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Nw Persona Jtbd Analysis

Organization
nWave-ai
nw-persona-jtbd-analysis

Structured persona creation and JTBD analysis methodology - persona templates, ODI job step tables, pain point mapping, success metric quantification, and multi-persona segmentation

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-persona-jtbd-analysis
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 Persona Jtbd Analysis 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-persona-jtbd-analysis
  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-persona-jtbd-analysis .claude/skills/nw-persona-jtbd-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Persona Jtbd Analysis 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 Persona Jtbd Analysis 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 Persona Jtbd Analysis 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.

Persona and JTBD Analysis

Use during Phase 1 (GATHER) when the user lacks clear personas or the "Who" section needs rigorous definition. Provides structured alternative to ad-hoc persona descriptions.

Persona Template

For each user type, build a complete persona:

markdown
## Persona: {Name}

**Who**: {Role description -- one sentence capturing relationship to product}
**Demographics**:
- {Characteristic 1: e.g., technical proficiency level}
- {Characteristic 2: e.g., frequency of interaction}
- {Characteristic 3: e.g., environment/context of use}
- {Characteristic 4: e.g., primary motivation}

**Jobs-to-be-Done**: (see Job Step Table below)

**Pain Points**:
- {Pain 1} -- maps to Job Step: {step name}
- {Pain 2} -- maps to Job Step: {step name}

**Success Metrics**:
- {Quantified outcome 1: e.g., "Task completed in < 2 minutes"}
- {Quantified outcome 2: e.g., "Zero manual configuration steps"}

Job Step Table

Each persona has job steps describing what they accomplish. Steps follow ODI format.

Job StepGoalDesired Outcome
{Verb}{What the user wants to achieve}Minimize {metric} of {undesirable state}

Rules for Job Steps

  • Job Steps are always verbs: Discover, Validate, Install, Configure, Monitor, Recover
  • Goals describe what the user wants, not what the system does
  • Desired Outcomes use ODI format: "Minimize [time/effort/risk/likelihood] of [undesirable state]"
  • Each step maps to a workflow point where value is created or destroyed

Example Job Step Table

Job StepGoalDesired Outcome
DiscoverFind the right tool for the taskMinimize time to evaluate fit
InstallGet the tool running locallyMinimize steps to working state
ConfigureAdapt to local environmentMinimize likelihood of misconfiguration
VerifyConfirm correct installationMinimize uncertainty about readiness
StartBegin productive workMinimize time from install to first output

Pain Point Mapping

Every pain point maps to a specific job step. Pain points without a corresponding step indicate either a missing step or irrelevant pain point.

Pain Point: "I don't know if the tool supports my OS"
  -> Job Step: Discover
  -> Desired Outcome: Minimize uncertainty about compatibility

Pain Point: "Installation fails silently with no error message"
  -> Job Step: Install
  -> Desired Outcome: Minimize time to diagnose installation failures

Prioritize: pain points on high-frequency job steps deserve attention first.

Success Metric Quantification

Every success metric needs a number or threshold. Qualitative metrics ("easy to use") are not actionable.

QualitativeQuantified
"Easy to install""Install completed in < 2 minutes with zero manual steps"
"Fast startup""First productive output within 30 seconds of launch"
"Reliable""Zero silent failures; all errors produce actionable messages"
"Intuitive""New user completes core task without reading documentation"

Multi-Persona Segmentation

Different users have fundamentally different jobs even when using the same product. Segment by relationship to the product.

Common axes: Frequency (first-time vs returning vs power user) | Role (end user vs admin vs developer) | Context (individual vs team vs CI/CD) | Motivation (exploration vs production vs evaluation)

Example: Same Product, Different Jobs

PersonaPrimary JobKey Difference
ExplorerEvaluate the tool quicklyNeeds fast time-to-value, minimal commitment
ReturnerResume work after absenceNeeds state preservation, quick re-orientation
DeployerInstall for a teamNeeds configuration management, multi-user setup
AutomatorIntegrate into CI/CD pipelineNeeds scriptability, headless operation, exit codes

Each persona gets their own Job Step table because workflows differ. Do not merge personas -- JTBD analysis value comes from surfacing differences.

Integration with Story Crafting

After completing persona analysis, feed results into LeanUX user story template:

  1. Persona Who -> populated from persona template
  2. Persona Pain Points -> inform story Problem section
  3. Job Step Desired Outcomes -> inform AC (ODI outcomes translate to testable criteria)
  4. Success Metrics -> inform NFR requirements in handoff package

Cross-reference: use bdd-requirements skill for Example Mapping once personas established. Use jtbd-workflow-selection skill to determine workflow for resulting stories.

Frequently asked questions

What does the Nw Persona Jtbd Analysis AI skill do?

Structured persona creation and JTBD analysis methodology - persona templates, ODI job step tables, pain point mapping, success metric quantification, and multi-persona segmentation

Why use Nw Persona Jtbd Analysis on TypingMind?

Because you install it once and use it with any model. Nw Persona Jtbd Analysis 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 Persona Jtbd Analysis in TypingMind?

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

Which AI models can use Nw Persona Jtbd Analysis?

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 Persona Jtbd Analysis?

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

Is the Nw Persona Jtbd Analysis 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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