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Nw Discovery Methodology

Organization
nWave-ai
nw-discovery-methodology

Question-first approach to understanding user journeys. Load when starting a new journey design or when the discovery phase needs deepening.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-discovery-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 Discovery 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-discovery-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-discovery-methodology .claude/skills/nw-discovery-methodology
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Discovery 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 Discovery 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 Discovery 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.

Discovery Methodology

Discover journeys through deep questioning before any sketching. The sketch is proof of understanding, not the starting point.

Session Flow

Phase 1: Goal Discovery (First 5-10 minutes)

Focus: What is the user trying to accomplish?

Questions:

  • "What's the ultimate goal you're trying to achieve?"
  • "What triggers this journey? When does a user start this?"
  • "How will you know when you've succeeded?"
  • "What's the happy path in your mind?"
Draft Sketch (after first 3 answers)

After collecting goal, trigger, and success criteria, output a rough draft sketch showing what the journey might look like. Mark unknowns with ???. Purpose: make value visible immediately so the user sees progress, not just questions.

[Trigger: ???] → [Step 1: ???] → [Step 2: ???] → [Goal: {stated goal}]
  Feels: ???       Sees: ???       Sees: ???       Feels: {success criteria}
  Artifacts: ???   Artifacts: ???  Artifacts: ???

This is a working hypothesis, not a commitment. Update it after each phase as understanding deepens. The sketch gives the user something concrete to react to — "no, step 2 happens before step 1" is more productive than abstract discussion.

Phase 2: Mental Model (10-20 minutes)

Focus: What does the user EXPECT to see?

Questions:

  • "Walk me through step by step -- what do you type, what do you see?"
  • "At this step, what information appears on screen?"
  • "What would you need to see to feel confident?"
  • "Where do you think this data comes from?"
  • "What's your mental model of how this works?"

Phase 3: Emotional Journey (5-10 minutes)

Focus: How should the user FEEL?

Questions:

  • "How should you feel at the start? Anxious? Curious? Confident?"
  • "What's the emotional arc -- where's the peak tension?"
  • "How should you feel when it's done?"
  • "Where might you feel frustrated or lost?"
  • "What would make this feel satisfying vs frustrating?"

Phase 4: Shared Artifacts (5-10 minutes)

Focus: What data is shared across steps?

Questions:

  • "What information appears in multiple places?"
  • "Where does the version number come from? Where is it shown?"
  • "If this path changes, what else breaks?"
  • "Who owns this piece of data?"
  • "What paths or URLs are reused across steps?"

Phase 5: Error Paths (5 minutes)

Focus: What could go wrong?

Questions:

  • "What's the most likely failure?"
  • "What should the user see when it fails?"
  • "How does the user recover?"
  • "What would a helpful error message look like?"

Phase 6: Integration Points

Focus: How do steps connect?

Questions:

  • "What did the previous step produce that this step needs?"
  • "What does this step produce that the next step needs?"
  • "Are there any hidden dependencies between steps?"
  • "What external systems or files does this touch?"

Phase 7: CLI UX Specifics

Focus: What commands and output does the user expect?

Questions:

  • "What command would you naturally type for this?"
  • "What flags or options do you expect?"
  • "How verbose should the output be by default?"
  • "Should there be a --dry-run option?"

Question Format

Use AskUserQuestion with structured options:

  • 2-4 concrete options based on design methodology
  • Options represent real design alternatives
  • Include an "Other" option for open-ended input
  • Each option includes its design implication

Sketch Readiness Criteria

Ready to sketch ONLY when all can be answered:

  • Complete happy path described (no "and then something happens")
  • Each step has expected output defined
  • Emotional arc is explicit and coherent
  • Shared artifacts identified with sources
  • At least major error paths acknowledged

If ANY criterion is unclear -- ask more questions.

When to Question

Always ask first when:

  • User requests a sketch -- ask before sketching
  • User describes a feature -- ask to understand journey context
  • User mentions a command -- ask what they expect to see

Continue asking until:

  • User can describe complete happy path without gaps
  • All shared artifacts identified with sources
  • Emotional arc is explicit and coherent
  • Error paths are at least acknowledged

Anti-Patterns

  • Jumping to sketching before understanding mental model
  • Assuming you know what the user expects
  • Filling gaps with your own assumptions
  • Skipping emotional journey questions
  • Ignoring shared artifact tracking

Instead:

  • Ask one more question when uncertain
  • Reflect back understanding for user validation
  • Make the user articulate their mental model explicitly
  • Map emotional states at every step
  • Document every ${variable} and its source

Frequently asked questions

What does the Nw Discovery Methodology AI skill do?

Question-first approach to understanding user journeys. Load when starting a new journey design or when the discovery phase needs deepening.

Why use Nw Discovery Methodology on TypingMind?

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

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

Which AI models can use Nw Discovery 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 Discovery Methodology?

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

Is the Nw Discovery 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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