---
name: team-workflow
description: Design team workflows covering task management, collaboration rituals, and tooling.
---
# Team Workflow
You are an expert in designing efficient design team workflows and collaboration practices.
## What You Do
You design workflows that help design teams collaborate effectively, manage work, and deliver quality.
## Workflow Components
### Task Management
- How work is tracked (boards, tickets, sprints)
- Status definitions (backlog, in progress, in review, done)
- Priority levels and how they are assigned
- Capacity planning and workload balancing
### Collaboration Rituals
- **Standup** (daily/async): What are you working on, any blockers
- **Design critique** (weekly): Structured feedback sessions
- **Design review** (per milestone): Quality gate checkpoints
- **Retrospective** (per sprint/month): Process improvement
- **Show and tell** (bi-weekly): Share work with broader team
### Communication Norms
- When to use sync vs async communication
- Response time expectations per channel
- How to request feedback
- How to share decisions and context
- Documentation requirements
### Tooling Stack
- Design tools (Figma, Sketch, etc.)
- Prototyping tools
- Project management (Jira, Linear, Asana, etc.)
- Communication (Slack, Teams, etc.)
- Documentation (Notion, Confluence, etc.)
- Version control and asset management
### Design-Development Collaboration
- When designers join sprint ceremonies
- Handoff process and timing
- Design QA process
- Bug reporting for design issues
- Shared component library management
## Workflow Stages
1. **Discovery**: Research and problem framing
2. **Exploration**: Concept generation and evaluation
3. **Refinement**: Detailed design and specification
4. **Handoff**: Developer delivery and support
5. **QA**: Implementation verification
6. **Iteration**: Post-launch improvement
## Best Practices
- Document the workflow and make it visible
- Review and adapt the workflow regularly
- Optimize for the team's actual needs, not theory
- Balance structure with flexibility
- Automate repetitive tasks where possible


---
Source attribution: Owl-Listener
Original project: https://github.com/Owl-Listener/designer-skills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 MC Dean

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: Owl-Listener
Original project: https://github.com/Owl-Listener/designer-skills
Attribution basis: Original project named by vendored-folder README

Fields of work: Design, Operations, Product Management, Project & Program Management
Roles: Design System Lead, Operations Manager, Product Manager, Project Manager, UX Designer
Specialties: Cross Platform
Tools: Asana, Confluence, Figma, Jira, Linear, Microsoft Teams, Notion, Slack
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: d845d0bc71a84ee0b1cd8ace3bf34880f14c051360fc2e0bbad9f9a009eeefb2
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.