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Agent Skill · 研究检索

cargo-orchestration(货物编排)

cargo-orchestration 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

cargo-orchestration 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 57,063。

总安装 441周安装 18GitHub Stars 12下载 141核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:cargo-orchestration(货物编排)
来源仓库:https://github.com/getcargohq/cargo-skills
仓库路径:skills/cargo-orchestration
安装命令:
npx skills add https://github.com/getcargohq/cargo-skills --skill cargo-orchestration
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/getcargohq/cargo-skills --skill cargo-orchestration

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

cargo-orchestration 提供对 Cargo 平台运行时操作的编排能力,支持批量任务与工作流调度。适用于自动化部署、多步骤构建或跨组件协调等复杂开发流程的管理场景。通过 action execute 或 run create 等指令触发具体操作,需预先定义节点或工作流。

使用前应熟悉 Cargo 工作区结构,并确认 API 令牌与权限范围以避免越权调用。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/cargo-orchestration

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/getcargohq/cargo-skills --skill cargo-orchestr…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

cargo-orchestration 怎么安装?

在终端执行 npx skills add https://github.com/getcargohq/cargo-skills --skill cargo-orchestration。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

cargo-orchestration 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

cargo-orchestration 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

cargo-orchestration 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

cargo-orchestration 用的人多吗?

公开数据是累计安装 441 次,近一周安装 18 次,下载 141 次,GitHub 12 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

cargo-orchestration 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
cargo-orchestration

货物编排 · 本页

44112接触敏感数据未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Cargo CLI — Orchestration

Runtime operations for the Cargo platform.

What do you want to run?

Need to run something?
├── One action, one record       → action execute
├── One action, many records     → action execute-batch
├── Multiple actions chained
│   ├── One-off / ad-hoc         → run create --nodes (one record)
│   │                              batch create --nodes (many records)
│   └── Reusable workflow        → build a tool, then run create --workflow-uuid
│                                  or batch create --workflow-uuid
└── Conversational AI agent      → message create
Terminology: An orchestration tool is a saved on-demand workflow (listed via tool list). An action is a single operation you execute without building a workflow — it can embed a saved orchestration tool (kind: "tool"), call a third-party connector (kind: "connector"), invoke an AI agent (kind: "agent"), or run a built-in platform operation (kind: "native").

References:

references/actions.md — action execute and execute-batch examples references/tools.md — tool (on-demand workflow) examples references/plays.md — play (segment-driven automation) examples references/agents.md — AI agent chat examples references/nodes.md — full node creation guide (kinds, native actions, expressions, validation, routing) references/templates.md — pre-built workflow templates references/queries.md — storage query execute SQL examples references/segments.md — segment fetch and filter examples references/response-shapes.md — full JSON response structures references/filter-syntax.md — complete filter condition reference references/polling.md — async polling patterns, error handling, retry strategies references/troubleshooting.md — common errors, plus a "Debugging a workflow run" section for runs that succeed but produce wrong output (wrong-branch routing, empty downstream values)

Prerequisites

npm install -g @cargo-ai/cli
cargo-ai login --oauth                                  # browser sign-in (recommended)
# or: cargo-ai login --token <your-api-token>           # workspace-scoped API token (non-interactive)
# Pin a default workspace at login (with --oauth)
cargo-ai login --oauth --workspace-uuid <uuid>

Verify with cargo-ai whoami. All commands output JSON to stdout. Without a global install, prefix every command with npx @cargo-ai/cli instead of cargo-ai.

Failed commands exit non-zero and return {"errorMessage": "..."}.

Discover resources first

Most commands require UUIDs. Always discover them before acting.

cargo-ai orchestration play list            # all plays (name, workflowUuid, modelUuid, segmentUuid)
cargo-ai orchestration tool list            # all tools (name, workflowUuid, description)
cargo-ai orchestration workflow list        # all workflows (uuid only — no name)
cargo-ai orchestration template list       # all workflow templates (slug, name, kind)
cargo-ai ai agent list                     # all agents (uuid, name)
cargo-ai ai template list                  # all AI agent templates (slug, name, languageModelSlug)
cargo-ai storage model list                # all models (uuid, name, slug, columns)
cargo-ai storage dataset list              # all datasets
cargo-ai segmentation segment list         # all segments (uuid, name, modelUuid)
cargo-ai system-of-record sor list         # all systems of record
cargo-ai connection connector list         # all connectors

Plays vs tools: Both are backed by a workflow. A play is a segment-driven automation — it reacts to data changes in a segment (records added, updated, removed). A tool is an on-demand workflow — triggered manually, via API, or on a cron schedule. Workflows don't have a name field; use play list or tool list to find names and extract the workflowUuid.

Retrieve in the UI: plays live at app.getcargo.io/workspaces/<WORKSPACE_UUID>/plays/<PLAY_UUID> and tools at app.getcargo.io/workspaces/<WORKSPACE_UUID>/tools/<TOOL_UUID>. Get <WORKSPACE_UUID> from cargo-ai whoami under workspace.uuid.

