Create Cowork Plugin logo

Create Cowork Plugin

OrganizationPopular
anthropics
create-cowork-plugin

Guide users through creating a new plugin from scratch in a cowork session. Use when users want to create a plugin, build a plugin, make a new plugin, develop a plugin, scaffold a plugin, start a plugin from scratch, or design a plugin. This skill requires Cowork mode with access to the outputs directory for delivering the final .plugin file.

Overview

Publisheranthropics
Repositoryknowledge-work-plugins
Skill namecreate-cowork-plugin
Stars
24.9K
Forks
3K
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Create Cowork Plugin 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.
  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/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins
mkdir -p .claude/skills
cp -r /tmp/knowledge-work-plugins/cowork-plugin-management/skills/create-cowork-plugin .claude/skills/create-cowork-plugin
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Cowork Plugin 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 Create Cowork Plugin 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 Create Cowork Plugin 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.

Create Cowork Plugin

Build a new plugin from scratch through guided conversation. Walk the user through discovery, planning, design, implementation, and packaging — delivering a ready-to-install .plugin file at the end.

Overview

A plugin is a self-contained directory that extends Claude's capabilities with skills, agents, hooks, and MCP server integrations. This skill encodes the full plugin architecture and a five-phase workflow for creating one conversationally.

The process:

  1. Discovery — understand what the user wants to build
  2. Component Planning — determine which component types are needed
  3. Design & Clarifying Questions — specify each component in detail
  4. Implementation — create all plugin files
  5. Review & Package — deliver the .plugin file

Nontechnical output: Keep all user-facing conversation in plain language. Do not expose implementation details like file paths, directory structures, or schema fields unless the user asks. Frame everything in terms of what the plugin will do.

Plugin Architecture

Directory Structure

Every plugin follows this layout:

plugin-name/
├── .claude-plugin/
│   └── plugin.json           # Required: plugin manifest
├── skills/                   # Skills (subdirectories with SKILL.md)
│   └── skill-name/
│       ├── SKILL.md
│       └── references/
├── agents/                   # Subagent definitions (.md files)
├── .mcp.json                 # MCP server definitions
└── README.md                 # Plugin documentation

Legacy commands/ format: Older plugins may include a commands/ directory with single-file .md slash commands. This format still works, but new plugins should use skills/*/SKILL.md instead — the Cowork UI presents both as a single "Skills" concept, and the skills format supports progressive disclosure via references/.

Rules:

  • .claude-plugin/plugin.json is always required
  • Component directories (skills/, agents/) go at the plugin root, not inside .claude-plugin/
  • Only create directories for components the plugin actually uses
  • Use kebab-case for all directory and file names

plugin.json Manifest

Located at .claude-plugin/plugin.json. Minimal required field is name.

json
{
  "name": "plugin-name",
  "version": "0.1.0",
  "description": "Brief explanation of plugin purpose",
  "author": {
    "name": "Author Name"
  }
}

Name rules: kebab-case, lowercase with hyphens, no spaces or special characters. Version: semver format (MAJOR.MINOR.PATCH). Start at 0.1.0.

Optional fields: homepage, repository, license, keywords.

Custom component paths can be specified (supplements, does not replace, auto-discovery):

json
{
  "commands": "./custom-commands",
  "agents": ["./agents", "./specialized-agents"],
  "hooks": "./config/hooks.json",
  "mcpServers": "./.mcp.json"
}

Component Schemas

Detailed schemas for each component type are in references/component-schemas.md. Summary:

ComponentLocationFormat
Skillsskills/*/SKILL.mdMarkdown + YAML frontmatter
MCP Servers.mcp.jsonJSON
Agents (uncommonly used in Cowork)agents/*.mdMarkdown + YAML frontmatter
Hooks (rarely used in Cowork)hooks/hooks.jsonJSON
Commands (legacy)commands/*.mdMarkdown + YAML frontmatter

This schema is shared with Claude Code's plugin system, but you're creating a plugin for Claude Cowork, a desktop app for doing knowledge work. Cowork users will usually find skills the most useful. Scaffold new plugins with skills/*/SKILL.md — do not create commands/ unless the user explicitly needs the legacy single-file format.

