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Screen Flow Patterns

Community
phazurlabs
screen-flow-patterns

Taxonomy of 25+ screen types, 15+ user flows, and 25+ UI element deep-dives with layout patterns, component hierarchies, state matrices, and best-in-class references. Use when deciding which screens a product needs and what connects them, before any visual design starts.

Overview

Publisherphazurlabs
Repositorysumi
Skill namescreen-flow-patterns
Stars
58
Forks
11
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Screen Flow Patterns 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/phazurlabs/sumi/tree/main/skills/screen-flow-patterns
  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/phazurlabs/sumi.git /tmp/sumi
mkdir -p .claude/skills
cp -r /tmp/sumi/skills/screen-flow-patterns .claude/skills/screen-flow-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Screen Flow Patterns 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 Screen Flow Patterns 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 Screen Flow Patterns 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.

Screen and Flow Patterns

Mental model

Before anything is styled, two questions decide the shape of a product: which screens exist, and what connects them. Getting this wrong is expensive in a way visual decisions never are -- you can restyle a screen in an afternoon; you cannot re-architect a flow after it ships without breaking people's habits.

  • Name the screen type before designing it. Nearly every screen is an instance of a known type with known states and a known failure mode. "A page where users manage their team" is a List/Feed with an invite flow attached, and saying so imports thirty solved decisions.
  • A flow is a sequence with an exit at every step. If a step has no way back and no way out, it is a trap, and traps are where support tickets come from.
  • Design the states, not the screen. The happy path is the easy fifth of the work. The other four fifths are empty, loading, error and partial.
  • Density is a decision, not a consequence. Pick a tier and hold it.

Constants

Every screen ships all five. A screen specified only in its ideal state is not specified.

Every screen passes through states. Model them explicitly to ensure all states are designed.

6.1 The Five Core States

[Initial] -> [Loading] -> [Loaded / Populated]
                |               |
                v               v
            [Error]       [Empty]
                |               |
                v               v
           [Retry -> Loading]  [Action -> Loading]
  1. Initial: Screen is mounted but no data request has fired yet. Often invisible (immediate transition to Loading).
  2. Loading: Data is being fetched. Show skeleton screens (preferred), spinner, or shimmer. Never show a blank screen.
  3. Loaded / Populated: Data received and displayed. The "happy" state. Most design time is spent here.
  4. Empty: Data request succeeded but returned zero items. Show empty state illustration + CTA.
  5. Error: Data request failed. Show error message + retry button. Distinguish between network error, server error, and permission error.

6.2 Extended States

  • Refreshing: Pulling to refresh or background sync. Show inline indicator without replacing content.
  • Partial Load: Some data loaded, more available. Infinite scroll loading indicator at bottom.
  • Stale: Data is old. Show "last updated" timestamp. Background refresh in progress.
  • Offline: No network connection. Show cached data with offline banner. Queue actions for sync.
  • Permission Required: User has not granted necessary permission (location, camera, contacts). Show explanation + request button.

6.3 State Design Checklist

For every screen, verify you have designed:

  • Loading state (skeleton or spinner)
  • Populated state (with real data, not lorem ipsum)
  • Empty state (zero items, with helpful CTA)
  • Error state (with clear message and retry)
  • Partial/paginated state (loading more indicator)
  • Offline state (if applicable)
  • Permission request state (if applicable)

Section 7: Information Density Levels

7.1 Three Density Tiers

TierDescriptionTarget Elements per ViewportExample Screen Types
LowBreathing room. One idea per fold. Marketing-oriented.3-5Landing, onboarding, login, confirmation, empty state, error pages
MediumBalanced. Content-rich but not overwhelming. Scrolling expected.6-12Profile, detail, settings, blog/article, checkout, contact
HighData-dense. Power users. Scanning and filtering essential.12-25+Dashboard, analytics, file manager, data table, calendar, comparison

7.2 Density Adjustments by Platform

  • Mobile: Reduce density by 30-40% vs desktop. Stack columns. Collapse secondary info behind taps.
  • Tablet: Intermediate density. Use split views to maintain context without overwhelming.
  • Desktop: Highest density acceptable. Multi-column layouts, sidebars, persistent panels.

7.3 Density Controls for Users

  • Comfortable / Cozy / Compact: Gmail-style density toggle. Adjusts row height and spacing.
  • Card / List / Table: View mode toggle. Cards are lowest density, tables are highest.
  • Show/Hide Columns: Let users configure which columns appear in data tables.
  • Collapse Panels: Resizable and collapsible sidebars for user-controlled density.

