以 AI 机器人身份加入视频会议,提供语音、虚拟形象与屏幕共享四种模式。
编程
cargo-quickstart
试用Guided first-run demo for Cargo — one persona question to 25 real leads with a cost receipt in under two minutes, ending by saving the pull as a recurring play. Triggers: "show me what Cargo can do", "give me a demo", "take me on a tour", "quickstart", "getting started with Cargo", "I just installed Cargo", "my workspace is empty", "does this actually work". Skip when: the user has a real job to run (build a list, enrich a CSV, find emails) — use cargo-gtm; when they want CLI reference or routing — use the cargo router skill.
它能做什么
Cargo Quickstart — first value in two minutes
技能文档
Cargo Quickstart — first value in two minutes
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.
The one question
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.
Speed budget — HARD RULES
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.
Fast path
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:
# 1. Execute — returns a run object; note run.uuid and run.workflowUuid.
# searchLeads returns a 25-row page minimum (limit below 25 still bills 25 × 0.02 = 0.5 credits).
cargo-ai orchestration action execute \
--action '{"kind":"connector","integrationSlug":"salesNavigator","actionSlug":"searchLeads","config":{}}' \
--data '{"keywords": "\"\"", "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
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;
# each row's .output is the leads array directly, fields are snake_case)
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").
Fallback ladder (on auth/error, drop a rung — don't stop)
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, withlimitcapped 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.
The receipt (mandatory, verbatim discipline)
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).
Minute 3 — save it as a play
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 `` 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.
After the demo — route onward
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.
相关技能
诊断生产力系统反复失效的根因,给出最小干预——容量测算、瓶颈定位、可靠的本地记录。
把自然语言描述转为结构化 JSON,并由 mcp-diagram-generator MCP 服务生成 Draw.io、Mermaid 或 Excalidraw 图表文件。
在本地磁盘以分类纯 Markdown 文件保存需要长期留存的事实,与智能体内置记忆并存。
编写、调试与调优 Playwright 测试,涵盖定位器策略、追踪诊断与 CI 友好的超时配置。
从 AdMapix API 拉取广告创意、应用、榜单和收入预估等数据,原样返回结构化 JSON。
cargo-ai 的更多技能
浏览全部技能读写团队的 GTM 知识库——一个由 Git 管理的 Markdown 仓库,记录 ICP、人设、玩法、证据、异议等。
用一个 CLI 表面执行、构建、绘制并查询 Cargo 工作流、动作、批量与 AI 代理。
查找、认证并配置 Cargo 工作流节点所需的外部系统连接器。
检查并修改 Cargo 工作区的数据模型,并对存储运行 SQL 查询。
在 Cargo 上跑 B2B 拓客、联系人补全、线索打分与 CRM 同步,每一步都内置授权基础与抑制名单校验。
通过 Cargo CLI 管理 AI agent、release、MCP server 与 memory。