Customizable plugins with ~~ placeholders

Do not use or ask about this pattern by default. Only introduce ~~ placeholders if the user explicitly says they want people outside their organization to use the plugin. You can mention this is an option if it seems like the user wants to distribute the plugin externally, but do not proactively ask about this with AskUserQuestion.

When a plugin is intended to be shared with others outside their company, it might have parts that need to be adapted to individual users. You might need to reference external tools by category rather than specific product (e.g., "project tracker" instead of "Jira"). When sharing is needed, use generic language and mark these as requiring customization with two tilde characters such as create an issue in ~~project tracker. If used any tool categories, write a CONNECTORS.md file at the plugin root to explain:

markdown
# Connectors

## How tool references work

Plugin files use `~~category` as a placeholder for whatever tool the user
connects in that category. Plugins are tool-agnostic — they describe
workflows in terms of categories rather than specific products.

## Connectors for this plugin

| Category        | Placeholder         | Options                         |
| --------------- | ------------------- | ------------------------------- |
| Chat            | `~~chat`            | Slack, Microsoft Teams, Discord |
| Project tracker | `~~project tracker` | Linear, Asana, Jira             |

${CLAUDE_PLUGIN_ROOT} Variable

Use ${CLAUDE_PLUGIN_ROOT} for all intra-plugin path references in hooks and MCP configs. Never hardcode absolute paths.

Guided Workflow

When you ask the user something, use AskUserQuestion. Don't assume "industry standard" defaults are correct. Note: AskUserQuestion always includes a Skip button and a free-text input box for custom answers, so do not include None or Other as options.

Phase 1: Discovery

Goal: Understand what the user wants to build and why.

Ask (only what is unclear — skip questions if the user's initial request already answers them):

  • What should this plugin do? What problem does it solve?
  • Who will use it and in what context?
  • Does it integrate with any external tools or services?
  • Is there a similar plugin or workflow to reference?

Summarize understanding and confirm before proceeding.

Output: Clear statement of plugin purpose and scope.

Phase 2: Component Planning

Goal: Determine which component types the plugin needs.

Based on the discovery answers, determine:

  • Skills — Does it need specialized knowledge that Claude should load on-demand, or user-initiated actions? (domain expertise, reference schemas, workflow guides, deploy/configure/analyze/review actions)
  • MCP Servers — Does it need external service integration? (databases, APIs, SaaS tools)
  • Agents (uncommon) — Are there autonomous multi-step tasks? (validation, generation, analysis)
  • Hooks (rare) — Should something happen automatically on certain events? (enforce policies, load context, validate operations)

Present a component plan table, including component types you decided not to create:

| Component | Count | Purpose |
|-----------|-------|---------|
| Skills    | 3     | Domain knowledge for X, /do-thing, /check-thing |
| Agents    | 0     | Not needed |
| Hooks     | 1     | Validate writes |
| MCP       | 1     | Connect to service Y |

Get user confirmation or adjustments before proceeding.

Output: Confirmed list of components to create.

Phase 3: Design & Clarifying Questions

Goal: Specify each component in detail. Resolve all ambiguities before implementation.

For each component type in the plan, ask targeted design questions. Present questions grouped by component type. Wait for answers before proceeding.

Skills:

  • What user queries should trigger this skill?
  • What knowledge domains does it cover?
  • Should it include reference files for detailed content?
  • If the skill represents a user-initiated action: what arguments does it accept, and what tools does it need? (Read, Write, Bash, Grep, etc.)

Agents:

  • Should each agent trigger proactively or only when requested?
  • What tools does it need?
  • What should the output format be?

Hooks:

  • Which events? (PreToolUse, PostToolUse, Stop, SessionStart, etc.)
  • What behavior — validate, block, modify, add context?
  • Prompt-based (LLM-driven) or command-based (deterministic script)?

MCP Servers:

  • What server type? (stdio for local, SSE for hosted with OAuth, HTTP for REST APIs)
  • What authentication method?
  • What tools should be exposed?

If the user says "whatever you think is best," provide specific recommendations and get explicit confirmation.

Output: Detailed specification for every component.

