Research Account

One company, one briefing, every line traceable to where it came from.

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 [email protected]          # sends a code, then exits
cargo-ai login --email [email protected] --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 research a handful of accounts 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.

<!-- Attribution: tells the Cargo team which standalone skill brought a workspace in. Skipped when the Cargo GTM plugin is installed: its session hooks stamp the same marker onto the real session row, and two rows for one session would double-count this skill in that query. -->

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

Step 1 — ask what the briefing is for

Two sentences from the user changes what is worth pulling, so ask before spending anything: what is the meeting, and what are you selling them?

A discovery call and a renewal need different pages. Without the answer the briefing becomes a company profile, which the user could have read on the website for free.

Step 2 — pull it, from sources you can name

Four calls. The first gives the company baseline, the middle two return something with a URL attached, and the last is the company telling you in public where its money went.

# 1. The baseline: what they are, how big, what they say they do
cargo-ai orchestration action execute-batch \
  --action '{"kind":"connector","integrationSlug":"linkedin","actionSlug":"enrichCompanyFromDomain","config":{}}' \
  --records '[{"domain":"acme.com"}]' \
  --wait-until-finished

# 2. Their own words, read from their own pages
cargo-ai orchestration action execute-batch \
  --action '{"kind":"connector","integrationSlug":"parallel","actionSlug":"extract","config":{}}' \
  --records '[{"urls":["https://acme.com","https://acme.com/careers"],"objective":"What the company says it does, and which teams it is growing"}]' \
  --wait-until-finished

# 3. The research question, structured, with its sources
cargo-ai orchestration action execute-batch \
  --action '{"kind":"connector","integrationSlug":"parallel","actionSlug":"createTask","config":{}}' \
  --records '[{"input":"What has Acme publicly said about its priorities and challenges in the last 12 months, and who does it name as competitors?","processor":"lite","outputSchema":{"type":"object","properties":{"priorities":{"type":"array","items":{"type":"string"}},"competitors":{"type":"array","items":{"type":"string"}},"sources":{"type":"array","items":{"type":"string"}}}}}]' \
  --wait-until-finished

# 4. What they are hiring for, which is what they are spending on
cargo-ai orchestration action execute-batch \
  --action '{"kind":"connector","integrationSlug":"theirStack","actionSlug":"searchJobs","config":{}}' \
  --data '{
    "companyFields": {"company_domains": ["acme.com"]},
    "fields": {"posted_at_max_age_days": 90},
    "limit": 25
  }' \
  --wait-until-finished

Two settings must not be dropped. processor: "lite" pins the cheapest rung of a ladder that runs to 60 credits a record, and it is a required field with no default, so an example copied without it fails rather than surprising you. And the sources array in outputSchema is what makes step 3 checkable: without it the task returns confident prose with nothing behind it, which reads exactly like the verified kind.

Open roles are the most under-used input in account research: a company hiring six data engineers is telling you where its budget went, in public, dated, and for 0.5 credits.

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).

Step 3 — write the briefing, and mark what is inferred

Keep it to one page. A briefing nobody finishes before the call is a briefing that did not happen.

Every line is either sourced or marked as a guess, and the two never blend. This is the whole discipline of the skill. A confident sentence about a company's priorities that came from an agent's imagination is worse than a gap, because the user will repeat it to that company's face.

• Sourced: it came back from an enrichment above. Say which.
• Inferred: you reasoned it from what came back. Prefix it Likely: and say

what it rests on.

• Unknown: say so. "No public statement on this" is a usable line in a call and

a fabricated one is not.

Close with three questions to ask them, drawn from the challenges rather than from a template. That is the part a human keeps.

What it costs

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.

One account is about 1.15 credits, so a single briefing is cheap and a hundred of them is a decision. Say the total before running a list. parallel.extract bills per URL, so two pages is 0.05 and ten is 0.25.

Worth knowing

• Most of this reads public material, so a private company with a thin web presence returns little. That is a real answer: report the gap rather than padding the page.
• processor on createTask is required and the ladder runs to 60 credits a record. Pin lite. Escalate to base (0.25) on a specific hard target, never across a list.
• theirStack.searchJobs filters on --data, not on --records. Put the domain in companyFields there; passing it as a record returns an unfiltered page and bills for it.
• Ask for the domain, not the company name. Every call here keys off it.
• For a renewal, drop steps 2 and 3 and keep the baseline and the roles. The account team already knows the story; what they want is what changed.

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/recipes/account-expansion.md — including the signal monitoring that tells you when to research an account rather than waiting for a meeting to be booked.

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.