Cargo project — a Cargo workspace, declared in a repo

Use this skill to define a Cargo workspace in TypeScript (define* builders from @cargo-ai/cdk) and reconcile it to live infrastructure with cargo-ai project deploy. It is the declarative counterpart to the imperative capability skills: instead of running one CLI command per resource, you write the whole graph once and deploy it repeatably, with a deploy state — held in your workspace, pointed at by a committed cargo.state.json — linking your code to what Cargo created. A project is the folder holding cargo.json ({ "resources": "infra" }): every cargo-ai project command finds the nearest one above where it runs, and stops without one.

"Project" and "workspace" are two different things — keep them apart. A workspace is the Cargo tenant you sign into: it holds the models, connectors, agents, mailboxes and credits, and it is what login --workspace-name selects and what whoami reports. A project is the folder holding cargo.json that declares resources and deploys them into a workspace: it is what project init scaffolds, where cargo.state.json sits, and what every command finds by walking up to the nearest cargo.json. A repo can hold several, one cargo.json each. One project deploys into one workspace at a time — the workspace guard refuses a deploy when the state's workspace isn't the selected one — and the same project reaches a dev and a prod workspace through a different deploy state each. Say "project" for it, and don't name a project directory my-workspace.

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
cargo-ai project --help                     # `unknown command` = CLI too old; reinstall @cargo-ai/cli@latest

Two project-specific extras: the project needs @cargo-ai/cdk as a dependency for the define* builders you import (cargo-ai project init scaffolds a package.json with it — then npm install), and the cargo-ai project domain ships with the CLI itself.

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

• Authoring every Cargo resource with a define* builder that returns a handle; wiring resources by passing handles to each other (the dependency graph is your variable graph).
• Deploying the graph: plan (offline diff) → deploy (create/update, write state) → destroy (tear down). Plus drift (refresh), adoption (import), and recovery (rollback).
• Typing the config against your workspace's real integration schemas (cargo-ai project types).

A project spans every resource kind — so it overlaps every imperative capability skill (cargo-connection, cargo-storage, cargo-ai, cargo-orchestration, cargo-content, cargo-hosting, …). Which to reach for is the first decision:

2) A project or the CLI? — the routing decision

Declarative (this skill) vs imperative (a capability skill).

Use a project when the user is managing resources as an artifact:

• "Set up / stand up / bootstrap a whole workspace (as code / from a template)."
• "Make this reproducible / version-controlled / in git / repeatable across environments (dev → prod)."
• "Deploy these connectors + models + agents together" (a multi-resource graph wired by dependency).
• Anything that should be re-runnable and diffable, where losing the definition would be a problem.

Use the matching capability skill (imperative cargo-ai <domain>) when the user is doing a one-off operation or exploring:

• "Create one connector", "add a column to this model", "list connectors", "run this workflow", "query storage", "read this agent's memory."
• Any read, ad-hoc query, or single mutation that doesn't need to live in code.

When unsure, ask whether the result should be committed and re-deployable. If yes → a project. If it's a quick action or a read → the capability skill (see the cargo router to pick the right domain).

Before writing anything: restate it, then ask only what you cannot read

A project request is usually one sentence ("score our accounts every week"), and the gap between that sentence and a deployed graph is where a wrong build comes from. Close it before the first define* file, not after the plan.

1. Read first. cargo-ai whoami, connection connector list, storage model list, the project's existing infra/, and references/cookbooks.md. Never ask what one of those answers (which CRM is connected, whether a Companies model exists, whether a cookbook already does this).
2. Restate the build in four lines: what keeps arriving and where it lands; what triggers it (schedule, webhook, record change); what it reads and what it writes; what it spends on each run (the paid nodes, how many records, how often).
3. Ask at most three questions, only for what lives in the user's head: which records count (the segment), where the result goes (a channel, a CRM field), the volume or credit ceiling. Give each a proposed default, so "yes" answers all of them.
4. Wait for that yes before authoring. If the user already said to just build it, or the request names every piece, write the restatement as stated assumptions and carry on; the plan review is then where they correct it.

A cookbook's What you will be asked table is this step already worked out for that outcome: use it instead of improvising one.

3) The lifecycle

cargo-ai project init <dir>     scaffold the Cargo template: cargo.json + cargo.state.json, resources in infra/
        │
cargo-ai project types          generate per-workspace types for typed config (optional)
        │
   (author define* files)   importing a .ts file IS registration — no list to keep
        │
cargo-ai project plan           compile the graph, diff against the deploy state (creates nothing)
        │
cargo-ai project deploy         create/update resources in dependency order, write state
        │
cargo-ai project destroy        tear down resources recorded in state

project plan says what resources change; it doesn't show what a play does. For a definePlay / defineTool graph past three nodes, present a Mermaid flowchart of the node graph alongside the plan — routing, fallbacks, and which nodes bill on every scheduled run are what the reviewer is approving. Generate it from the deployed release after the first deploy, or from the node array while authoring: ../cargo-orchestration/references/node-diagram.md.

