cargo-gtm

Cargo GTM — Meta Skill

Use this skill for prospecting, account research, contact enrichment, verification, lead scoring, personalization, signal monitoring, and campaign activation.

Acceptable use — MANDATORY, before anything that touches a person

Full spec: `references/acceptable-use.md`. The short version, binding on every recipe here:

• B2B professional identities only, from the licensed providers in `provider-playbooks/` — never consumer targeting, purchased lists, or data taken from a platform in breach of its terms.
• Three checks before any outreach step — basis (customers, opted-in contacts, event attendees, or a documented legitimate-interest case), suppression (filter on unsubscribe / DNC / hard-bounce before enriching or sending), relevance (name, per recipient, why this message is for them). Any check that fails is a stop-and-ask, not a warning.
• Refuse and say why: undifferentiated fan-out ("email everyone in <industry>"), contacting a suppressed record, filter evasion or disguised sender identity, auto-dialing and SMS blasts, batch-blasting LinkedIn engagement actions. Offer the compliant version once — state it, don't lecture.
• This skill never sends. Outreach recipes stop at send-ready variables and hand off to the user's own sequencer, under that sequencer's limits, domains, and identities. Copy it drafts must carry an honest sender and subject, a working opt-out, and a postal address where the jurisdiction requires one.

Bootstrap

Already signed in (cargo-ai whoami returns a workspace)? Skip to the next section.

npm install -g @cargo-ai/cli            # no global install? prefix every command with `npx @cargo-ai/cli`
cargo-ai login --email you@company.com  # emailed code, no browser; creates the account on first use
                                        # alternatives: --oauth (browser) · --token <api-token> (CI)
cargo-ai whoami                         # confirm the active workspace before any write

Every command prints JSON to stdout; failures exit non-zero with {"errorMessage": "..."}. Anything that creates a run or a batch is async — pass --wait-until-finished or poll the matching get. When the full skill bundle is installed, `../cargo/references/prerequisites.md` adds the CLI version pin, token scopes, and the admin-only surface.

1) What this skill governs

• Route GTM decisions, safety gates, and provider/quality defaults before execution.
• Keep long command chains and tooling nuance in sub-docs; provider-specific implementation detail in provider-playbooks/*.md.
• Anchor recipes in credits-based actions (the high-value action calls). Free CRUD (createLead, getLead, deleteRecords) doesn't need this skill — agents can compose those ad hoc.

Process / goal

The user is generally trying to go from "I have an ICP" to "Here's a list of prospects with verified emails and personalized signals." They may be anywhere in this process — guide them along.

Discovery order: companies first, then people. When the task requires finding contacts at companies matching criteria (portfolio, ICP, hiring signal), discover the company set first, then find people at each company. Don't start with broad people-search queries.

Documentation hierarchy

• Level 1 — SKILL.md (this file): decision model, guardrails, routing table, links to sub-docs.
• Level 2 — Phase docs: `guides/finding-companies-and-contacts.md`, `guides/enriching-and-researching.md`, `guides/writing-outreach.md`.
• Level 2.5 — Recipes: `recipes/*.md` — step-by-step playbooks for specific scenarios.
• Level 3 — Provider playbooks: `provider-playbooks/<slug>.md` — provider-specific quirks, costs, and fallback behavior.

2) Read behavior — MANDATORY before any execution

STOP. Do not call any provider, run any `cargo-ai orchestration action execute` command, or write any search query until you have opened the correct sub-doc for your task.

These docs encode what works, what fails, and why. They contain validated parameter schemas, cheapest-provider mappings, parallel execution patterns, sample payloads, and known pitfalls. Reading the right doc for 10 seconds saves 10 failed action calls, wasted credits, and garbage output.

Routing rules — match your task to a doc and READ IT

Recipes: step-by-step playbooks (check before executing)

Scan this list and read the recipe matching your task. When a recipe matches: follow it step-by-step as your execution plan.

If none match, scan the phase docs above for the closest pattern and adapt — or invoke `agents/execution-plan-creator.md` to compose a custom chain with provider/action slugs and cost estimates. For wide sourcing sweeps that fan out (per-industry, per-geo), delegate approved slices to `agents/list-builder.md` — it executes exactly one pre-approved action per slice and returns rows to a file, keeping row data out of the main context. (On Claude Code with the plugin, both are installed as native subagents: cargo-execution-planner and cargo-list-builder.)

