跳到正文
cccskills
免费在 GitHub 公开中文介绍

company-creator

根据构想或现有代码仓库,梳理AI智能体的职责、汇报关系与工作交接,生成符合规范的公司或团队文件包,供组织协作和导入Paperclip使用。

原文查看原文说明

Create agent company packages that conform to agentcompanies/v1. Use when asked to create a company, scaffold an agent team, hire agents, or turn a repo/skills collection into a company package.

实测评分 未实测

查看安装方法

适用场景

  • 从构想建立智能体公司
  • 将代码仓库整理为团队
  • 设计职责分工与工作交接
  • 制作Paperclip导入包

中文介绍

功能

company-creator将AI智能体公司或团队的设计整理为符合agentcompanies/v1的文件包。既可以先了解目标再从头创建,也可以分析现有代码仓库中的技能和结构。它梳理职责、汇报关系与成果交接,生成COMPANY.md及各智能体的AGENTS.md,并可按需加入团队、项目和初始任务。

适用场景

适合让多个智能体分担工作,或将已有技能集整理成可协作的组织。可以根据工作内容考虑不同流程,例如按顺序逐步交接,或由管理者向各专业负责人分派任务。

使用示例

  • “建立一家分别负责研究、写作和编辑的智能体公司。”
  • “根据这个代码仓库,建立职责和工作流程明确的团队。”

注意事项

生成前会确认职责和运作方式,并与你商定保存位置。外部技能默认采用引用方式,未经明确要求不会复制其内容。生成的文件包包含README.md、LICENSE及Paperclip导入说明。以现有代码仓库为基础时,采用与该仓库一致的许可证类型。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

文档检查

AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。

安全性发送数据、删除文件、执行命令等是否有说明
A良好要求写入前确认需求并商定保存位置,禁止导出秘密信息。
安装难易度所需工具和 API 密钥是否少且有说明
A良好提供规范和示例的位置,未列出必须使用的API密钥或付费服务。

不计入评分。 由 AI(OpenAI Codex)于 2026年10月20日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(4)

  • SKILL.md11.6 KB
  • references/companies-spec.md3.5 KB
  • references/example-company.md4.1 KB
  • references/from-repo-guide.md2.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Company Creator

Create agent company packages that conform to the Agent Companies specification.

Spec references:

Two Modes

Mode 1: Company From Scratch

The user describes what they want. Interview them to flesh out the vision, then generate the package.

Mode 2: Company From a Repo

The user provides a git repo URL, local path, or tweet. Analyze the repo, then create a company that wraps it.

See references/from-repo-guide.md for detailed repo analysis steps.

Process

Step 1: Gather Context

Determine which mode applies:

  • From scratch: What kind of company or team? What domain? What should the agents do?
  • From repo: Clone/read the repo. Scan for existing skills, agent configs, README, source structure.

Step 2: Interview (Use AskUserQuestion)

Do not skip this step. Use AskUserQuestion to align with the user before writing any files.

For from-scratch companies, ask about:

  • Company purpose and domain (1-2 sentences is fine)
  • What agents they need - propose a hiring plan based on what they described
  • Whether this is a full company (needs a CEO) or a team/department (no CEO required)
  • Any specific skills the agents should have
  • How work flows through the organization (see "Workflow" below)
  • Whether they want projects and starter tasks

For from-repo companies, present your analysis and ask:

  • Confirm the agents you plan to create and their roles
  • Whether to reference or vendor any discovered skills (default: reference)
  • Any additional agents or skills beyond what the repo provides
  • Company name and any customization
  • Confirm the workflow you inferred from the repo (see "Workflow" below)

Workflow — how does work move through this company?

A company is not just a list of agents with skills. It's an organization that takes ideas and turns them into work products. You need to understand the workflow so each agent knows:

  • Who gives them work and in what form (a task, a branch, a question, a review request)
  • What they do with it
  • Who they hand off to when they're done, and what that handoff looks like
  • What "done" means for their role

Not every company is a pipeline. Infer the right workflow pattern from context:

  • Pipeline — sequential stages, each agent hands off to the next. Use when the repo/domain has a clear linear process (e.g. plan → build → review → ship → QA, or content ideation → draft → edit → publish).
  • Hub-and-spoke — a manager delegates to specialists who report back independently. Use when agents do different kinds of work that don't feed into each other (e.g. a CEO who dispatches to a researcher, a marketer, and an analyst).
  • Collaborative — agents work together on the same things as peers. Use for small teams where everyone contributes to the same output (e.g. a design studio, a brainstorming team).
  • On-demand — agents are summoned as needed with no fixed flow. Use when agents are more like a toolbox of specialists the user calls directly.

For from-scratch companies, propose a workflow pattern based on what they described and ask if it fits.

