The GTM engineering playbook
with Claude Code

What is a GTM engineer, and what tools and data providers do they need in 2026?

A GTM engineer turns a company’s sales and marketing playbook into systems that run every week: the ICP written as filters, lists built and enriched, signals that say who to contact now, research for every first line, and routing for inbound, with a person approving what goes out. GTM engineering is the practice; the GTM engineer is the person who owns it. The job sits between RevOps, sales and engineering. What changed in 2026 is where it runs: more of it now runs from an agent such as Claude Code or Codex, where you describe the job in a prompt and the agent makes the calls.

What a GTM engineer does in a week

1. Write the ICP down from the deals the team actually won and kept, as fields a provider can filter on (chapter 1).
2. Build and enrich lists cheaply: qualify on the fields you already have, then pay for people, emails and verification only on the rows that pass (chapters 4 to 8).
3. Watch for timing: hiring, funding, job changes and posts, each with a link and a date, scored against fit (chapters 9 and 10).
4. Hand reps the reason to write, with its source, and keep a person on the send button (chapter 11).
5. Keep the data honest: verify before sending, re-check people who just moved, and test a signal before it gets a weight. Our job-change study shows why.
6. Measure and roll out: tag every row so pipeline traces back to a list, a signal and a provider, and automate one step at a time (chapters 14 to 16).

GTM engineer, RevOps or sales ops?

Titles vary and the work overlaps, and in many teams GTM engineers sit inside RevOps. For the pipeline work in this playbook, this is a useful way to divide it:

In small teams one person holds several of these. Roughly: RevOps decides how the revenue system should work, and a GTM engineer builds the automated parts of it.

When GTM engineering helps, and what the work takes

The workflows in this playbook pay off once the ICP can be written down and someone is doing the same research by hand every week. A small workflow can be owned by someone already on the team; a dedicated role makes sense when the volume and value justify it. Either way, the skills are less about code than about precision:

• Writing rules a machine can follow: what qualifies, what disqualifies, what the agent may do alone.
• Data judgement: cost per correct result rather than price per row, and knowing which fields go stale (found is not deliverable).
• Testing before trusting: a timing signal earns a weight only after it separates your own wins from losses (our test).
• Enough sales sense to know a weak reason to write when the agent produces one.
• Comfort with an agent, a CLI and a spreadsheet. Little code is needed when the agent makes the calls.

The stack has five layers. Only one of them is treg.

set up once, in your agentcopy

set up treg — https://treg.to/llms.txt

Go deeperThe catalogSet up in Claude CodeTutorial

Write your ICP as fields a provider can filter on

An agent cannot search for “values data”. It can search for country, headcount band, category, funding and technology. Anything you cannot turn into one of those becomes a check the agent runs on the rows afterwards (chapter 5), not a filter.

1. Split the ICP into filters (fields providers index) and checks (judgement on each row).
2. Write the buyer as a function (“marketing or growth”), not a title, so chapter 6 can search for it.
3. Keep the exclusions explicit: agencies, consumer apps, companies that were acquired.

ICP: the one we ran this playbook on
filters   country = US · employees = 51-200 · category = B2B and SaaS
checks    sells to sales, marketing, revenue or growth teams, or runs outbound itself
          would buy lead data, enrichment or prospecting APIs
buyer     function = marketing or growth (Head of Marketing, Head of Growth)
exclude   agencies, services firms, consumer apps, hardware, acquired companies

promptcopy

Using treg, turn the ICP in icp.md into filters for two company-search providers.
Use their free count endpoints only. For each provider show the exact filter
object and the count it returns. Do not return any rows yet.

Go deeperCompany research for agentsCompany data providers

Count the market for free before you pay for a single row

Several providers will count matches for free. Use that before anything else, and use two of them: the same ICP, expressed in each provider’s vocabulary, returns very different markets.

1. Count on two providers. A gap of 2× is normal; 100× means one filter is not doing what you think.
2. Pull 10 sample rows from each (cheap) and read them before you trust either number.
3. Size the adjacent bands too. Your real market is often the band next door.

promptcopy

Using treg's free company-count endpoints, count my ICP on two providers and in the
headcount bands either side of it. Show every filter you used. If two counts differ by
more than 3x, tell me which filter is likely wrong before we pay for anything.