3) Cost discipline — MANDATORY gates

Full spec: `references/cost-discipline.md`. The short version every task must honor:

1. Sample → approval → full run, in that order. Run a slice of the exact input first — 1–3 rows to prove one action's config, 10–20 records before any batch (one row can't show a hit-rate). Then present the 4-section approval message (Assumptions · Sample result verbatim · Credits/Scope/Cap — always stating how many records the full run enrolls and what they cost, reconciled against the actual balance · 3 shaped choices); stay in AWAIT_APPROVAL until the user picks. Never fan out on an unapproved or cost-unknown action, and never read approval of the sample as approval of the full enrollment.
2. Receipt after every paid action: credits spent + balance remaining + hit-rate ("found 34 emails of 40") + estimate-vs-actual with the why when they diverge. Prefer billing usage get-metrics over your own arithmetic.
3. Over-provision 1.4×N, then filter — coverage is a property of the company; drop incomplete rows instead of chasing them with more providers.
4. Count first, pay second — search is billed on returned rows; keep limit strict and size the pool with a 1-row probe before any full pull.
5. Phone is the guarded lever — explicit user request only, qualified leads only. Still true at the cheap end: aiArk.findMobilePhone (0.5, mobile-only) is the first rung and bills 0 on a miss, but the escalation behind it is 3–7 credits (~10× email), so a full-list phone sweep needs the same approval as any other paid fan-out.

4) After every run — receipt, then grounded next steps

End every completed run with the receipt (above), then propose 2–3 next steps maximum, computed from the data just produced — never a generic menu. Required shape:

1. Continuity — builds on this session's artifacts ("67 of these 70 companies have RevOps teams — find the leads?"), not a fresh generic idea.
2. Budget-aware — framed against the remaining balance ("with your ~9 credits left, ~5 verified emails fits").
3. Cost-per-unit stated — "email waterfalls run ~1.4 credits each."
4. A default picking heuristic so answering takes one word ("I'd default to: has funding data + RevOps ≥ 2 + posting is recent").
5. An escape hatch — always end with "or something else entirely."

When a run produced a durable, repeatable result, one of the suggestions should be making it systematic — see `recipes/save-as-play.md`.

When a run or batch misbehaved — errors, missing downstream values, cost surprises — hand off to the cargo-diagnostics skill (../cargo-diagnostics/SKILL.md): sweep the batch for root causes before re-running anything paid. Interaction defaults for plan gates, shaped choices, and presenting results live in ../cargo/references/interaction.md.

5) Priority provider stack (recipes lead with these 7)

These seven credits-based providers cover the full prospecting → enrichment → verification → signal pipeline at the lowest credit cost in the catalog. Every recipe in this skill's recipes/ leads with this stack:

aiArk and apolloio sit at opposite ends of the enrich tier and are picked by what you hold, not by preference: aiArk wins whenever a LinkedIn URL is in hand (profile + verified email at 0.1, mobile at 0.5, both billing 0 on a miss), apolloio is the 1-credit niche-coverage rung you promote per-batch when a pilot shows Apollo hits where aiArk (0.1) and waterfall (2) miss — investor-backed and portfolio niches especially. Neither displaces salesNavigator for plain at-scale sourcing (0.02/lead).

Three signal families sit outside the stack and are picked per task from `references/stage-action-map.md`: firmographic depth beyond aiArk.enrichCompany → companyEnrich.enrichByDomain (0.25); funding / acquisitions → enrichCrm.getFunding (1, the only credits-based funding action in the catalog); tech stack on a known domain → builtwith.getDomainSummary (free) before builtwith.enrichDomain (1).

See `provider-playbooks/` for per-provider deep dives — including each provider's Recurring use section for when the task is a monitor, play, or scheduled pull rather than a one-off. See `references/stage-action-map.md` for the complete cheapest-action-per-stage table across the full 136-integration catalog.

Already holding identifiers (not sourcing)? The stack above leads the sourcing-first spine. When you already have LinkedIn URLs, the cheapest enrich is `aiArk.enrichPerson` (0.1 — full profile plus a verified email, bills 0 when no email is found); drop to `linkedin.enrichProfile` / `enrichCompany` (0.25) when you don't need the email, and skip waterfall.enrichContact entirely (it keys on email or name+company, not a URL). Need a phone? aiArk.findMobilePhone (0.5) is the first rung, not the 3–7 tier. Have a LinkedIn event URL? linkedin.extractEventAttendees sources the attendee list directly. Have emails? aiArk.reverseLookup (0.05), then leadMagic / contactOut. See references/stage-action-map.md for the full input-type → cheapest-action map.