Designing a new tool or play? Check templates first — they are pre-built node graphs for common automation patterns (enrichment pipelines, CRM syncs, lead scoring) and are an excellent starting point. List templates with cargo-ai orchestration template list and inspect a specific one with cargo-ai orchestration template get <slug>. Templates are tagged by kind so you can find ones suited for tools ("kind":"tool") or plays ("kind":"play") right away. See references/templates.md for the full guide.

Compatibility rules:

  • run create — only works with tool workflows (or no workflowUuid). Play workflows return playNotCompatible.
  • batch create — allowed data kinds depend on the workflow type:

- Play workflows: segment, change, filter, recordIds - Tool workflows (or no workflowUuid): file, records

Quick reference

# Single actions
cargo-ai orchestration action execute --action '{"kind":"tool","toolUuid":"<uuid>","config":{}}' --data '{"domain":"acme.com"}'
cargo-ai orchestration action execute-batch --action '{"kind":"connector","integrationSlug":"clearbit","actionSlug":"company_enrich","config":{}}' --records '[{...},{...}]'

# Workflows (chain multiple actions)
cargo-ai orchestration run create --workflow-uuid <uuid> --data '{"company":"Acme","domain":"acme.com"}'
cargo-ai orchestration run create --data '{"domain":"acme.com"}' --nodes '[...]'
cargo-ai orchestration batch create --workflow-uuid <uuid> --data '{"kind":"segment","segmentUuid":"..."}'

# AI agents
cargo-ai ai message create --chat-uuid <uuid> --parts '[{"type":"text","text":"..."}]'

# Data
cargo-ai storage query execute "SELECT * FROM default.companies LIMIT 10"
cargo-ai segmentation segment fetch --model-uuid <uuid> --filter '{"conjonction":"and","groups":[]}' --fetching-limit 100
cargo-ai storage model get-ddl <model-uuid>

Polling async operations

All operations are asynchronous. Either poll until terminal state, or pass --wait-until-finished to block.

action execute returns a run. action execute-batch returns a batch. They poll the same way:

Result typePoll commandIntervalDone when
Runrun get <uuid>2sstatus is success, error, or cancelled
Batchbatch get <uuid>5sstatus is success, error, or cancelled
Agent messagemessage get <uuid>2sstatus is success or error

For long-running batches (1000+ records), increase the interval to 10-15s after the first minute.

Execute actions

Run a single action — no workflow or node graph needed.

# One action, one record → returns a run
cargo-ai orchestration action execute \
  --action '{"kind":"connector","integrationSlug":"clearbit","actionSlug":"company_enrich","config":{}}' \
  --data '{"domain":"acme.com"}' \
  --wait-until-finished

# One action, many records → returns a batch
cargo-ai orchestration action execute-batch \
  --action '{"kind":"tool","toolUuid":"<tool-uuid>","config":{}}' \
  --records '[{"domain":"acme.com"},{"domain":"globex.com"}]' \
  --wait-until-finished

Action kinds: tool, connector, agent, native. See references/actions.md for all action kinds, parameters, retry config, response shapes, and end-to-end examples.

Create a run

A run processes a single record through a workflow. Use run create when you need to chain multiple actions together via a node graph, or when running an existing tool workflow.

Runs only work with tool workflows. Play workflows return playNotCompatible — use batch create instead.

cargo-ai orchestration run create \
  --workflow-uuid <tool.workflowUuid> \
  --data '{"company":"Acme","domain":"acme.com"}'
# → Poll with: cargo-ai orchestration run get <run-uuid>

# Or wait synchronously — blocks until the run reaches a terminal state and returns the final result
cargo-ai orchestration run create \
  --workflow-uuid <tool.workflowUuid> \
  --data '{"company":"Acme","domain":"acme.com"}' \
  --wait-until-finished

Also supports --release-uuid to pin a specific release.

Cancelling runs:

cargo-ai orchestration run cancel --workflow-uuid <uuid> --uuids run-uuid-1,run-uuid-2

See references/tools.md for file uploads, monitoring, and cancellation. See references/nodes.md for custom node graphs.

Create a batch

Batches process multiple records at once. Allowed data kinds depend on the workflow type:

  • Play workflows: segment, change, filter, recordIds
  • Tool workflows (or no workflowUuid): file, records
# Play workflow — run on a segment
cargo-ai orchestration batch create \
  --workflow-uuid <play.workflowUuid> \
  --data '{"kind":"segment","segmentUuid":"..."}'

# Tool workflow — run on a file
cargo-ai orchestration batch create \
  --workflow-uuid <tool.workflowUuid> \
  --data '{"kind":"file","s3Filename":"..."}'
# → Poll with: cargo-ai orchestration batch get <batch-uuid>

# Or wait synchronously — blocks until the batch reaches a terminal state and returns the final result
cargo-ai orchestration batch create \
  --workflow-uuid <play.workflowUuid> \
  --data '{"kind":"segment","segmentUuid":"..."}' \
  --wait-until-finished

Downloading results: get the releaseUuid from batch get, then cargo-ai orchestration release get <release-uuid> to find nodes[].slug, then cargo-ai orchestration batch download --uuid <batch-uuid> --output-node-slug <slug>.