Go deeperCompany data providersCompanyEnrichDropleads

Write down the rules the agent follows, and who can stop it

An agent acts at volume on whatever its instructions leave open. A written rule is still an instruction, not a guarantee, so it needs a number, a log and an owner.

icp_check      pass if fit >= 0.5, judged on the fields the row already has (one cheap enrichment first if it has too few)
people, email and news steps   only on rows that passed icp_check
drops          log every dropped row with its reason
email          send only to verifier = deliverable; unknown and catch-all go to a separate list
first_line     a person approves every first line before a sequence starts
spend_cap      stop and ask if a run will cost more than $5
owner          <name> can pause every sequence with one command

Go deeperThe run the thresholds came from

Lookalikes of your best accounts are candidates, not leads

We took the three best-fit accounts from the 23 Sep run as seeds, asked for 25 lookalikes each in the same country, enriched every one and ran the same ICP check as chapter 5.

1. Seed with 3 to 5 accounts that actually closed, not the logos you wish you had.
2. Constrain the lookalike by country and size if the provider allows it; we only constrained country, and most results were smaller companies.
3. Enrich once, cheaply, so there are fields to judge; then enforce your size filter and run the same check as any other list.

promptcopy

Using treg, find 25 lookalikes for each of these customer domains, same country and
same size band. Enrich each lookalike, then run my ICP check from rules.md on it.
Return only the ones that pass, with the reason each failed one was dropped.

Go deeperCompany enrichment comparedFindymail

Qualify on the fields you already have before any expensive lookup

1. Judge every row on the fields the list already carries: description, industry, size, funding.
2. Drop the rows that fail and keep the reason next to them.
3. Run finders, verifiers and news only on what passed; use your own provider keys where you already pay.

promptcopy

Using treg, list 50 US software companies with 51 to 200 staff that raised a Series A.
Before you find anyone, judge each company against my ICP from the list fields alone,
drop anything below 50%, and show me the price of every paid step before you run it.

Go deeperThe full lead-list runLead enrichmentApollo

Which people search APIs work inside an AI agent? Search by role, not “decision makers”

On 8 accounts that passed chapter 5, we asked a “decision makers” endpoint for the committee, then asked a routed people search for the buyer’s function directly.

1. Map the committee yourself: who pays, who champions, who uses it. Write each as a function.
2. Search each account for those functions, cheapest rows first, then pick the most senior per function.
3. Reveal contact details only for the people you will actually write to.

promptcopy

Using treg, for each passed account search people by company domain with title
"marketing" and then "growth", at most 3 rows each and $0.20 per call. Return name,
title and a seniority guess. Do not reveal emails yet.

Go deeperPeople search APIs comparedClaude for people searchPeople Search BenchQuickEnrich

The cheapest way to run waterfall email enrichment from an AI agent

Coverage depends on the segment, so the only honest test is your own rows. And the number that matters is cost per correct result, because a cheap provider that misses is expensive.

1. Take 20 to 50 rows of your real list and run them through a routed finder, which tries providers cheapest first; misses on per-success providers are not billed.
2. Record which provider found each email, and compare cost per correct, not price per row.
3. Widen adjacent titles before switching vendors; thin results are often a title filter.

promptcopy

Using treg, find work emails for these 30 people with the routed email finder, then
verify each one. Report found, verified and cost per verified email, and which
provider found each address.

Go deeperEmail finders comparedThe 292-person benchHunter

Find, then verify, and keep the unknowns apart

Those companies had passed a fit check first. In a separate sample built from title searches alone, the gap between found and deliverable was much wider. We asked the routed finder for each person’s email and verified every address it returned.

1. Verify as its own step and store the verdict next to the address. “Found” only means a finder returned an address; in the title-search sample above, 53% of found addresses verified deliverable.
2. Send only to deliverable. Unknown and catch-all go to a smaller, slower list, or nowhere. A blank catch-all field means unknown, not safe.
3. Watch bounces and complaints per sending domain.

Go deeperEmail verifiers comparedKittTomba

How to set up signal-based outbound: use signals you can open and date

An intent score cannot be checked, so reps discount it. A job post, a hire, a funding announcement or a post about the problem can be opened, dated and quoted in the first line.

Job changes: most contact records do not show the new job yet

A champion who moves to a new company is one of the best reasons to write. The trap is the data: in the first weeks after a move, most of the stored records we checked did not show the new company, so the email can go to an inbox they no longer read and the first line names the wrong company.

1. Keep only signals with a source link and a date, and drop closed job postings: more than half of those returned were closed.
2. Write one line of “why now” per signal. If you cannot, it is noise.
3. For a job change, confirm the new company with a live profile read before anything else. A second database helps little: when two both had the person, both missed the new job 64% of the time.
4. Give the feed an owner who triages it on a fixed day.

install the signals skillcopy

npx skills add superdesigndev/treg --skill lead-signals

prompt: check job changes livecopy

Using treg, for each person in job-changes.csv read their live LinkedIn profile and
return current company, title and start date. Compare with the company in our CRM and
flag every mismatch. For mismatches only, find and verify an email at the new company.
Show the cost before you start.