Side branches: cargo-ai project check (validate the tree, no state, no API calls — the CI/editor subset of plan) · cargo-ai project info (what is here: every declared resource, the file that declared it, whether it is deployed) · cargo-ai project refresh (read-only drift report) · deploy --refresh (re-apply code over out-of-band edits) · deploy --prune (delete resources removed from code) · cargo-ai project import <id> <uuid> (bind an existing live resource into state) · cargo-ai project pull (generate define* source from a live workspace and adopt it) · cargo-ai project rollback (restore the pre-deploy state snapshot) · cargo-ai project state (inspect and repoint the deploy state; state connect to deploy from GitHub) · cargo-ai project graph (Mermaid of the live workspace's resources, or --project [--plan] for the local code) · deploy --dry-run / --draft (preview / stage without going live).

plan and info answer different questions. plan answers what will deploy do — its unit is the change, and it exits non-zero so CI can gate on it. info answers what is here — its unit is the resource, and it never fails. check is plan minus the diff: it needs no state, so it is what an editor or a pre-commit hook runs. Running cargo-ai project with no subcommand prints its help.

4) Documentation hierarchy

• Level 1 — SKILL.md (this file): the decision model, lifecycle, critical rules, and routing.
• Level 2 — Guides: guides/authoring-resources.md, guides/deploy-and-state.md, guides/executions.md, guides/typed-config.md.
• Level 2.5 — Recipes: recipes/*.md — step-by-step playbooks to follow as your execution plan.
• References — references/resources.md (the full builder catalog), references/commands.md (every cargo-ai project subcommand + flags), references/troubleshooting.md, and references/examples/full-workspace.md.

5) Read behavior — match the task to a doc and READ IT

Cookbooks — check the menu before authoring a known outcome from scratch

getcargohq/gtm-skills holds, beside its one-off skills, cookbooks: skills that carry worked project resources, the same job as a deployed pipeline that keeps producing the result (TAM building, account scoring, call capture, inbound qualification, CRM enrichment, stalled-deal nudges, …). Every folder is self-contained: its own models, connectors and folders, no shared foundation, no requires graph.

The menu is local: references/cookbooks.md. Read it before authoring a common GTM outcome from scratch, and again once a cookbook is set up: it lists them in stage order, and that order is what to suggest next — the earliest cookbooks not set up yet. It is generated from gtm-skills' catalog.json, so it cannot drift.

A cookbook is a worked example, not a template to fill in. Each one declares in its SKILL.md what may be reshaped, what must hold or it stops working, and what has to be answered either way, and it carries its own procedure. project add is the copy step in that procedure:

cargo-ai project add cookbook/tam-building               # inside a project
cargo-ai project init my-project --cookbook tam-building # no project yet: both at once

That writes the resources to infra/tam-building/ and the procedure to .claude/skills/tam-building/, skipping any file it would overwrite. Then start the skill at its Adapt section — its opening steps are written for someone who found the folder on GitHub and still has to place it, so following them from the top scaffolds a second project and copies the folder in again.

What is left after the copy is the part only you can do: reconcile it with what is already declared, adapt the copied files in place to the project's real shape (do not regenerate them from the skill's prose — the safety lives in the TypeScript), plan and stop, deploy on a yes, walk its Done when.

If you are mid-task and the skill is not in this session, npx skills add getcargohq/gtm-skills/<slug> fetches the procedure alone and you can read .agents/skills/<slug>/SKILL.md directly; no reload needed. To read one without installing, npx skills use getcargohq/gtm-skills@<slug> prints it. Neither brings the project resources — for those you still want project add.

Routing rule: one-off versus standing. A user who wants the list today wants cargo-gtm (or gtm-skills' one-off build-tam-list); a user who wants a pipeline that keeps producing it wants tam-building. The same words describe both ("build our TAM"), so listen for whether the result is meant to keep arriving. A cookbook matches → install it and follow it. No match → author from the recipes below.

Never cargo-ai project init --force into a directory that is not empty. It replaces the project's package.json and reverts adapted code, while cargo.state.json survives, so the next plan diffs a live workspace against code nobody wrote. Copy the skill folder in as a sibling instead.

Each cookbook's state is in the menu's State column. approved means it was deployed and run on a live workspace and its results checked; to-be-approved means it typechecks and plans but has not been deploy-verified yet, so say so when you suggest it. Either way, treat the skill's Done when as the acceptance test and review cargo-ai project plan before deploying.

