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Digital Employee

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mateaix
digital_employee

根据一句业务需求,自动规划并创建一组分工明确的数字员工(Agent),再把他们编排成一条工作流,无需逐个手工创建。

Overview

Publishermateaix
Repositorymateclaw
Skill namedigital_employee
Stars
1.1K
Forks
337
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mateaix on GitHub. Read the source before you install it.

Installation

Install the Digital Employee AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mateaix/mateclaw/tree/dev/mateclaw-server/src/main/resources/skills/digital_employee
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mateaix/mateclaw.git /tmp/mateclaw
mkdir -p .claude/skills
cp -r /tmp/mateclaw/mateclaw-server/src/main/resources/skills/digital_employee .claude/skills/digital_employee
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Digital Employee in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Digital Employee on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Digital Employee is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

数字员工组建

把一句业务需求,变成「一支分工明确的数字员工团队 + 一条把他们串起来的工作流」。用户不必逐个设计、逐个手工创建 Agent。

何时使用

  • 用户描述了一个需要多个角色协作的目标("帮我搭一个做竞品分析的团队"、"我要一套从线索到成交的销售流程")。
  • 用户说"建几个 Agent / 数字员工帮我做 X"、"把 X 这件事自动化成一支团队"。
  • 现有 Agent 不足以覆盖需求,需要新建专才角色。

不应使用

  • 一个 Agent 就能完成 → 直接用 chat_with_agent 或现有 Agent。
  • 只是想编排已存在的 Agent 协作 → 用 multi_agent_collaboration。
  • 只是想把流程做成工作流,且员工都已存在 → 直接用 workflow_draft_generate。

工作流程

第一步:理解需求,盘点现状

  1. 读懂用户的业务目标、产出物、是否有触发条件(定时 / 来消息时)、要不要审批、结果发到哪个渠道。
  2. 调用 listAvailableAgents() 看现有员工——能复用就复用,不要重复造同名角色。
  3. 调用 list_capability_catalog() 拿到可分配的技能名和工具名清单。后续 create_employee 只能用清单里的真实名字,不要臆造。

如果需求关键信息缺失(产出物、角色边界),先向用户澄清一句再继续,不要凭空假设。

第二步:设计团队(2–6 个角色)

把目标拆成互补的角色,每个角色给出:

  • name:工作区内唯一,用英文 kebab-case(如 market-research-analyst)。
  • description:一句话职责,工作流编排器会据此分配任务。
  • systemPrompt:定义这个员工的专长、视角、工作方式——要具体,别写空话。
  • skillNames / toolNames:从 list_capability_catalog() 里挑这个角色真正需要的;留空则继承全局默认能力(通才)。专才角色建议显式收窄。
  • agentType:默认 react;只有当角色需要"先规划再分步执行"时才用 plan_execute。

设计原则:

  • 角色数量 2–6 个,宁少勿滥;每个角色职责单一、边界清晰。
  • 避免两个角色职责重叠。
  • 一般不指定 modelName,留空用工作区默认模型;用户明确要求某模型时才填。

第三步:逐个创建员工

对每个设计好的角色调用一次 create_employee(...):

create_employee(
  name="market-research-analyst",
  description="负责竞品功能、定价、市场动态的检索与结构化整理",
  systemPrompt="你是资深市场研究分析师……(写清专长与产出格式)",
  skillNames=["news", "web_search"],   // 来自 list_capability_catalog,可留空
  toolNames=["web_search"]              // 来自 list_capability_catalog,可留空
)
  • 工具会返回 agentId 和实际绑定的技能/工具;记下每个员工的 name,下一步要用。
  • 若返回 [error](如重名),换个名字重试,不要中断整个流程。
  • 创建即启用——员工立刻可被工作流引用。

第四步:编排工作流

所有员工创建完成后,调用一次 workflow_draft_generate(description=...)。在 description 里:

  • 点名第三步创建的真实员工(用它们的 name),说明执行顺序、依赖关系、并行还是串行。
  • 写清触发条件、是否需要审批、产出发往哪个渠道。

由于员工已经落库,workflow_draft_generate 会读到它们作为可用数字员工,直接引用真实 agent,而不是填 TODO_*_AGENT 占位。

工作流只会保存为草稿,不会自动发布、不会自动启用触发器。这是安全约定——让用户在工作流编辑器里 review 后再 publish。

第五步:汇报

给用户一份清晰小结:

  • 创建了哪些员工(name + 职责 + 绑定的关键能力)。
  • 生成的工作流草稿名称、id、各步骤如何串联。
  • 草稿编译预校验是否通过、有无缺失字段需要补。
  • 明确告知:工作流是草稿,请到工作流编辑器确认后再发布。

关键规则

  • 员工 name 用英文 kebab-case 且工作区内唯一;工作流 step.name / outputVar 的命名约束由 workflow_draft_generate 负责,本技能不必操心。
  • 技能名 / 工具名必须来自 list_capability_catalog(),臆造的名字会被静默跳过。
  • 先创建员工,再生成工作流——顺序不能反,否则工作流只能拿到占位符。
  • 能复用现有 Agent 就复用,避免制造一堆同质化角色。
  • 不自动发布工作流,不自动启用触发器。

Frequently asked questions

What does the Digital Employee AI skill do?

根据一句业务需求,自动规划并创建一组分工明确的数字员工(Agent),再把他们编排成一条工作流,无需逐个手工创建。

Why use Digital Employee on TypingMind?

Because you install it once and use it with any model. Digital Employee is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Digital Employee in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mateaix/mateclaw/tree/dev/mateclaw-server/src/main/resources/skills/digital_employee. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Digital Employee?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Digital Employee?

As many as you like. As long as a model supports skills, you can use Digital Employee with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Digital Employee AI skill free?

Yes. It is published on GitHub by mateaix under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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