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tam-building

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Stand up your account universe as a deployed pipeline: an AI Ark company search shaped by your ICP, sized for free before it bills, then tiered A / B / C / disqualified by an agent that reads your rubric from the workspace context and web-searches the evidence the sourced row does not carry. Triggers: "our TAM is a stale CSV", "build our account universe", "source companies matching our ICP and rank them", "keep our market list current", "which of these companies are actually worth a rep", "tier the market we just sourced". Cargo CDK, aiArk, countCompanies, fetchCompanies, agent tiering, workspace context, webSearch. Skip when: you want the list once rather than a pipeline that keeps producing it, which is build-tam-list; or the accounts already exist in a CRM or an accounts model and only need judging, which is account-scoring.

它能做什么

Stand up your account universe as a deployed pipeline: an AI Ark company search shaped by your ICP, sized for free before it bills, then tiered A / B / C / disqualified by an agent that reads your rubric from the workspace context and web-searches the evidence the sourced row does not carry. Triggers: "our TAM is a stale CSV", "build our account universe", "source companies matching our ICP and rank them", "keep our market list current", "which of these companies are actually worth a rep", "tier the market we just sourced". Cargo CDK, aiArk, countCompanies, fetchCompanies, agent tiering, workspace context, webSearch. Skip when: you want the list once rather than a pipeline that keeps producing it, which is build-tam-list; or the accounts already exist in a CRM or an accounts model and only need judging, which is account-scoring.

技能文档

Tam building

State: to-be-approved. Deploy-verified against a live workspace: not yet. Treat Done when below as the acceptance test and review cargo-ai cdk plan before deploying. Make no outcome claim for this skill until it is approved.

The outcome

Your account universe, built from the Sales Navigator search that describes your whole market, even when that search returns far more than the extractor will hand over. Counted before it is extracted, split into sub-searches that each sit under the cap, resolved to real domains, and merged into the shared accounts model deduped.

The count is a design-time CLI call, not a deployed resource:

cargo-ai orchestration action execute --wait-until-finished \
  --action '{"kind":"connector","integrationSlug":"salesNavigator","actionSlug":"searchCompanyMetrics","config":{}}' \
  --data '{"url":""}'

It returns total_results and costs a fraction of a credit. Splitting order that usually works: industry first (descend the LinkedIn taxonomy only where a segment is still oversized), then geography, then headcount band. Recount every sub-search; split any that is still over.

Put it in your project

This folder is a worked example: real CDK resources written for some other company. The job is to end up with the code your company would have written, in your project, and an agent does the adapting. If the cargo-cdk skill is in your session it carries the long form of this; if not, this is enough.

  1. Look first. grep -l '@cargo-ai/cdk' package.json says whether a CDK project already lives here; ls */models/*.ts */connectors/*.ts says what it already declares. If there is no project: cargo-ai cdk init --template blank && cd && npm install. That is the whole shell; this folder never ships one.
  2. Copy this folder in as a sibling of what is there, then reconcile: for every model or connector this example carries that the project already has (an accounts model keyed on website, a HubSpot connector, an OpenAI connector), rewire the imports to the existing one and drop the copy. Two resources with one slug is a collision at deploy. Append this folder's .env needs to the project's .env.example; never overwrite it.
  3. Adapt. Work the sections below in order: What should not change is what you argue back about (say what breaks, then do it if they still want it); What you can change is what you offer unprompted (nobody asks for a variant they do not know exists); What you will be asked is the floor, and you derive before you ask. If you are asking more than about four questions you have skipped lookups. Record what you changed and why under a ## Decisions section in your copy of this file.
  4. Plan, then stop. npm run typecheck && cargo-ai cdk plan. Show the diff. Deploy only on an explicit yes: cargo-ai cdk deploy. Never cdk init --force into a non-empty directory.
  5. Verify. Walk Done when line by line and report each with evidence. Deployed cleanly and produced nothing is the normal failure.

What you will be asked

Derive before you ask. An input with a lookup is looked up, not asked. Only the ones marked asked genuinely live in the operator's head.

InputKindHow it is answeredWhy it matters
marketSearchmanualchecked, not written: Paste the Sales Navigator company search that describes your whole market, however oversized it isThis is the only input that cannot be derived: it encodes who you sell to. Everything else in this skill is arithmetic on top of it.
searchUrls (models/salesnav-companies.ts)valuederived: cargo-ai orchestration action execute --action salesNavigator.searchCompanyMetrics --data '{"url":""}' returns total_results. Over the cap, split by facet (industry first, descending the LinkedIn taxonomy only where still oversized, then geography, then headcount band) and recount each sub-search until every one is under the cap.A search holding 4,000 companies extracts 1,000 and tells you nothing about the missing 3,000. Splitting under the cap is the entire point of this skill.
limit (models/salesnav-companies.ts)valuedefaults to 1000; ask only to change itThis is the upstream cost control. Promotion is 1:1 with extraction, so what you extract is what you pay to enrich.