Recipes — follow step-by-step when one matches

6) Critical rules

• Commit cargo.state.json — it is a pointer now, not the state. The deploy state lives in your workspace; the repo commits only {"stateUuid": "8f2c…"}. project init creates the state and writes that pointer, so it lands in the scaffold commit. Commit it: without the uuid a fresh checkout can't find its state, and a deploy will not quietly create a replacement (that would orphan everything the old one tracks). The state records only uuids, hashes and outputs — never secret values. It is still the only handle on a deployed play, agent, or alert (they have no slug); recover a lost link with project state bind <uuid>, or per-resource with cargo-ai project import. Git-ignore the working files (project init scaffolds this):

  .cargo-ai/
  cargo.state.lock
  cargo.state.bak.json
  cargo.state.cache.json
  cargo.state.audit.jsonl
  

  Copy

  One state per project, and at most 10 live states per workspace:

  cargo-ai project state list            # every state here, and which one this project is on
  cargo-ai project state create          # create one for this project, write the pointer
  cargo-ai project state bind <uuid>     # repoint at an existing one
  

  Copy

  A project scaffolded before states moved to the workspace has the resource map inline in cargo.state.json, and keeps deploying against that file for as long as you leave it there — nothing migrates behind your back. state create is what moves it: the map goes onto a new state, the file becomes a pointer, and you commit it. Don't migrate without telling the team, since a teammate who deploys before pulling the new pointer is deploying from a state that no longer exists. (Deleting a state: cargo-ai project state remove <uuid> drops the state without touching the live resources it tracked; --force if cargo.state.json points at it.)

• Three ways to get a value in, and they are not interchangeable.

  • secret("ENV_VAR") — read from your environment at deploy time, excluded from the content hash and from state. Export the env var before deploying; a missing one fails the deploy with an unresolved ${ENV_VAR} placeholder.
  • env("ENV_VAR") — read from your environment at load time, and its value enters the spec and therefore the content hash, so changing it shows as drift. Use it for non-secret config you want tracked.
  • workspaceEnv("NAME") — a pointer at a workspace env-var catalog entry. Nothing is read locally and nothing can fail; the value stays server-side and is re-read on every use, so rotating it in the workspace reaches the resource with no redeploy. Manage the catalog with cargo-ai workspaceManagement envVar (see ../cargo-workspace-management/SKILL.md).

  secret() and workspaceEnv() are accepted at secret-typed schema positions only — cargo-ai project types prints the union at exactly those positions. A resource's own env block takes env() or secret() and rejects a pointer, because a worker, app, agent or script already inherits the whole workspace catalog; a pointer there could only restate a variable it can read anyway.

• A rotated secret() needs more than a plain deploy to land. Keeping the value out of the content hash is what stops rotation reading as drift — and it is also why deploy won't push the new value: nothing in the hash changed, so the resource isn't re-applied. Roll it with deploy --refresh, or any other edit to that resource. workspaceEnv() has no such step; that is most of its appeal.

• Wire by handle, never by .uuid. Pass a define* handle directly (connector: hubspot, uses: [enrich]), or xxRef("uuid") for a resource you didn't define in code (connectorRef, modelRef, folderRef, toolRef, agentRef, …). Where a reference needs per-call options, wrap it as { ref, …options } (e.g. uses: [{ ref: contacts, readOnly: true }]).

• Run cargo-ai project types after workspace integrations change — it regenerates .cargo-ai/ so defineConnector/defineModel config (and connector actions called through uses in workflow bodies) type-check against the real schemas. Typing is a bonus, never a gate: deploy works without it.

• A project is the folder holding cargo.json (@cargo-ai/cdk ≥ 1.0.112). The file names the folder whose .ts files are loaded, { "resources": "infra" }, and optionally where executions live, "executions": ["outputs"] (default outputs/ beside cargo.json, read at any depth; a * segment globs folders, e.g. ["outputs/*/executions"] — see guides/executions.md). Every command walks up from where it runs, as far as the repo root, to the nearest cargo.json, so any subdirectory works; a repo with several projects has one cargo.json each, and the nearest wins. Without one, the command stops — it no longer guesses from an infra/ folder or a package.json — and prints the line that adds it, at the project's root:

  echo '{ "resources": "infra" }' > cargo.json
  

  Copy

  cargo.state.json lives beside cargo.json, never inside infra/. A leftover infra/cargo.state.json stops every command with the mv to run (two state files stop it too; keep the one that points at this project's state). --dir <path> still exists: it says where the lookup starts. Resources live in infra/ and not at the root because the loader imports every .ts under the folder resources names, and a repo root also holds scripts/ — whose files run on import.

• --yes in CI. deploy and destroy prompt for confirmation; non-interactive runs must pass --yes.