6) Recipe spine (default chain)

1. SOURCE   → salesNavigator.searchLeads / searchAccounts            (0.02–0.05/record)
              lookalike seeds, or filters SN can't express (skills,
              education, tenure)? aiArk.searchCompanies / searchPeople (0.01–0.05/record)
2. DEDUPE   → match against the workspace's own Companies / Contacts models
              on domain / linkedin_url (storage SQL or a segment filter)  (free)
3. ENRICH   → LinkedIn URL in hand? aiArk.enrichPerson (0.1) FIRST — profile + verified
              email in one call; linkedin.enrichProfile/enrichCompany (0.25) if no email needed
              aiArk.enrichCompany (0.01) for firmographics; companyEnrich.enrichByDomain
              (0.25) on the rows that come back thin
              + waterfall.enrichContact / enrichCompany              (1–2/record)
              + apolloio.enrichPerson / enrichOrganization on the niche residue (1/record)
4. SIGNAL   → enrichCrm.getFunding                                   (1/record)
              + theirStack.searchJobs / builtwith.getDomainSummary   (0–0.5/record)
              + waterfall.detectJobChange                            (3/record)
5. CONTACT  → FullEnrich.findEmail — only on rows step 3 left without
              an email (fallback peopleDataLabs)                     (1–3/record)
6. VERIFY   → waterfall.verifyEmail                                  (0.1/record)
7. BACKFILL → peopleDataLabs.enrichPerson (only if step 5 missed)    (3/record)
8. QA       → scripts/contact-accuracy-audit.ts                      (free, local)

Two spine notes from the 8-provider stack: step 3's aiArk.enrichPerson already returns a verified email, so step 5 runs on the residue only — don't pay FullEnrich.findEmail (1) behind a row that already has one. And when the goal reaches a phone, aiArk.findMobilePhone (0.5, mobile-only, bills 0 on a miss) is the first rung before prospeo (3) / FullEnrich (6) / waterfall (7) — the guarded-lever rule in §3 still applies to all four.

Adapt by phase: drop steps that aren't relevant to the user's goal. For pure sourcing, run step 1 only. For "enrich a list I already have," run steps 2–7.

7) Output retrieval — use run download-outputs, not run download

When the agent needs the actual data produced by an action (enriched fields, found emails, search results), use:

cargo-ai orchestration run download-outputs \
  --workflow-uuid <uuid> \
  --output-node-slug <slug> \
  --format json

(Don't pass --is-finished — the CLI help still lists it but the API currently rejects it with unrecognized_keys; reported.)

Returns {"url": "..."} — a signed URL to a CSV/JSON containing only the output node's data. Faster and cheaper than run download (which pulls full run records). See `references/output-retrieval.md` and `../cargo-analytics/SKILL.md`.

8) Contact accuracy — run the QA scripts, don't eyeball

Four deterministic TypeScript scripts in `scripts/` (Node ≥ 22.18, zero deps, fixture-tested in CI) replace in-context row checking. Run the script — never re-derive its logic by reasoning over rows. Full doctrine, pipeline order, and the SEND/VERIFY/REVIEW/REMOVE verdict semantics: `references/contact-accuracy.md`.

• scripts/validate-emails.ts — free syntax/risk/duplicate cull before paid verifyEmail.
• scripts/select-current-role.ts — pick the real current role from an experiences array (catches job changers).
• scripts/validate-linkedin-names.ts — name↔profile match (catches same-name decoys); pairs with `recipes/linkedin-url-lookup.md`.
• scripts/contact-accuracy-audit.ts — final per-row audit_action stamp on the merged output; cite its summary counts in the receipt. Reads files or a finished run directly (--workflow-uuid, via @cargo-ai/api).

9) Action shape rules (every recipe)

Every action JSON in this skill follows the rules in `../cargo-orchestration/references/examples/actions.md`:

