cargo-quickstart

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Cargo Quickstart — first value in two minutes

Cargo 快速入门——两分钟内获得实际价值

One guided demo: pull ~25 fresh leads matching a buyer persona the user picks, show the cost receipt, then save the pull as a recurring play. The point is not the list — it's that in minute 3 the user owns a running system, not a one-off result.

一次引导式演示：获取约25条符合用户所选买家画像的全新销售线索，展示费用凭证，然后将该获取任务保存为定期执行的任务。重点不在于这份线索列表——而是在第3分钟时，用户拥有的是一个可运行的系统，而非一次性结果。

The one question

唯一问题

Ask exactly one question before doing anything:

"Who do you sell to?" (a persona in a few words — e.g. "Heads of RevOps at mid-market SaaS")

Everything else — provider, filters, limits — you decide. Don't ask about output format, volume, or providers; defaults below.

在执行任何操作前，只需问一个问题：

「你的销售对象是谁？」（用几个词描述画像——例如：「中型SaaS公司的RevOps负责人」）

其他所有事项——供应商、筛选条件、数量限制——均由你决定。不要询问输出格式、数量或供应商；使用下方默认设置。

Speed budget — HARD RULES

时间预算——严格规则

The demo has a two-minute budget from answer to deliverable. On the fast path:

No discovery detours. Do not run
```
cargo-ai --version
```
,
```
cargo-ai whoami
```
,
```
connection connector list
```
, or any exploratory command first. Auth problems will surface as errors on the first real call — handle them then.
One command block per step, no narration between commands.
Paid work is capped at ~1 credit total. The demo uses the cheapest sourcing action in the catalog (
```
salesNavigator.searchLeads
```
, 0.02/record → 25 records ≈ 0.5 credits). Nothing else paid runs without asking.
Never dead-end. Every step has a fallback (ladder below). If a rung fails, drop one rung silently and keep moving.

从用户回答问题到交付成果，演示需控制在两分钟内。快速流程要求：

不得偏离探索流程。不得先运行
```
cargo-ai --version
```
、
```
cargo-ai whoami
```
、
```
connection connector list
```
或任何探索性命令。认证问题会在首次实际调用时以错误形式呈现——届时再处理即可。
每步一个命令块，命令之间无需解说。
付费操作总费用上限约为1个积分。演示使用目录中成本最低的获取操作（
```
salesNavigator.searchLeads
```
，每条0.02积分→25条≈0.5积分）。未经询问，不得执行其他付费操作。
不得陷入死胡同。每一步都有备选方案（如下方阶梯）。若某一级失败，直接降级到下一级，继续推进。

Fast path

快速流程

Translate the persona into a

searchLeads

filter (quote the exact title phrase in

keywords

; a bare keyword matches loosely and pollutes the page) and run:

bash

undefined

将用户描述的画像转换为

searchLeads

筛选条件（在

keywords

中使用引号包裹准确的职位短语；裸关键词匹配范围过宽，会导致结果杂乱），然后运行以下命令：

bash

undefined

1. Execute — returns a run object; note run.uuid and run.workflowUuid.

1. 执行——返回一个运行对象；记录run.uuid和run.workflowUuid。

searchLeads returns a 25-row page minimum (limit below 25 still bills 25 × 0.02 = 0.5 credits).

searchLeads最少返回25行结果（即使limit设置低于25，仍按25×0.02=0.5积分计费）。

cargo-ai orchestration action execute
--action '{"kind":"connector","integrationSlug":"salesNavigator","actionSlug":"searchLeads","config":{}}'
--data '{"keywords": ""<persona title phrase>"", "limit": 25}'
--wait-until-finished > /tmp/quickstart-run.json

2. Fetch the output data (NOT in the execute stdout) — signed URL, then filter to THIS run

2. 获取输出数据（不在执行命令的标准输出中）——获取签名URL，然后筛选当前运行任务的结果

RUN_UUID=$(jq -r '.run.uuid' /tmp/quickstart-run.json) WF_UUID=$(jq -r '.run.workflowUuid' /tmp/quickstart-run.json) curl -s "$(cargo-ai orchestration run download-outputs
--workflow-uuid "$WF_UUID" --output-node-slug action --format json | jq -r '.url')" \

/tmp/quickstart-outputs.json

3. Show the table (the file holds ALL of the workflow's runs — filter by _uuid;

3. 展示表格（文件包含工作流的所有运行结果——按_uuid筛选；

each row's .output is the leads array directly, fields are snake_case)

每行的.output直接是线索数组，字段采用蛇形命名法）

jq -r --arg u "$RUN_UUID"
'[.[] | select(._uuid==$u)][0].output[] | [.full_name, .job_title, .company_name, (.recently_hired // false)] | @tsv'
/tmp/quickstart-outputs.json | head -25


