ClickUp Skills

A professional AI Skill for designing, auditing, troubleshooting, and implementing ClickUp workspaces.

clickup-skills helps ChatGPT, Claude, and other AI agents reason about ClickUp like a workspace architect: hierarchy, Spaces, Folders, Lists, statuses, Custom Fields, task types, views, Docs, dashboards, Forms, automations, dependencies, permissions, API, webhooks, MCP, troubleshooting, and governance.

What this is

This repository contains:

• A packaged Skill: dist/skill.zip
• The editable Skill source: clickup-skills/
• Reference guides for professional ClickUp architecture
• A JSON blueprint validator
• Example prompts and a sample workspace blueprint
• Release and contribution docs

What this is not

This repo does not contain ClickUp credentials, private workspace data, paid templates, or a direct ClickUp integration. It is a reusable instruction and reference bundle for AI assistants.

Use cases

• Design a ClickUp workspace from scratch
• Restructure a messy workspace
• Define Spaces, Folders, Lists, statuses, fields, task types, and views
• Build dashboards and reporting surfaces
• Design Docs/wiki operating models
• Create automation and webhook specs
• Plan API/MCP integrations
• Audit ClickUp hierarchy, permissions, automations, and fields
• Troubleshoot status drift, field sprawl, broken views, automation failures, and reporting gaps
• Generate implementation prompts for AI agents

Install

Upload dist/skill.zip to the Skill area of your AI assistant if supported.

For development, edit files under clickup-skills/ and repackage the skill as skill.zip.

Repository structure

clickup-skills-repo/
├── clickup-skills/
│   ├── SKILL.md
│   ├── agents/openai.yaml
│   ├── references/
│   └── scripts/
├── dist/skill.zip
├── examples/
├── docs/
├── README.md
├── LICENSE
├── CONTRIBUTING.md
├── SECURITY.md
└── CHANGELOG.md

Example prompt

Design a professional ClickUp workspace for a 40-person agency. Include Spaces, Folders, Lists, statuses, Custom Fields, views, Docs, dashboards, automations, permissions, rollout plan, and a JSON blueprint for implementation.

Design philosophy

• Model workflow before configuration.
• Keep hierarchy small and durable.
• Treat statuses as lifecycle, not labels.
• Treat Custom Fields as a governed data model.
• Use views for operational surfaces.
• Use Docs/wikis for knowledge and process.
• Use dashboards for decision-making.
• Use automations only after the data model is stable.
• Make integrations idempotent and auditable.
• Keep secrets out of prompts and repositories.

Validation

The Skill includes a lightweight JSON validator:

python clickup-skills/scripts/validate_workspace_blueprint_json.py examples/sample-blueprint.json

License

MIT