• kind: "connector" action shape: {"kind":"connector","integrationSlug":"<slug>","actionSlug":"<slug>"}. `connectorUuid` is NOT in `config` — the platform resolves the workspace's authenticated connector from integrationSlug automatically.
• A top-level action has no `config` — omit it. Inputs go in --data / --records, and every recipe here writes the action without the key. That holds for action execute, execute-batch, and get-output-schema alike — the object action list returns pastes into all three. Inputs misplaced into config are not rejected, they are dropped, and the action runs with no input, so check this first when a call returns empty for no visible reason.
• Don't hand-write a slug you're unsure of, and don't page the catalog looking for one. cargo-ai orchestration action list <keywords> [--integration-slug <slug>] is free, searches every integration plus native actions, tools, and agents, and returns the action object ready to paste with the action's credit costs — a cheap sanity check on both the slug and the price before a paid call. When the question is which paid actions exist for this?, cargo-ai connection action search <keywords> --credits-only is the one that filters on it. Neither replaces the provider playbook below: the playbook is where the input quirks, hit-rates, and recurring-use traps live.
• For multi-step node graphs: connectorUuid lives at the top level of the node, not in config. Cross-node interpolation uses {{nodes.<slug>.<field>}}. Agent node outputs wrap under .answer (read as {{nodes.<slug>.answer.<field>}}).

10) When stuck — file a workspace report

If a recipe fails repeatedly and the cause isn't obvious, escalate via cargo-ai workspaceManagement report create. See `../cargo-workspace-management/SKILL.md` (Reports section).

11) Provider playbooks — read before you call (one-off or recurring)

STOP — do not execute any paid action against a provider below, and do not wire a provider into a recurring play/tool node graph, until you have opened its playbook. Each playbook carries the exact action slugs, config shapes, input quirks, and cost traps; reading it for five seconds is cheaper than one failed paid call, and a failed batch is 100 failed paid calls. The stakes are higher, not lower, when the provider goes into a recurring workflow: a bad config repeats on every scheduled run, and a wrong cadence re-bills the same rows forever — each playbook ends with a Recurring use section (schedule fit, cadence default, re-billing gates, extractors) for exactly this. Every credits-based provider with callable actions now has a playbook, with one stated exception: openRouter, which exposes a model lister rather than credits-based actions, so there is nothing to document. brightData and proxycurl gained playbooks rather than staying unlisted — an undocumented provider still shows up in the cost table, and leaving the acceptable-use framing implicit was the weaker option: `provider-playbooks/brightData.md` states the consumer-targeting refusal up front. Own-key integrations fall back to `references/alternatives.md` and `references/stage-action-map.md`.

Priority stack (recipes lead with these):

• `provider-playbooks/salesNavigator.md` — cheapest sourcing in the catalog (0.02–0.05/record).
• `provider-playbooks/aiArk.md` — LinkedIn-anchored people/company data: enrichPerson returns profile + verified email at 0.1, findMobilePhone (0.5) is the cheapest phone rung, searchCompanies (0.01/record) does lookalikes, and analyzePersonality (0.05) is catalog-unique. All actions run on the managed connection.
• `provider-playbooks/waterfall.md` — swiss-army-knife: enrichment, verification, and the cargo-unique detectJobChange signal.
• `provider-playbooks/FullEnrich.md` — premium contact lookup; reverseEmailLookup is unique.
• `provider-playbooks/apolloio.md` — the 1-credit niche-coverage enrich rung (person + organization); read it before assuming Apollo is available — only two of its eleven actions are credits-based, the rest need your own Apollo API key.
• `provider-playbooks/theirStack.md` — tech-stack + hiring-intent signals.
• `provider-playbooks/peopleDataLabs.md` — heavyweight backfill at flat 3-credit tier.

Sourcing & company-data specialists:

• `provider-playbooks/linkedin.md` — the native LinkedIn integration's action set (profiles, companies, posts, jobs).
• `provider-playbooks/oceanio.md` — lookalike-company discovery from seed domains, with technographic / web-traffic filters aiArk.searchCompanies (0.01) can't express.
• `provider-playbooks/datagma.md` — lightweight person/company enrichment alternative.
• `provider-playbooks/companyEnrich.md` — cheapest company-by-domain (0.25) + per-item-billed lookalikes.
• `provider-playbooks/enrichCrm.md` — CRM-record enrichment; getFunding is the funding-signal fallback.
• `provider-playbooks/societeInfo.md` — French-registry company/contact data (SIREN/SIRET).
• `provider-playbooks/snitcher.md` — website-visitor identification; the recurring extractor is the cost trap.
• `provider-playbooks/piloterr.md` — ultra-cheap bulk company extractor + G2 product info.
• `provider-playbooks/g2.md` — software-review & category signal data.
• `provider-playbooks/theSwarm.md` — warm-intro network mapping to target companies/people.
• `provider-playbooks/mixrank.md` — premium person/company backfill (4/lookup, phone-only reverse lookup).