Cancelling a batch:

cargo-ai orchestration batch cancel <batch-uuid>

See references/plays.md and references/tools.md for filtering, record IDs, file uploads, monitoring, and cancellation.

Send a message to an AI agent

cargo-ai ai agent list                                    # 1. Find the agent
cargo-ai ai chat create \                                 # 2. Create a chat
  --trigger '{"type":"draft"}' \
  --agent-uuid <agent-uuid> --name "Research session"
cargo-ai ai message create \                              # 3. Send a message
  --chat-uuid <chat-uuid> \
  --parts '[{"type":"text","text":"Find the VP of Sales at Acme Corp"}]'
# → Extract assistantMessage.uuid, poll with: cargo-ai ai message get <uuid>
#   Done when .message.status is "success" (read .parts) or "error" (read .errorMessage)

Also supports --actions, --resources, --language-model-slug, --temperature, --max-steps, and --wait-until-finished (blocks until the assistant message reaches a terminal status). See references/agents.md for multi-turn conversations, action/resource injection, and model selection.

Inspect records

Records are individual items processed by a workflow. Use these commands to list, count, download, or cancel records within a workflow.

# List records for a workflow
cargo-ai orchestration record list --workflow-uuid <uuid> --limit 50

# Filter by batch or status
cargo-ai orchestration record list --workflow-uuid <uuid> --batch-uuid <uuid> --statuses error

# Count records
cargo-ai orchestration record count --workflow-uuid <uuid>

# Download records as a file
cargo-ai orchestration record download --workflow-uuid <uuid>

# Get per-node execution metrics
cargo-ai orchestration record get-metrics --workflow-uuid <uuid>

# Cancel records
cargo-ai orchestration record cancel --workflow-uuid <uuid> --ids record-id-1,record-id-2

Query the system of record

Run SQL against your connected data warehouse with storage query execute. Tables are referenced as <datasetSlug>.<modelSlug> and rewritten to the underlying warehouse table under the hood — no DDL lookup needed for the table name.

cargo-ai storage query execute \
  "SELECT name, domain FROM default.companies LIMIT 10"
# → { "rows": [...] } on success; non-zero exit with { "errorMessage": "..." } on error

Get column slugs from storage column list --model-uuid <uuid> (or run storage model get-ddl <model-uuid> for full schema). Page through large result sets with LIMIT / OFFSET directly in the SQL. For full exports, use cargo-ai storage query download "<sql>". For SoR docs, use cargo-ai system-of-record client get-documentation. See references/queries.md for WHERE clauses, aggregations, joins, date queries, and pagination.

Fetch segment data

Retrieve live records from a segment. IMPORTANT: requires --model-uuid (not --segment-uuid). Get the modelUuid from segment list. Filter JSON uses conjonction (not conjunction) — this is intentional.

cargo-ai segmentation segment fetch \
  --model-uuid <uuid> \
  --filter '{"conjonction":"and","groups":[]}' \
  --fetching-limit 100 --fetching-offset 0

Supports --sort, --enrich, and --sync. See references/filter-syntax.md for the full filter syntax and references/segments.md for filtering, pagination, sorting, enrollment filters, and enrichment.

Managing segments:

# Update a segment's name or filter
cargo-ai segmentation segment update --uuid <segment-uuid> --name "Updated Name"
cargo-ai segmentation segment update --uuid <segment-uuid> --filter '{"conjonction":"and","groups":[...]}'

# Remove a segment (fails if linked to a workflow)
cargo-ai segmentation segment remove <segment-uuid>

Use a workflow template

Templates are pre-built node graphs for common automation patterns (enrichment pipelines, CRM syncs, lead scoring). Browse with template list, inspect with template get <slug>, fill in placeholders, validate, and run.

cargo-ai orchestration template list              # list available templates
cargo-ai orchestration template get <slug>        # get template nodes + config

See references/templates.md for the full guide including placeholder conventions and end-to-end examples.

Validate and test nodes

Always validate custom node graphs before running them.

cargo-ai orchestration node validate --nodes '[...]'
# → { "outcome": "valid" } or { "outcome": "notValid", "invalidNodes": [...] }

For debugging, use node compute (dry-run expressions) or node execute (live test, costs credits). For runs that complete with status: success but produce wrong output (wrong branch taken, empty downstream values), use run.executions[].title from run get only as a quick summary — it may be truncated — and read runContext.<nodeSlug> (returned at the top level of the same run get <run-uuid> response) to verify field-level data. See references/troubleshooting.md → "Debugging a workflow run" and references/nodes.md for the full node creation guide, validation error codes, and examples.

Help

Every command supports --help:

cargo-ai orchestration run create --help
cargo-ai orchestration template list --help
cargo-ai orchestration node validate --help
cargo-ai ai message create --help
cargo-ai system-of-record client --help

平台分布

Codex

39.54%
按下载量换算56

Claude

28.94%
按下载量换算41

Cursor

18.57%
按下载量换算26

Gemini CLI

8.85%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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