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Nw Design Methodology

Organization
nWave-ai
nw-design-methodology

Apple LeanUX++ design workflow, journey schema, emotional arc patterns, and CLI UX patterns. Load when transitioning from discovery to visualization or when designing journey artifacts.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-design-methodology
Stars
616
Forks
65
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 nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Design Methodology 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/nWave-ai/nWave/tree/main/nWave/skills/nw-design-methodology
  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/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-design-methodology .claude/skills/nw-design-methodology
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Design Methodology 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 Nw Design Methodology 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 Nw Design Methodology 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 Methodology (Apple LeanUX++)

Design Workflow

PHASE 1              PHASE 2              PHASE 3              PHASE 4
Journey Mapping      Emotional Design     TUI Prototyping      Integration Check
      |                    |                    |                    |
      v                    v                    v                    v
"What's the flow?"   "How should it feel?"  "What does it look?"  "Does it connect?"

Phase 1: Journey Mapping (1-2 days)

  • Techniques: User journey mapping | goal-completion flow | step identification
  • Question: "What complete journey is the user trying to accomplish?"
  • Output: Journey map with steps, commands, and touchpoints

Phase 2: Emotional Design (1 day)

  • Techniques: Emotional arc design | form follows feeling | transition analysis
  • Question: "How should the user FEEL at each step?"
  • Output: Emotional annotations on journey map

Phase 3: TUI Prototyping (1-3 days)

  • Techniques: Progressive fidelity | ASCII mockups | TUI design patterns
  • Question: "What does each step look like?"
  • Output: TUI mockups for each journey step

Phase 4: Integration Check (1 day)

  • Techniques: Shared artifact tracking | horizontal coherence | CLI vocabulary
  • Question: "Do all pieces connect properly?"
  • Output: Validated journey with integration checkpoints

Journey Schema

yaml
schema_version: 1

journey:
  name: "{Goal Name}"
  goal: "{What user is trying to accomplish}"
  persona: "{User persona reference}"

  emotional_arc:
    start: "{Initial emotional state}"
    middle: "{Journey emotional state}"
    end: "{Final emotional state}"

steps:
  - id: 1
    name: "{Step Name}"
    command: "{CLI command or action}"

    tui_mockup: |
      +-- Step N: {Name} -----------------------------------------+
      | {ASCII representation of CLI output}                       |
      | ${variable} <-- tracked artifact                           |
      +------------------------------------------------------------+

    shared_artifacts:
      - name: "{artifact_name}"
        source: "{single source of truth file}"
        displayed_as: "${variable}"
        consumers: ["{list of places this appears}"]

    emotional_state:
      entry: "{How user feels entering step}"
      exit: "{How user feels after step}"

    integration_checkpoint: |
      {What must be validated before proceeding}

    failure_modes:
      - "{What can go wrong at this step — used by DISTILL for error scenario generation}"
      - "{Another failure scenario}"

    gherkin: |
      Scenario: {Step description}
        Given {precondition}
        When {action}
        Then {observable outcome}
        And shared artifact "${variable}" matches source

integration_validation:
  shared_artifact_consistency:
    - artifact: "{name}"
      must_match_across: [1, 2, 3]
      failure_message: "{Integration error description}"

changelog:
  - date: "{YYYY-MM-DD}"
    feature: "{feature-id}"
    change: "{What changed in this update}"

Emotional Arc Patterns

Confidence Building

Start: Anxious/Uncertain | Middle: Focused/Engaged | End: Confident/Satisfied Use when: Complex multi-step operations

Discovery Joy

Start: Curious | Middle: Exploring | End: Delighted Use when: Learning new features

Problem Relief

Start: Frustrated | Middle: Hopeful | End: Relieved Use when: Fixing issues or debugging

Transition Rules

  • Build confidence progressively through small wins
  • Provide clear feedback at each step
  • Error states guide to resolution rather than adding frustration
  • Positive-to-negative transitions need explicit warning or buffer step

Apple Design Principles Applied

  • Form Follows Feeling: Design for emotion first, function second
  • Concentrated Focus: One thing done excellently beats many done adequately
  • Material Honesty: Respect the medium -- CLI should feel like CLI
  • Hidden Quality: Excellence in details users may never see

CLI UX Patterns (clig.dev)

Command Structure

  • Pattern: tool [noun] [verb] or tool [verb] [noun]
  • Pick one pattern consistently across entire journey
  • Example: crafter agent create or crafter create agent

Feedback Principles

Responsive: print something in <100ms | Progress: show for long operations | Transparent: show what is happening | Recoverable: clear errors with suggested fixes

Progressive Disclosure

  • Level 1 (default): Basic output for common use
  • Level 2 (--verbose): Detailed output for power users
  • Level 3 (--debug): Diagnostic output for troubleshooting

Help Design

Implement --help on every command | Make help discoverable | Provide contextual suggestions

Output Formats

Three artifact types produced:

  1. Visual Journey (journey-{name}-visual.md): ASCII flow diagram with emotional annotations and TUI mockups per step
  2. Structured Schema (journey-{name}.yaml): Machine-readable journey definition following schema above
  3. Gherkin Scenarios (journey-{name}.feature): Testable acceptance scenarios from each journey step

All artifacts go to docs/feature/{feature-id}/discuss/.

Frequently asked questions

What does the Nw Design Methodology AI skill do?

Apple LeanUX++ design workflow, journey schema, emotional arc patterns, and CLI UX patterns. Load when transitioning from discovery to visualization or when designing journey artifacts.

Why use Nw Design Methodology on TypingMind?

Because you install it once and use it with any model. Nw Design Methodology 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 Nw Design Methodology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-design-methodology. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Design Methodology?

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 Nw Design Methodology?

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

Is the Nw Design Methodology AI skill free?

Yes. It is published on GitHub by nWave-ai 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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