For from-repo companies, infer the pattern from the repo's structure. If skills have a clear sequential dependency (like plan-ceo-review → plan-eng-review → review → ship → qa), that's a pipeline. If skills are independent capabilities, it's more likely hub-and-spoke or on-demand. State your inference in the interview so the user can confirm or adjust.

Key interviewing principles:

  • Propose a concrete hiring plan. Don't ask open-ended "what agents do you want?" - suggest specific agents based on context and let the user adjust.
  • Keep it lean. Most users are new to agent companies. A few agents (3-5) is typical for a startup. Don't suggest 10+ agents unless the scope demands it.
  • From-scratch companies should start with a CEO who manages everyone. Teams/departments don't need one.
  • Ask 2-3 focused questions per round, not 10.

Step 3: Read the Spec

Before generating any files, read the normative spec:

docs/companies/companies-spec.md

Also read the quick reference: references/companies-spec.md

And the example: references/example-company.md

Step 4: Generate the Package

Create the directory structure and all files. Follow the spec's conventions exactly.

Directory structure:

<company-slug>/
├── COMPANY.md
├── agents/
│   └── <slug>/AGENTS.md
├── teams/
│   └── <slug>/TEAM.md        (if teams are needed)
├── projects/
│   └── <slug>/PROJECT.md     (if projects are needed)
├── tasks/
│   └── <slug>/TASK.md        (if tasks are needed)
├── skills/
│   └── <slug>/SKILL.md       (if custom skills are needed)
└── .paperclip.yaml            (Paperclip vendor extension)

Rules:

  • Slugs must be URL-safe, lowercase, hyphenated
  • COMPANY.md gets schema: agentcompanies/v1 - other files inherit it
  • Agent instructions go in the AGENTS.md body, not in .paperclip.yaml
  • Skills referenced by shortname in AGENTS.md resolve to skills/<shortname>/SKILL.md
  • For external skills, use sources with usage: referenced (see spec section 12)
  • Do not export secrets, machine-local paths, or database IDs
  • Omit empty/default fields
  • For companies generated from a repo, add a references footer at the bottom of COMPANY.md body: Generated from [repo-name](repo-url) with the company-creator skill from [Paperclip](https://github.com/paperclipai/paperclip)

Reporting structure:

  • Every agent except the CEO should have reportsTo set to their manager's slug
  • The CEO has reportsTo: null
  • For teams without a CEO, the top-level agent has reportsTo: null

Writing workflow-aware agent instructions:

Each AGENTS.md body should include not just what the agent does, but how they fit into the organization's workflow. Include:

  1. Where work comes from — "You receive feature ideas from the user" or "You pick up tasks assigned to you by the CTO"
  2. What you produce — "You produce a technical plan with architecture diagrams" or "You produce a reviewed, approved branch ready for shipping"
  3. Who you hand off to — "When your plan is locked, hand off to the Staff Engineer for implementation" or "When review passes, hand off to the Release Engineer to ship"
  4. What triggers you — "You are activated when a new feature idea needs product-level thinking" or "You are activated when a branch is ready for pre-landing review"

This turns a collection of agents into an organization that actually works together. Without workflow context, agents operate in isolation — they do their job but don't know what happens before or after them.

Add a concise execution contract to every generated working agent:

  • Start actionable work in the same heartbeat and do not stop at a plan unless planning was requested.
  • Leave durable progress in comments, documents, or work products with the next action.
  • Use child issues for long or parallel delegated work instead of polling agents, sessions, or processes.
  • Mark blocked work with the unblock owner and action.
  • Respect budget, pause/cancel, approval gates, and company boundaries.

Step 5: Confirm Output Location

Ask the user where to write the package. Common options:

  • A subdirectory in the current repo
  • A new directory the user specifies
  • The current directory (if it's empty or they confirm)

Step 6: Write README.md and LICENSE

README.md — every company package gets a README. It should be a nice, readable introduction that someone browsing GitHub would appreciate. Include:

  • Company name and what it does
  • The workflow / how the company operates
  • Org chart as a markdown list or table showing agents, titles, reporting structure, and skills
  • Brief description of each agent's role
  • Citations and references: link to the source repo (if from-repo), link to the Agent Companies spec (https://agentcompanies.io/specification), and link to Paperclip (https://github.com/paperclipai/paperclip)
  • A "Getting Started" section explaining how to import: paperclipai company import --from <path>

LICENSE — include a LICENSE file. The copyright holder is the user creating the company, not the upstream repo author (they made the skills, the user is making the company). Use the same license type as the source repo (if from-repo) or ask the user (if from-scratch). Default to MIT if unclear.

Step 7: Write Files and Summarize

Write all files, then give a brief summary:

  • Company name and what it does
  • Agent roster with roles and reporting structure
  • Skills (custom + referenced)
  • Projects and tasks if any
  • The output path

.paperclip.yaml Guidelines

The .paperclip.yaml file is the Paperclip vendor extension. It configures adapters and env inputs per agent.

