Nw Opportunity Mapping logo

Nw Opportunity Mapping

Organization
nWave-ai
nw-opportunity-mapping

Opportunity Solution Trees, opportunity scoring, Lean Canvas, JTBD job mapping, and technique selection guide

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-opportunity-mapping
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 Opportunity Mapping 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-opportunity-mapping
  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-opportunity-mapping .claude/skills/nw-opportunity-mapping
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Opportunity Mapping 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 Opportunity Mapping 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 Opportunity Mapping 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.

Opportunity Mapping

Opportunity Solution Tree (OST)

Connects desired outcomes to opportunities to solution ideas:

Desired Outcome
  |
  +-- Opportunity 1 (score)
  |     +-- Solution Idea A
  |     +-- Solution Idea B
  |
  +-- Opportunity 2 (score)
  |     +-- Solution Idea C
  |
  +-- Opportunity 3 (score)
        +-- Solution Idea D
        +-- Solution Idea E

Building the OST

  1. Define desired outcome from customer research
  2. Map opportunities from interview insights (min 5 distinct)
  3. Score each using Opportunity Algorithm
  4. Generate solution ideas for top 2-3
  5. Seek real diversity in solutions (avoid variations of same idea)

Opportunity Scoring Algorithm

Formula: Score = Importance + Max(0, Importance - Satisfaction) Importance: 1-10 | Satisfaction: 1-10 | Max score: 20

ScoreMeaningAction
>8High importance, satisfaction gap -- underservedPursue
5-8Moderate importance or partially servedEvaluate
<5Low importance or well-servedDeprioritize

Process: gather importance + satisfaction from 5+ interviews, calculate per opportunity, rank, select top 2-3.

Job Mapping (JTBD)

Universal Job Map Steps

StepGoalOutcome Format
DefineDetermine what needs doingMinimize time to identify [object]
LocateFind inputs/informationMinimize time to gather [resources]
PrepareReady inputs for useMinimize likelihood of missing [requirements]
ConfirmVerify readinessMinimize likelihood of incorrect [categorization]
ExecutePerform core taskMinimize time from [start] to [completion]
MonitorTrack progressMinimize uncertainty about [status]
ModifyAdjust if neededMinimize effort to correct [issues]
ConcludeComplete the jobMinimize time from [completion] to [result]

Outcome statement format: [Direction] + [Metric] + [Object] + [Clarifier] Example: "Minimize the time it takes to identify which project budget a new expense should be allocated to"

Lean Canvas

One-page business model for Phase 4 viability validation.

  1. Problem: Top 3 problems (Phase 1 validated) | 2. Customer Segments: by JTBD
  2. UVP: single clear message | 4. Solution: top 3 features for top problems
  3. Channels: path to customers (Phase 4 validated) | 6. Revenue Streams: monetization
  4. Cost Structure: key costs | 8. Key Metrics: activity metrics
  5. Unfair Advantage: not easily copied

4 Big Risks

RiskQuestionValidation
ValueWill customers want this?Interviews, fake doors
UsabilityCan customers use this?Prototype testing, task completion
FeasibilityCan we build this?Spikes, expert review
ViabilityBusiness model works?Lean Canvas, stakeholder review

Technique Selection Guide

GoalTechniques
Validate problemMom Test, Job Mapping
Understand needsOutcome Statements, Opportunity Mapping
Prioritize opportunitiesOST, Opportunity Algorithm
Generate solutionsIdeation with OST constraints
Validate solution valueHypothesis Testing, Prototypes
Test usabilityPrototype testing, Task completion
Assess feasibility4 Risks, Spikes
Structure business modelLean Canvas
Continuous learningWeekly customer touchpoints

Frequently asked questions

What does the Nw Opportunity Mapping AI skill do?

Opportunity Solution Trees, opportunity scoring, Lean Canvas, JTBD job mapping, and technique selection guide

Why use Nw Opportunity Mapping on TypingMind?

Because you install it once and use it with any model. Nw Opportunity Mapping 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 Opportunity Mapping in TypingMind?

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

Which AI models can use Nw Opportunity Mapping?

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 Opportunity Mapping?

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

Is the Nw Opportunity Mapping 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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