Email & contact specialists (all feed the VERIFY step — see `references/waterfall-strategy.md`):

• `provider-playbooks/hunter.md` — domain-search email finding + verification.
• `provider-playbooks/prospeo.md` — email/phone lookup, LinkedIn-URL input path.
• `provider-playbooks/icypeas.md` — budget email find/verify.
• `provider-playbooks/findyMail.md` — email finding alternative.
• `provider-playbooks/leadMagic.md` — email + mobile lookup alternative.
• `provider-playbooks/contactOut.md` — contact info from LinkedIn profiles.
• `provider-playbooks/zeroBounce.md` — email-verification second opinion to waterfall.verifyEmail.
• `provider-playbooks/bouncer.md` / `neverBounce.md` / `kitt.md` / `enrichley.md` — verification long tail (0.3 / 0.2 / 0.05 / 0.1; enrichley's slug is verify, not verifyEmail).
• `provider-playbooks/dropcontact.md` — email finding with French/EU registry depth; email output is an array.
• `provider-playbooks/enrowio.md` — email find (1) + verify (0.1); takes fullName only.
• `provider-playbooks/reverseContact.md` — company-from-LinkedIn (credits); profile lookups are own-key.
• `provider-playbooks/rocketreach.md` — person lookup (1); healthcare/NPI niche; beware the currrentEmployer schema key.
• `provider-playbooks/forager.md` — personal-email + phone from a LinkedIn URL.
• `provider-playbooks/cleon1.md` — terminal phone rung (15/lookup) — explicit user request only.

Research & scraping:

• `provider-playbooks/firecrawl.md` — web scraping for research/personalization stages.
• `provider-playbooks/serper.md` — Google SERP queries for research and URL discovery.
• `provider-playbooks/linkup.md` — web search (0.5 standard / 2 deep) + sourced/structured answers.
• `provider-playbooks/parallel.md` — cheapest page read in the catalog (extract, 0.025/URL) plus createTask, the only action that fills a caller-supplied output schema.
• `provider-playbooks/exa.md` — semantic search with a document-type category filter and publication-date bounds.
• `provider-playbooks/builtwith.md` — a domain's technology stack; getDomainSummary is free and runs in front of the paid rung.
• `provider-playbooks/x.md` — public X posts and profiles at 0.02 an action; a signal rung, gated by acceptable use.
• `provider-playbooks/sillage.md` — inbound signal detections read back from a model, free, so it runs first on any signal question.
• `provider-playbooks/brightData.md` — Instagram / TikTok / Facebook / YouTube profiles by URL, 0.1 an action; the catalog's only non-LinkedIn, non-X social coverage, and the playbook opens with the consumer-targeting refusal that gates it.
• `provider-playbooks/proxycurl.md` — LinkedIn profile / company lookups on your own key.

LLM providers (all: one instruct action, cost per 1,000-token package, per-model tiers — prompts come from `references/prompt-library/index.md`):

• `provider-playbooks/anthropic.md` — judgment-tier default (Haiku/Sonnet 0.2, Opus 2); temperature nests under advancedSettings with required maxTokens.
• `provider-playbooks/openAi.md` — cheapest bulk tier (gpt-5-nano 0.006) + native JSON-schema output.
• `provider-playbooks/gemini.md` — cheap high-throughput (Flash 0.01, 15,000/min) + search grounding.
• `provider-playbooks/perplexity.md` — web-grounded research answers; default model is the expensive sonar-deep-research — always set model explicitly.

12) References

• `references/cost-discipline.md` — the mandatory spend rules: pilot → approval gate, per-run receipts, 1.4×N over-provision, count-first sizing, provider-billing rules.
• `references/contact-accuracy.md` — the deterministic QA scripts (email cull, current-role, name match, final audit) and the SEND/VERIFY/REVIEW/REMOVE verdicts.
• `references/prompt-library/index.md` — ~40 named, parameterized LLM prompts (personalization, scoring, research, qualification, signal analysis, extraction). Before authoring any enrichment/scoring prompt from scratch, grep this index — reuse beats reinvention, and each entry carries a tested output contract. Load only the shard you need, never all of them.
• `references/stage-action-map.md` — cheapest credits-based action per stage across the full 136-integration catalog.
• `references/credits-cost-table.md` — auto-generated cost table for all 176 credits-based actions.
• `references/waterfall-strategy.md` — canonical waterfall chains by enrichment goal (every recipe's "fallback" follows these).
• `references/alternatives.md` — provider swap-ins from the long tail when the priority stack can't serve.
• `references/output-retrieval.md` — run download-outputs patterns for fetching action data.