Adapter Rules

Do not specify an adapter unless the repo or user context warrants it. If you don't know what adapter the user wants, omit the adapter block entirely — Paperclip will use its default. Specifying an unknown adapter type causes an import error.

Paperclip's supported adapter types (these are the ONLY valid values):

  • claude_local — Claude Code CLI
  • codex_local — Codex CLI
  • opencode_local — OpenCode CLI
  • pi_local — Pi CLI
  • cursor — Cursor
  • gemini_local — Gemini CLI
  • openclaw_gateway — OpenClaw gateway

Only set an adapter when:

  • The repo or its skills clearly target a specific runtime (e.g. gstack is built for Claude Code, so claude_local is appropriate)
  • The user explicitly requests a specific adapter
  • The agent's role requires a specific runtime capability

Env Inputs Rules

Do not add boilerplate env variables. Only add env inputs that the agent actually needs based on its skills or role:

  • GH_TOKEN for agents that push code, create PRs, or interact with GitHub
  • API keys only when a skill explicitly requires them
  • Never set ANTHROPIC_API_KEY as a default empty env variable — the runtime handles this

Example with adapter (only when warranted):

schema: paperclip/v1
agents:
  release-engineer:
    adapter:
      type: claude_local
      config:
        model: claude-sonnet-4-6
    inputs:
      env:
        GH_TOKEN:
          kind: secret
          requirement: optional

Example — only agents with actual overrides appear:

schema: paperclip/v1
agents:
  release-engineer:
    inputs:
      env:
        GH_TOKEN:
          kind: secret
          requirement: optional

In this example, only release-engineer appears because it needs GH_TOKEN. The other agents (ceo, cto, etc.) have no overrides, so they are omitted entirely from .paperclip.yaml.

External Skill References

When referencing skills from a GitHub repo, always use the references pattern:

metadata:
  sources:
    - kind: github-file
      repo: owner/repo
      path: path/to/SKILL.md
      commit: <full SHA from git ls-remote or the repo>
      attribution: Owner or Org Name
      license: <from the repo's LICENSE>
      usage: referenced

Get the commit SHA with:

git ls-remote https://github.com/owner/repo HEAD

Do NOT copy external skill content into the package unless the user explicitly asks.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

add-product-e2e-eval

免费有简介

为Paperclip新增或扩展贯穿真实浏览器、服务器、数据库和runner的端到端评估,整理测试数据、判定条件与证据,并在现有报告中查看操作结果。

  • 新增完整用户操作流程的评估
  • 更新测试数据与判定条件
  • 在评估报告中加入截图和产物
paperclipai/paperclip10.7万2026年10月23日 更新

add-runner-eval

免费有简介

为Paperclip Runner添加或扩展行为评估,定义判定条件、执行目标和报告验证数据,利用正常及异常证据验证结果,并保留执行记录与费用。

  • 添加Runner行为评估
  • 用有效与无效证据检验判定
  • 更新执行列表和持续评估
paperclipai/paperclip10.7万2026年10月23日 更新

agent-browser

免费有简介

通过实际浏览器操作网页和应用,检查界面显示与运行情况。结合截图、简单表单填写、错误及网络请求记录,帮助复现问题并确认操作结果。

  • 检查界面修改后的显示效果
  • 读取由JavaScript渲染的内容
  • 复现界面问题并记录错误
paperclipai/paperclip10.7万2026年10月23日 更新

agentmail

免费有简介

通过 Paperclip 分配的 AgentMail 收件箱,查看任务关联邮件、发送新邮件或回复,并核查投递结果;重试时保留相同标识,避免重复发送。

  • 查看所负责任务关联的邮件
  • 从分配的收件箱发送新邮件
  • 回复会话中的指定邮件
paperclipai/paperclip10.7万2026年10月23日 更新

check-pr

免费有简介

检查GitHub、GitLab和Perforce变更中的评审意见、自动检查失败及说明缺漏,整理待办事项,并按请求协助修改和解决已处理的讨论。

  • 提交变更前进行检查
  • 确认失败的检查和待修改项
  • 整理人工及机器人评审意见
paperclipai/paperclip10.7万2026年10月23日 更新

complain

免费有简介

将智能体对工具、权限或工作环境的不满,以简短自由文本记录到Paperclip。无需分析原因或提出解决方案,保留当时的直接反应,随后继续原本的任务。

  • 记录对智能体工具的不满
  • 留下对权限或运行环境的不满
  • 记录尚无解决方案的直接反应
paperclipai/paperclip10.7万2026年10月23日 更新

查看 paperclipai 的全部技能

报告此技能的问题