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cargo-project

该 Skill 指导用 TypeScript 的 define* 构建器声明 Cargo 工作区的连接器、模型、Play、工具、Agent 等资源,并通过 cargo-ai project 的初始化、类型生成、计划、部署、导入、回滚与状态管理实现可重复的 IaC 流程;还提供配置指南、配方、示例和故障排查。

getcargohq
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最近更新 2026-10-19 13:21:41
分类:开发依赖:代码执行 / 读写文件 / 第三方 API应用场景:代码生成 / API 集成 / DevOps / 工作流 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add getcargohq/cargo-skills/cargo-project
Metadata
name
cargo-project
description
Declare a whole Cargo workspace in a project, GTM as Code — connectors, models, plays, tools, agents, MCP servers, segments, context, folders, files, workers, and apps in TypeScript, reconciled with `cargo-ai project` (init → types → plan → deploy), the way you would run Pulumi or Terraform. Triggers: "as code", "in git", "version-controlled", "reproducible", "Terraform for Cargo", "set up a whole workspace", "staging and production", "deploy the workspace from CI", "review this in a PR", "cargo.json", "cargo.state.json", "scaffold from a template", "is there a cookbook for this", "start from a cookbook". Skills with a worked project example (TAM building, account scoring, call capture, inbound qualification, CRM enrichment, standup) live in gtm-skills; menu in references/cookbooks.md. Skip when: it is a one-off operation, a read, or an ad-hoc query — use the matching capability skill.
version
4.0.0
compatibility
Requires @cargo-ai/cli (npm). Sign in or create an account with `cargo-ai login --email` (emailed code, no browser), `--oauth`, or an API token
homepage
https://github.com/getcargohq/cargo-skills
metadata
{"author":"getcargo","openclaw":{"requires":{"bins":["cargo-ai"]},"install":[{"kind":"node","package":"@cargo-ai/cli@latest","bins":["cargo-ai"]}],"homepage":"https://github.com/getcargohq/cargo-skills"}}

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).

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).

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

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

When the task involves… Read this first What it gives you
Writing define* files, wiring resources, secret()/env(), defineWorkflow bodies (tool/play logic) guides/authoring-resources.md The builder catalog, the handle/ref model, secrets, and how workflow bodies compile.
plan / deploy / destroy, the state file, drift, adopting existing resources, CI guides/deploy-and-state.md The deploy lifecycle, cargo.state.json semantics, drift/import/rollback, async builds.
Typed config, cargo-ai project types, tsconfig wiring, uses.<key>.<action> connector calls in workflow bodies guides/typed-config.md What project types generates and how to wire it into your project.
A field/spec/output for a specific builder references/resources.md Every builder → spec fields → which ref each takes → outputs.
Exact command flags references/commands.md Every cargo-ai project subcommand and its flags.
A deploy error / footgun references/troubleshooting.md The known failure modes and fixes.
A known GTM outcome, before authoring one; or what to suggest after one is set up references/cookbooks.md The cookbook menu, in stage order: gtm-skills that carry a worked project example, the adaptations each supports, and which to suggest next.

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.

Caveat: the examples typecheck, but they are not yet deploy-verified against a live workspace, and every one is to-be-approved. Treat each skill's Done when as the acceptance test, and always review cargo-ai project plan before deploying.

Recipes — follow step-by-step when one matches

Recipe Use when…
recipes/scaffold-a-project.md Standing up a new project from scratch (init → types → plan → deploy).
recipes/add-connector-and-model.md Adding a data source + a model sourced from it, wired by handle.
recipes/build-an-agent.md Composing a model + tool + agent (wired through uses) and deploying.
recipes/migrate-existing-workspace.md Bringing an already-live workspace under a project via project import.
recipes/deploy-from-ci.md Deploying non-interactively from CI (token auth + committed state).

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
    

    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
    

    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). 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
    

    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 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.

  • 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 rewrites a DNS zone. 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 whole zone, not a patch: declaring it replaces every live record (including the ones the registrar wrote at purchase), and omitting it leaves the zone untouched. redirectUrl, dmarcEmail and dmarcPolicy are additive and are the supported way to configure a zone without replacing it, so reach for dnsRecords only when you mean to own every record. Use adopt: true for a domain or mailbox bought in the UI. 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).