Cowork Plugin Customizer logo

Cowork Plugin Customizer

OrganizationPopular
anthropics
cowork-plugin-customizer

Customize a Claude Code plugin for a specific organization's tools and workflows. Use when: customize plugin, set up plugin, configure plugin, tailor plugin, adjust plugin settings, customize plugin connectors, customize plugin skill, tweak plugin, modify plugin configuration.

Overview

Publisheranthropics
Repositoryknowledge-work-plugins
Skill namecowork-plugin-customizer
Stars
24.9K
Forks
3K
Bundled files
3
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.

  • 3 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 Cowork Plugin Customizer 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/cowork-plugin-customizer .claude/skills/cowork-plugin-customizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cowork Plugin Customizer 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 Cowork Plugin Customizer 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 Cowork Plugin Customizer 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.

Cowork Plugin Customization

Customize a plugin for a specific organization — either by setting up a generic plugin template for the first time, or by tweaking and refining an already-configured plugin.

Finding the plugin: To find the plugin's source files, run find mnt/.local-plugins mnt/.plugins -type d -name "*<plugin-name>*" to locate the plugin directory, then read its files to understand its structure before making changes. If you cannot find the plugin directory, the user is likely running this conversation in a remote container. Abort and let them know: "Customizing plugins is currently only available in the desktop app's Cowork mode."

Determining the Customization Mode

After locating the plugin, check for ~~-prefixed placeholders: grep -rn '~~\w' /path/to/plugin --include='*.md' --include='*.json'

Default rule: If ~~ placeholders exist, default to Generic plugin setup unless the user explicitly asks to customize a specific part of the plugin.

1. Generic plugin setup — The plugin contains ~~-prefixed placeholders. These are customization points in a template that need to be replaced with real values (e.g., ~~JiraAsana, ~~your-team-channel#engineering).

2. Scoped customization — No ~~ placeholders exist, and the user asked to customize a specific part of the plugin (e.g., "customize the connectors", "update the standup skill", "change the ticket tool"). Read the plugin files to find the relevant section(s) and focus only on those. Do not scan the entire plugin or present unrelated customization items.

Legacy commands/ directories: Some plugins include a commands/ directory. The Cowork UI now presents these alongside skills as a single "Skills" concept, so treat commands/*.md files the same way you would skills/*/SKILL.md files when customizing.

3. General customization — No ~~ placeholders exist, and the user wants to modify the plugin broadly. Read the plugin's files to understand its current configuration, then ask the user what they'd like to change.

Important: Never change the name of the plugin or skill being customized. Do not rename directories, files, or the plugin/skill name fields.

Nontechnical output: All user-facing output (todo list items, questions, summaries) must be written in plain, nontechnical language. Never mention ~~ prefixes, placeholders, or customization points to the user. Frame everything in terms of the plugin's capabilities and the organization's tools.

Customization Workflow

Phase 0: Gather User Intent (scoped and general customization only)

For scoped customization and general customization (not generic plugin setup), check whether the user provided free-form context alongside their request (e.g., "customize the standup skill — we do async standups in #eng-updates every morning").

  • If the user provided context: Record it and use it to pre-fill answers in Phase 3 — skip asking questions that the user already answered here.

  • If the user did not provide context: Ask a single open-ended question using AskUserQuestion before proceeding. Tailor the question to what they asked to customize — e.g., "What changes do you have in mind for the brief skill?" or "What would you like to change about how this plugin works?" Keep it short and specific to their request.

    Use their response (if any) as additional context throughout the remaining phases.

Phase 1: Gather Context from Knowledge MCPs

Use company-internal knowledge MCPs to collect information relevant to the customization scope. See references/search-strategies.md for detailed query patterns by category.

What to gather (scope to what's relevant):

  • Tool names and services the organization uses
  • Organizational processes and workflows
  • Team conventions (naming, statuses, estimation scales)
  • Configuration values (workspace IDs, project names, team identifiers)

Sources to search:

  1. Chat/Slack MCPs — tool mentions, integrations, workflow discussions
  2. Document MCPs — onboarding docs, tool guides, setup instructions
  3. Email MCPs — license notifications, admin emails, setup invitations

Record all findings for use in Phase 3.