Checked before moving on, not after the deploy:

  • marketSearch: the URL is a linkedin.com/sales/search/company URL
  • searchUrls: every sub-search count is strictly below config.limit; a count equal to the cap means it is still truncated and must be split again
  • limit: at or below 1000

What you can change

The code is a worked example. These reshapes are expected, and the agent offers them rather than waiting to be asked. Every one costs something; that is what makes it a variation and not the default.

VariationWhen it is rightHowWhat it costs
non-linkedin-sourceYou do not want to source from LinkedIn at all, or Sales Nav does not cover your marketSwap the Sales Nav connector and landing model for the Ark enrichment API (https://ai-ark.com/platform/enrichment-api), keeping promote-to-accounts and the accounts contract unchanged (models/salesnav-companies.ts, connectors/sales-navigator.ts)You lose the Sales Nav facet taxonomy that makes the split tactic mechanical, and the splitting has to be redesigned around the new source's own limits
land-without-promotingYou want to see and filter the raw market before paying to enrich itDeploy the landing model only and leave promote-to-accounts out until you have decided which rows are worth promoting (plays/promote-to-accounts.ts)Nothing reaches accounts, so no downstream skill (scoring, contact sourcing, signals) has anything to work with until you promote
sample-firstThe market search is large and you want to see the cost curve before committingSet config.limit well below the cap and start with one sub-search URL, then widen once rows land correctly (models/salesnav-companies.ts)Your TAM is deliberately incomplete until you widen it, so do not score or report on coverage from a sample

What should not change

However far you adapt, these hold. Ask for one anyway and the agent tells you what breaks, then does it if you still want it, and records why under ## Decisions in your copy of this file.

  • Every company is resolved to a real domain before it is promoted into accounts. (plays/promote-to-accounts.ts) The shared accounts model keys on website. A domainless account cannot be deduped, so it forks into duplicates the first time the same company appears in another list. This is why a row that cannot be resolved is dropped rather than written.
  • Every search is counted before it is extracted. (models/salesnav-companies.ts) Extracting blind is how you take a silently truncated list: the search holds 4,000 companies, the extractor takes 1,000, and nothing tells you the rest exist.
  • No sub-search sits at or above the extraction cap. (models/salesnav-companies.ts) A search returning exactly the cap is truncated. That is the failure this whole skill exists to prevent, and it is invisible unless you check the number.

Done when

  • the sub-search extractions sum to roughly the count of the original whole-market search
  • no sub-search returned exactly the cap, which would mean it is still truncated
  • promoted accounts appear in accounts with a website, deduped across overlapping sub-searches

What it costs

Counting is a fraction of a credit and is the cheapest insurance here. Promotion is the expensive step: every promoted company costs two credited calls (matchBusiness + enrichBusinessFirmographics), because Sales Nav returns no domain and both are needed to resolve one. A 5,000-company TAM is roughly 10,000 enrichment calls. That is the price of the outcome, not waste, but it is real money and it should be spent on purpose.

The control is upstream, where the rows are created: config.limit and how many sub-search URLs you add. To spend less, extract less. Start with one small sub-search, watch it land correctly, then widen.

Composes into

list-building (the same split tactic for people), contact-sourcing (the buyers at every account you just built), account-scoring, signal-based-tam (watch the universe you just built).

相关技能

Build a total addressable market list of companies filtered by industry, headcount, and geography, powered by Cargo. Triggers: "build a TAM list", "how many companies match our ICP", "list every SaaS company in Europe under 200 employees", "size our addressable market", "find target accounts", "list building", "build a list of companies". Providers: salesNavigator. Skip when: you want the people at those companies — use find-b2b-leads or find-stakeholders; or you want companies by tech stack — use find-companies-using-tech.

Score a list of companies against your ideal customer profile and rank them, powered by Cargo — every row gets a number, the reason behind it, and a tier, so the bottom of the list can be dropped before anyone spends time on it. Triggers: "score these leads", "which of these fit our ICP", "rank this list", "prioritise these accounts", "who should we go after first", "disqualify the bad ones", "tier this list". Firmographic fit, thresholds, tiering, prioritisation. Skip when: you have no list yet and need one built — use build-tam-list or find-b2b-leads; or you want people inside an account rather than a verdict on the account — use find-stakeholders.

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