Go deeperClaude for lead signalsThe skillPredictLeadsLive profile reads

Score fit and timing together, then work the top tier first

Timing signals deserve suspicion before they get a weight. We tested a popular one: can you see a startup’s next round coming in its hiring or its news? Hiring could not tell them apart, and news ran the wrong way.

1. Keep fit and timing as two numbers. Multiply them only to sort, never to decide.
2. Define tiers with numbers, and write them in rules.md.
3. Re-score weekly; timing decays, fit barely moves.
4. Before a timing signal gets a weight, check it on your own past deals: the share of wins that showed it against the share of losses. If it does not separate them, use it as a reason to write, not as a score. For funding, act on the announcement itself.

promptcopy

Using treg, for each passed account pull open jobs and funding rounds. Count sales,
marketing and growth roles that are still open and were posted or first seen in the last
60 days. Tier A if fit >= 0.6 and 2+
such roles, B if 1+, else C. Show the tier, the roles and their links.

prompt: test a signal on your own dealscopy

Using treg, take wins.csv and losses.csv (domain and close date). For each account pull
job postings and news from the 90 days before its close date. For each signal, report
the share of wins and the share of losses that showed it, and tell me which signals
separate them by more than 15 points. Show the cost before you start.

Go deeperLead signalsAviato

Let the agent research. Let a person write, or at least approve.

1. The agent gathers the reason to write, with its source.
2. Score the reason before anyone writes. Drop weak reasons; do not polish them.
3. A person writes or approves the first line. Read ten out loud before a sequence goes live.

Go deeperHow the opener scoring worksCompany research

Sending infrastructure and replies: what practitioners recommend

Sending

Inboxes, domains and warm-up belong to your sending tool. The advice that held up across threads: conservative volume, and watching bounces and complaints per domain. Absolute rules (“never use links”) did not.

Replies

Classify every reply as interested, objection, referral or out-of-office; route it to the rep with the context the agent gathered; let a person approve the answer; and write the outcome back to the row (chapter 14).

Enrich an inbound lead from its email domain and route it in seconds

A work email gives you a domain, and a domain gives you most of what routing needs. We enriched 20 company domains the way a form handler would.

1. On submit, enrich the company from the email domain, and store the result so a repeat never pays full price.
2. Route on two or three fields you trust: headcount band, country, industry. Send gaps to a person, not to nurture.
3. Answer tier-A inbound within the hour; it already told you the timing.

treg call treg.companies.enrich --method POST --data '{"domain":"acme.com"}'

Go deeperCompany enrichment comparedPerson enrichmentTheCompaniesAPI

Tag every row, so you can tell which list, signal and provider paid off

1. Store per row: source list, signal, the provider that found the email, the verifier’s verdict.
2. Monthly, join rows to replies, meetings and closed-won in the CRM.
3. Cut the sources and providers that never appear on the winning side of that join.

Go deeperDownload the tagged CSV

The metric stack: system health, performance, efficiency

Go deeperAll workflows with receipts

Roll automation out like software: shadow, small segment, human gate, then autonomy

1. Shadow. Run the play next to the manual process for a week and compare outputs row by row.
2. Small segment. Turn it on for one segment or one rep.
3. Human gate. A person approves each send until the error rate is known.
4. Autonomy only for the steps that passed; keep the gate on first lines.

What broke in our own runs

1. Alert on expected counts per stage per run, not on errors.
2. Store raw provider responses for a week, so a bad parse can be fixed without paying twice.
3. Set a spend cap per run and have the agent stop and ask above it.

Go deeperAll workflows with receiptsProvider success rates in the catalog

The best Claude Code skills for GTM engineering, and the data step each leaves to you

A skill is the method. Most either ask for a vendor API key or leave live data to the agent. The last column is the catalog job that covers that step. We have not yet run these with treg end to end.

Stars read from GitHub on 29 Sep 2026. The six third-party repositories are MIT-licensed.

Clay alternatives for GTM engineers who work in Claude Code or Codex

If you already think in prompts, the question is not which table to use but where the data comes from and what it costs per correct result. Measured on the same 292 people:

Keep Clay if your team needs a shared visual table and already pays for it. Move the work into your agent when you want the whole playbook in prompts, paid per call. The full bench.

Every recorded run behind this playbook

The three studies are separate tests, each on its own sample, and are not part of the one-ICP runs; the hiring study’s figure includes building its two samples. The $5.03 in the header is the 30 Sep and 23 Sep runs, which follow one ICP; it includes $1.08 we spent re-running a step after our own parsing bug (ch. 16). * Two of that run’s calls used the team’s own Apify key, which treg never meters.

Glossary and questions