Phase 4: Implementation

Goal: Create all plugin files following best practices.

Order of operations:

  1. Create the plugin directory structure
  2. Create plugin.json manifest
  3. Create each component (see references/component-schemas.md for exact formats)
  4. Create README.md documenting the plugin

Implementation guidelines:

  • Skills use progressive disclosure: lean SKILL.md body (under 3,000 words), detailed content in references/. Frontmatter description must be third-person with specific trigger phrases. Skill bodies are instructions FOR Claude, not messages to the user — write them as directives about what to do.
  • Agents need a description with <example> blocks showing triggering conditions, plus a system prompt in the markdown body.
  • Hooks config goes in hooks/hooks.json. Use ${CLAUDE_PLUGIN_ROOT} for script paths. Prefer prompt-based hooks for complex logic.
  • MCP configs go in .mcp.json at plugin root. Use ${CLAUDE_PLUGIN_ROOT} for local server paths. Document required env vars in README.

Phase 5: Review & Package

Goal: Deliver the finished plugin.

  1. Summarize what was created — list each component and its purpose

  2. Ask if the user wants any adjustments

  3. Run claude plugin validate <path-to-plugin-json> to check the plugin structure. If this command is unavailable (e.g., when running inside Cowork), verify the structure manually:

    • .claude-plugin/plugin.json exists and contains valid JSON with at least a name field
    • The name field is kebab-case (lowercase letters, numbers, and hyphens only)
    • Any component directories referenced by the plugin (commands/, skills/, agents/, hooks/) actually exist and contain files in the expected formats — .md for commands/skills/agents, .json for hooks
    • Each skill subdirectory contains a SKILL.md
    • Report what passed and what didn't, the same way the CLI validator would

    Fix any errors before proceeding.

  4. Package as a .plugin file:

bash
cd /path/to/plugin-dir && zip -r /tmp/plugin-name.plugin . -x "*.DS_Store" && cp /tmp/plugin-name.plugin /path/to/outputs/plugin-name.plugin

Important: Always create the zip in /tmp/ first, then copy to the outputs folder. Writing directly to the outputs folder may fail due to permissions.

Naming: Use the plugin name from plugin.json for the .plugin file (e.g., if name is code-reviewer, output code-reviewer.plugin).

The .plugin file will appear in the chat as a rich preview where the user can browse the files and accept the plugin by pressing a button.

Best Practices

  • Start small: Begin with the minimum viable set of components. A plugin with one well-crafted skill is more useful than one with five half-baked components.
  • Progressive disclosure for skills: Core knowledge in SKILL.md, detailed reference material in references/, working examples in examples/.
  • Clear trigger phrases: Skill descriptions should include specific phrases users would say. Agent descriptions should include <example> blocks.
  • Skills are for Claude: Write skill body content as instructions for Claude to follow, not documentation for the user to read.
  • Imperative writing style: Use verb-first instructions in skills ("Parse the config file," not "You should parse the config file").
  • Portability: Always use ${CLAUDE_PLUGIN_ROOT} for intra-plugin paths, never hardcoded paths.
  • Security: Use environment variables for credentials, HTTPS for remote servers, least-privilege tool access.

Additional Resources

  • references/component-schemas.md — Detailed format specifications for every component type (skills, agents, hooks, MCP, legacy commands, CONNECTORS.md)
  • references/example-plugins.md — Three complete example plugin structures at different complexity levels

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Create Cowork Plugin AI skill do?

Guide users through creating a new plugin from scratch in a cowork session. Use when users want to create a plugin, build a plugin, make a new plugin, develop a plugin, scaffold a plugin, start a plugin from scratch, or design a plugin. This skill requires Cowork mode with access to the outputs directory for delivering the final .plugin file.

Why use Create Cowork Plugin on TypingMind?

Because you install it once and use it with any model. Create Cowork Plugin 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 Create Cowork Plugin in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/knowledge-work-plugins/tree/main/cowork-plugin-management/skills/create-cowork-plugin. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Create Cowork Plugin?

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 Create Cowork Plugin?

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

Is the Create Cowork Plugin AI skill free?

Yes. It is published on GitHub by anthropics 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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