Section 8: Platform-Specific Screen Conventions

Index

Screen types (32) -- full specification for each in references/screen-type-catalog.md:

Landing · Home/Dashboard · List/Feed · Detail/Show · Profile · Settings · Search/Results · Checkout · Onboarding · Login · Sign-up · Forgot Password · Empty State · Error 404 · Error 500 · Maintenance · Loading/Splash · Notifications Center · Analytics/Reporting · File Manager · Editor/Canvas · Wizard/Multi-Step · Chat/Messaging · Calendar · Map View · Comparison · About/Team · Contact · Blog/Article · Product Gallery · Booking/Reservation · Confirmation/Success

Flow patterns (22) -- full specification for each in references/user-flow-catalog.md:

Authentication · Onboarding · Purchase/Checkout · Search-Filter-Select · CRUD · Invite/Share · Communication · Notification-to-Action · Upgrade/Upsell · Cancellation/Churn · Help/Support · Data Import/Export · Review/Rating · Onboarding Checklist · Collaborative Editing · Subscription/Renewal · Referral · Multi-Device Handoff · Account Deletion · Error Recovery · Settings Management · Content Creation

UI elements (120+) across navigation, input, feedback, overlay and layout -- references/ui-element-deep-dive.md.

When documenting or communicating user flows, use these standardized conventions.

3.1 Node Types

  • Start / End: Rounded rectangle (pill shape). Green for start, red for end.
  • Screen / Page: Rectangle. Contains screen name. Color-coded by section.
  • Decision: Diamond. Contains yes/no or conditional question.
  • Action: Rectangle with rounded corners. User or system action.
  • Sub-flow: Rectangle with double border. Links to separate flow diagram.
  • Wait / Delay: Hourglass or clock icon. Async step (email verification, processing).

3.2 Path Types

  • Happy Path: Solid green arrows. The primary expected journey.
  • Error Path: Dashed red arrows. What happens when things fail.
  • Alternative Path: Dotted blue arrows. Valid but less common routes.
  • Escape Hatch: Gray arrows. Exit points (cancel, back, close).

3.3 Entry Points

  • Direct URL: Globe icon. User arrives via typed URL or link.
  • Deep Link: Chain icon. From push notification, email, or external app.
  • Navigation: Menu icon. From in-app navigation.
  • System Trigger: Gear icon. Automated redirect (session expire, maintenance).

3.4 Edge Case Annotations

  • Conditional: Annotate arrows with conditions (e.g., "if cart > $50").
  • Frequency: Label paths with expected traffic percentage (e.g., "80% of users").
  • Data Dependencies: Note when a step requires data from a previous step.
  • Timeout: Note time limits (e.g., "OTP expires in 10 min").

Section 4: UI Element Pattern Catalog (120+ Elements)

Reference architecture

FileCoversLines
references/screen-type-catalog.md32 screen types, fully specified1731
references/ui-element-deep-dive.md120+ elements, 26 in depth1259
references/user-flow-catalog.md22 flows step by step1116
references/inspiration-reference-guide.mdwhere to look for precedent228
references/transitions-and-platform-conventions.mdpush, modal, tab, deep link; iOS/Android/web84

What every reference file contains

  1. Purpose -- the one job this screen or flow does
  2. The component hierarchy, named
  3. The state matrix: empty, loading, error, partial, ideal
  4. Accessibility requirements specific to the type
  5. Best-in-class examples, named
  6. The failure mode that type is known for

Routing

For deciding which screens a product needs and the full specification of each: read references/screen-type-catalog.md.

For the sequence connecting them -- entry points, steps, decision points, exits and error branches: read references/user-flow-catalog.md.

For a specific element -- navigation, input, feedback, overlay or layout: read references/ui-element-deep-dive.md.

For how screens move between each other and what each platform expects: read references/transitions-and-platform-conventions.md.

For precedent -- where to look and what to look for: read references/inspiration-reference-guide.md.

Cross-References

  • page-composition-engine -- once the screen type is chosen, this composes it
  • navigation-pattern-encyclopedia -- the navigation connecting the screens
  • performance-states-patterns -- the empty, loading and error states in depth
  • ui-pattern-intelligence -- whether the pattern chosen is still current
  • mobile-ux-design -- the mobile form of each screen type

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 Screen Flow Patterns AI skill do?

Taxonomy of 25+ screen types, 15+ user flows, and 25+ UI element deep-dives with layout patterns, component hierarchies, state matrices, and best-in-class references. Use when deciding which screens a product needs and what connects them, before any visual design starts.

Why use Screen Flow Patterns on TypingMind?

Because you install it once and use it with any model. Screen Flow Patterns 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 Screen Flow Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/phazurlabs/sumi/tree/main/skills/screen-flow-patterns. 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 Screen Flow Patterns?

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 Screen Flow Patterns?

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

Is the Screen Flow Patterns AI skill free?

Yes. It is published on GitHub by phazurlabs 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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