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80 20 Redesign Targeting

Community
Deibler
80-20-redesign-targeting

Use this skill when scoping a redesign or refactor — choosing which existing surfaces deserve full attention and which can be left mostly alone. Trigger when the user asks "where should we focus our redesign?", "do we need to redo all of this?", or when planning incremental design improvements with limited capacity. Sub-aspect of `80-20-rule`; read that first.

Overview

PublisherDeibler
Repositoryuniversal-design-principles
Skill name80-20-redesign-targeting
Stars
9
Forks
3
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the 80 20 Redesign Targeting 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/Deibler/universal-design-principles/tree/main/plugins/cognition-and-learnability-principles/skills/80-20-redesign-targeting
  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/Deibler/universal-design-principles.git /tmp/universal-design-principles
mkdir -p .claude/skills
cp -r /tmp/universal-design-principles/plugins/cognition-and-learnability-principles/skills/80-20-redesign-targeting .claude/skills/80-20-redesign-targeting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 80 20 Redesign Targeting 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 80 20 Redesign Targeting 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 80 20 Redesign Targeting 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.

80/20 in redesign targeting

A common failure mode of redesigns: trying to redo everything. The result is years-long projects that ship late, lose institutional context, and arrive in a state no better than incremental updates would have produced. The 80/20 rule applied to redesign: identify the surfaces that produce most of user pain or business value; concentrate redesign effort there; leave the rest alone or batch into a maintenance pass.

How to identify high-value surfaces

1. Pain analysis

Where do users struggle? Sources:

  • Support ticket categorization (which surfaces generate the most "how do I..." or "I can't..." tickets)
  • Session recordings and rage-click analytics
  • Drop-off / abandonment metrics (where users leave the funnel)
  • Time-on-task for primary flows (where users get stuck)

The 80/20 cut: ~20% of surfaces typically account for ~80% of pain.

2. Traffic analysis

Where do users actually spend time? A beautiful but rarely-visited page deserves less polish than an ugly but constantly-used one.

Cross-reference traffic with the surface's role: a low-traffic but business-critical surface (Settings → Billing) earns redesign even if traffic alone wouldn't justify it.

3. Business-impact analysis

Conversion surfaces (sign-up, checkout, upgrade) often punch above their traffic weight. A 5% lift on a low-traffic checkout might exceed a 30% lift on a high-traffic blog.

4. Strategic-fit analysis

Some surfaces represent the brand or category position. The marketing home page, the first-run experience, the demo screen for sales. These deserve disproportionate attention because they shape perception of the whole product.

The redesign 80/20 plan

A typical redesign plan that respects 80/20:

Phase 1 (the critical 20% — heavy investment)
  - Top 3–5 user-facing surfaces by pain × traffic × business impact
  - Full redesign: research, design, build, polish, A/B test
  - Often 60–70% of the project budget

Phase 2 (the meaningful tail — moderate investment)
  - Next 10–15 surfaces
  - Update to new design system; fix obvious issues; don't redesign whole flows
  - 20–30% of budget

Phase 3 (the rest — minimal investment)
  - All remaining surfaces
  - Apply new design system tokens; update components; cosmetic only
  - 10% of budget

Phase 4 (revisit)
  - Six months in: re-measure. The "rest" that wasn't redesigned might still be performing fine.
  - For surfaces that turn out to be problematic, fold into the next cycle.

Phases 1–3 ship over the course of the project; Phase 4 is the post-mortem that informs the next cycle.

When the 80/20 cut hurts

  • Design system consistency. If only 20% of surfaces get the new design and 80% don't, the product feels visually fragmented. Mitigation: even surfaces that don't get full redesign should adopt the design system tokens (color, type, spacing) for consistency.
  • Migration burden. Mixing old and new components creates maintenance complexity. Plan for the eventual deprecation of old components even if you're not redesigning every surface that uses them.
  • Edge-case surfaces. A surface used by 1% of users might be the only surface for a critical use case. The 80/20 cut shouldn't drop it.

Anti-patterns

  • The Big Bang redesign. Two-year project to redesign everything. Ships late, loses context, doesn't move metrics.
  • Surface-by-surface without strategy. Each surface designed independently with no shared system. Result: visual inconsistency.
  • Pet-surface focus. The team enjoys redesigning the most fun surfaces (marketing landing) while neglecting the highest-pain ones (settings, billing).
  • No measurement. A redesign ships with no before/after metrics. The team can't tell if it worked.

Heuristics

  1. The pain-traffic matrix. Plot surfaces by pain × traffic. Concentrate redesign on the top-right quadrant.
  2. The "would users notice?" check. For each candidate surface, ask: if we redesigned this and showed users, would they tell us it's better? If "probably not," lower priority.
  3. The conversion-impact estimate. For business-critical surfaces, estimate revenue impact of a 10% lift. Surfaces where 10% lift is meaningful deserve A/B tested redesign; surfaces where it isn't deserve cosmetic-only updates.
  4. The post-redesign metric. Define metrics before the redesign ships. If you can't measure improvement, you didn't redesign for the right reason.

Related sub-skills

  • 80-20-rule (parent).
  • 80-20-feature-prioritization — applied to new feature planning rather than redesign.
  • iteration (process) — redesigns benefit from iterative releases, not big bangs.
  • factor-of-safety (process) — redesign with safety: A/B test, kill-switch, rollback paths.

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 80 20 Redesign Targeting AI skill do?

Use this skill when scoping a redesign or refactor — choosing which existing surfaces deserve full attention and which can be left mostly alone. Trigger when the user asks "where should we focus our redesign?", "do we need to redo all of this?", or when planning incremental design improvements with limited capacity. Sub-aspect of `80-20-rule`; read that first.

Why use 80 20 Redesign Targeting on TypingMind?

Because you install it once and use it with any model. 80 20 Redesign Targeting 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 80 20 Redesign Targeting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Deibler/universal-design-principles/tree/main/plugins/cognition-and-learnability-principles/skills/80-20-redesign-targeting. 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 80 20 Redesign Targeting?

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 80 20 Redesign Targeting?

As many as you like. As long as a model supports skills, you can use 80 20 Redesign Targeting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the 80 20 Redesign Targeting AI skill free?

It is published on GitHub by Deibler. Check the repository for licensing terms. 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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