Phase 2: Create Todo List

Build a todo list of changes to make, scoped appropriately:

  • For scoped customization: Only include items related to the specific section the user asked about.
  • For generic plugin setup: Run grep -rn '~~\w' /path/to/plugin --include='*.md' --include='*.json' to find all placeholder customization points. Group them by theme.
  • For general customization: Read the plugin files, understand the current config, and based on the user's request, identify what needs to change.

Use user-friendly descriptions that focus on the plugin's purpose:

  • Good: "Learn how standup prep works at Company"
  • Bad: "Replace placeholders in skills/standup-prep/SKILL.md"

Phase 3: Complete Todo Items

Work through each item using context from Phase 0 and Phase 1.

If the user's free-form input (Phase 0) or knowledge MCPs (Phase 1) provided a clear answer: Apply directly without confirmation.

Otherwise: Use AskUserQuestion. Don't assume "industry standard" defaults are correct — if neither the user's input nor knowledge MCPs provided a specific answer, ask. 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.

Types of changes:

  1. Placeholder replacements (generic setup): ~~JiraAsana, ~~your-org-channel#engineering
  2. Content updates: Modifying instructions, skills, workflows, or references to match the organization
  3. URL pattern updates: tickets.example.com/your-team/123app.asana.com/0/PROJECT_ID/TASK_ID
  4. Configuration values: Workspace IDs, project names, team identifiers

If user doesn't know or skips, leave the value unchanged (or the ~~-prefixed placeholder, for generic setup).

Phase 4: Search for Useful MCPs

After customization items have been resolved, connect MCPs for any tools that were identified or changed. See references/mcp-servers.md for the full workflow, category-to-keywords mapping, and config file format.

For each tool identified during customization:

  1. Search the registry: search_mcp_registry(keywords=[...]) using category keywords from references/mcp-servers.md, or search for the specific tool name if already known
  2. If unconnected: suggest_connectors(directoryUuids=["chosen-uuid"]) — user completes auth
  3. Update the plugin's MCP config file (check plugin.json for custom location, otherwise .mcp.json at root)

Collect all MCP results and present them together in the summary output (see below) — don't present MCPs one at a time during this phase.

Packaging the Plugin

After all customizations are applied, package the plugin as a .plugin file for the user:

  1. Zip the plugin directory (excluding setup/ since it's no longer needed):
    bash
    cd /path/to/plugin && zip -r /tmp/plugin-name.plugin . -x "setup/*" && cp /tmp/plugin-name.plugin /path/to/outputs/plugin-name.plugin
  2. Present the file to the user with the .plugin extension so they can install it directly. (Presenting the .plugin file will show to the user as a rich preview where they can look through the plugin files, and they can accept the customization by pressing a button.)

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 and leave behind temporary files.

Naming: Use the original plugin directory name for the .plugin file (e.g., if the plugin directory is coder, the output file should be coder.plugin). Do not rename the plugin or its files during customization — only replace placeholder values and update content.

Summary Output

After customization, present the user with a summary of what was learned grouped by source. Always include the MCPs sections showing which MCPs were connected during setup and which ones the user should still connect:

markdown
## From searching Slack

- You use Asana for project management
- Sprint cycles are 2 weeks

## From searching documents

- Story points use T-shirt sizes

## From your answers

- Ticket statuses are: Backlog, In Progress, In Review, Done

Then present the MCPs that were connected during setup and any that the user should still connect, with instructions on how to connect them.

If no knowledge MCPs were available in Phase 1, and the user had to answer at least one question manually, include a note at the end:

By the way, connecting sources like Slack or Microsoft Teams would let me find answers automatically next time you customize a plugin.

Additional Resources

  • references/mcp-servers.md — MCP discovery workflow, category-to-keywords mapping, config file locations
  • references/search-strategies.md — Knowledge MCP query patterns for finding tool names and org values
  • examples/customized-mcp.json — Example fully configured .mcp.json

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 Cowork Plugin Customizer AI skill do?

Customize a Claude Code plugin for a specific organization's tools and workflows. Use when: customize plugin, set up plugin, configure plugin, tailor plugin, adjust plugin settings, customize plugin connectors, customize plugin skill, tweak plugin, modify plugin configuration.

Why use Cowork Plugin Customizer on TypingMind?

Because you install it once and use it with any model. Cowork Plugin Customizer 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 Cowork Plugin Customizer 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/cowork-plugin-customizer. 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 Cowork Plugin Customizer?

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 Cowork Plugin Customizer?

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

Is the Cowork Plugin Customizer 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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