Show the table (name · title · company · recently-hired), not the raw JSON. `recently_hired: true` rows are the demo's headline — lead with them ("6 of these 25 just started the job — the exact moment to reach out").

jq -r --arg u "$RUN_UUID"
'[.[] | select(._uuid==$u)][0].output[] | [.full_name, .job_title, .company_name, (.recently_hired // false)] | @tsv'
/tmp/quickstart-outputs.json | head -25


展示表格（姓名 · 职位 · 公司 · 是否新入职），而非原始JSON数据。`recently_hired: true`的行是演示的亮点——优先展示这些内容（「这25条线索中有6条刚入职——正是联系的最佳时机」）。

Fallback ladder (on auth/error, drop a rung — don't stop)

备选方案阶梯（若认证失败/出错，降级执行——不要停止）

```
salesNavigator.searchLeads
```
(0.02/record) — primary.
```
theirStack.searchJobs
```
(0.5) — reframe as "companies hiring your persona right now" (job postings for the persona's title). Same wow, different angle.
```
waterfall.searchProspects
```
(3/record) — exceeds the ~1-credit demo cap, so this rung asks first: "The two cheap sources aren't connected; I can pull 5 matches via waterfall for ~15 credits instead — run it, or connect Sales Navigator first (free)?" Run only on an explicit yes, with
```
limit
```
capped at 5.
Nothing connected at all → run the free path:
```
cargo-ai connection integration list | head
```
, show what could be wired, and offer to connect one (browser auth) — the demo resumes after.

```
salesNavigator.searchLeads
```
（每条0.02积分）——首选方案。
```
theirStack.searchJobs
```
（0.5积分）——重新定位为「当前正在招聘你目标人群的公司」（对应职位的招聘信息）。同样惊艳，只是角度不同。
```
waterfall.searchProspects
```
（每条3积分）——超出约1个积分的演示上限，因此使用此方案前需先询问：「两个低成本数据源未连接；我可以通过waterfall获取5条匹配线索，费用约为15个积分——是否执行，还是先免费连接Sales Navigator？」仅在用户明确同意后执行，且
```
limit
```
上限设为5。
未连接任何数据源→执行免费流程：运行
```
cargo-ai connection integration list | head
```
，展示可连接的数据源，并提供连接其中一个的服务（浏览器认证）——完成连接后继续演示。

The receipt (mandatory, verbatim discipline)

费用凭证（强制要求，严格执行）

The demo is itself the pilot from

../cargo-gtm/references/cost-discipline.md

. Close it with a receipt:

Credits spent + balance remaining (

cargo-ai billing subscription get

— remaining =

subscriptionAvailableCreditsCount − subscriptionCreditsUsedCount

Hit-rate: "25 of 25 returned" (or what actually came back, and which rows look off).

本次演示本身是

../cargo-gtm/references/cost-discipline.md

中的试点项目。演示结束时需展示费用凭证：

已花费积分 + 剩余积分（运行

cargo-ai billing subscription get

——剩余积分 =

subscriptionAvailableCreditsCount − subscriptionCreditsUsedCount

）。

命中率：「25条请求返回25条结果」（或实际返回数量，以及哪些结果看起来不符合要求）。

Minute 3 — save it as a play

第3分钟——保存为定期任务

Immediately offer to make the pull recurring — this is the step that shows what Cargo is:

"Want this to run by itself? I can save this exact search as a play that runs weekly and writes new matches into a model — new
<persona>
leads land without you asking."

On yes, follow

../cargo-gtm/recipes/save-as-play.md

with the demo's action + filter as the workflow body and a weekly cron.

立即提议将该线索获取任务设置为定期执行——这一步能展现Cargo的核心价值：

「想要让这个任务自动运行吗？我可以将这个搜索任务保存为每周执行的定期任务，将新匹配的线索写入模型——无需你操作，新的<画像>线索就会自动生成。」

若用户同意，按照

../cargo-gtm/recipes/save-as-play.md

的步骤，将演示中的操作+筛选条件作为工作流内容，设置每周的定时任务。

After the demo — route onward

演示结束——后续指引

Propose 2–3 next steps grounded in the rows just pulled, per the next-step spec in

../cargo-gtm/SKILL.md

(§4): e.g. "enrich these 25 with firmographics (~0.5 cr each)", "find + verify emails for the best 10 (~1.4 cr each)", or something else entirely. From here, real GTM work belongs to

cargo-gtm

— read it before anything beyond the demo.

根据

../cargo-gtm/SKILL.md

（第4节）中的后续步骤规范，基于刚获取的线索，提出2-3个后续步骤建议：例如「为这25条线索添加企业画像信息（每条约0.5积分）」、「为最优质的10条线索查找并验证邮箱（每条约1.4积分）」，或其他合适的操作。从这里开始，实际的GTM工作由

cargo-gtm

负责——在开展演示之外的工作前，请先阅读该文档。