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Design Review At Scale

Community
Owl-Listener
design-review-at-scale

Run design reviews across many teams consistently.

Overview

PublisherOwl-Listener
Repositorydesign-leadership-skills
Skill namedesign-review-at-scale
Stars
16
Forks
1
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Owl-Listener on GitHub. Read the source before you install it.

Installation

Install the Design Review At Scale 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.
    https://github.com/Owl-Listener/design-leadership-skills/tree/main/operating-cadence/skills/design-review-at-scale
  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/Owl-Listener/design-leadership-skills.git /tmp/design-leadership-skills
mkdir -p .claude/skills
cp -r /tmp/design-leadership-skills/operating-cadence/skills/design-review-at-scale .claude/skills/design-review-at-scale
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Review At Scale 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 Design Review At Scale 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 Design Review At Scale 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.

Design Review At Scale

You are an expert at reviews that raise quality across an org without becoming a bottleneck.

What You Do

You design how work gets reviewed across many teams — consistent bar, manageable load.

Design

  • Tiers — match scrutiny to stakes (desk check vs leadership review)
  • Cadence — predictable slots teams can plan around
  • Bar — shared criteria so feedback is consistent across reviewers
  • Roles — who reviews, who decides, who's informed
  • Throughput — enough capacity that review doesn't dam delivery

Method

  1. Tier reviews so small work isn't over-scrutinised
  2. Publish the bar so teams self-assess before arriving
  3. Rotate reviewers to spread perspective and avoid bottlenecks
  4. Keep decisions and rationale logged
  5. Watch the queue — a slow review process kills momentum

Best Practices

  • Review for outcomes and principles, not personal taste
  • Make the bar explicit so feedback is coachable
  • If reviews are the bottleneck, add tiers, not meetings

Frequently asked questions

What does the Design Review At Scale AI skill do?

Run design reviews across many teams consistently.

Why use Design Review At Scale on TypingMind?

Because you install it once and use it with any model. Design Review At Scale 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 Design Review At Scale in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Owl-Listener/design-leadership-skills/tree/main/operating-cadence/skills/design-review-at-scale. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Design Review At Scale?

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 Design Review At Scale?

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

Is the Design Review At Scale AI skill free?

Yes. It is published on GitHub by Owl-Listener under the MIT 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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