• A connected GitHub repo deploys on merge — and on connect. cargo-ai project state connect plans every PR in a comment and runs deploy --yes on every push to the production branch, starting with the branch head the moment you connect (--no-auto-deploy to plan only). A merge whose plays and executions would exceed 100 emails, 100 LinkedIn actions, 100 chat messages, 5,000 audience adds or 2,500 credits — or can't be counted — fails Needs approval: large impact until someone labels the PR cargo:approve-impact. That label is the user's cost approval: add it only on their explicit yes. secret() does not resolve there (use workspaceEnv()), and while auto-deploy is on every other deploy of the state is refused (409 stateAutoDeployed) — remove a GitHub Actions deploy rather than running both. See recipes/deploy-from-ci.md.

• A definePlay/defineTool graph with paid nodes gets a sample run before it goes wide. Deploying is not running, but the first thing that runs a deployed play is usually a batch over the whole segment — and a scheduled play re-bills every node on every run. Before enrolling everything (or enabling a schedule), run the deployed workflow on 10–20 records — cargo-ai orchestration batch create --workflow-uuid <uuid> --data '{"kind":"filter","modelUuid":"…","filter":…,"limit":15}', or batch create --file ./plays/x.ts --data '…' to test-run the module without deploying — then ask the user to approve the full enrollment with the record count and credit estimate. Read the provider's playbook (../cargo-gtm/provider-playbooks/<slug>.md, esp. its Recurring use section) and the gate in ../cargo-gtm/references/cost-discipline.md.

• An execution runs exactly once, on the next deploy, with no undo and no retry. defineBatch / defineRun files under outputs/ (or the cargo.json executions folders) start a real batch or run after the resources apply; a play batch can send email and LinkedIn actions and spends credits, and reverting the code takes nothing back. A filter / segment execution over the whole audience is a full batch: pilot it first and get approval on the record count and credit estimate per ../cargo-gtm/references/cost-discipline.md before it reaches a deploy (or a merge on a connected repo). The ledger lives in the workspace-held state, so a refused or finished execution never re-runs — give it a new id; executions need workspace-held state and never run under --draft. See guides/executions.md.

• A defineAlert whose actions call paid nodes re-bills on every breach. An alert's actions fire as real runs, so a badly-sized threshold on a tight schedule can breach — and bill — every tick. Size the threshold with cargo-ai observability alert preview before deploying, prefer cheap notification actions (an agent that posts, a connector notification) over anything that fans out, and apply the same cost gate above when an action calls a credits-based provider. Scope/threshold and firing semantics: ../cargo-observability/SKILL.md.

• defineMailbox bills monthly, and defineDomain owns the records it publishes. A mailbox is 100–125 credits per month (shared/private 100, google 125) for as long as it exists (cargo-ai mailboxManagement pricing get for live figures), so a + create mailbox:… line in the plan is a recurring charge the user approves, not a one-off. Its domain, username and type are create-only — changing any of them is destroy + recreate, i.e. a brand-new inbox back at the bottom of a 45-day warm-up ramp. Before creating a mailbox the deploy polls the domain's refreshStatus for up to 5 minutes waiting for the domain to be active; it does not wait for the new mailbox, which stays pending until the provider issues credentials. On defineDomain, dnsRecords is the set of records the deploy manages, merged into the live zone: the records the registrar wrote at purchase and anything added outside the deploy stay put (bar one that can't share a name with a declared CNAME). The deploy owns what it published, so dropping a record from the list deletes it on the next deploy, and [] deletes every record earlier deploys published. Omit dnsRecords to leave the zone untouched; an entry may be an app's domainRecords. Edit a managed record in code, not the registrar UI: an outside edit stops the deploy (dns record … was edited outside the deploy). redirectUrl, dmarcEmail and dmarcPolicy left undeclared keep the live value. Use adopt: true for a domain or mailbox bought in the UI (an adopted domain owns no records until a deploy publishes some). Ramp, suppression and sending: ../cargo-mailbox-management/SKILL.md.

• Route project-managed resources into a clearly-labelled folder. Set folder: on each builder so everything the project owns lands in a dedicated folder whose name signals "owned by code — don't hand-edit" to anyone in the UI (manual UI edits read back as drift on the next plan). Folders are per-kind, so give each kind its own but share one short, recognizable prefix — recommended: 🔒 Project (e.g. 🔒 Project Models, 🔒 Project Agents). Keep names short (long labels truncate in the folder tree); the lock emoji is the "don't touch" cue. See guides/authoring-resources.md.

Help

• cargo-ai project --help and cargo-ai project <subcommand> --help for the live flag surface.
• When a documented command/flag/response doesn't match what you observe, file a report: cargo-ai workspaceManagement report create (see ../cargo-workspace-management/SKILL.md).