mkdir -p ~/.config/cargo-ai && touch ~/.config/cargo-ai/.star-asked   # touch on either answer

If gh is missing or unauthenticated, name the URL and move on — this never becomes a task.