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Journey Mapper

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joeyvansommeren
journey-mapper

Scans a codebase and generates a self-contained HTML service-design journey map — NN/g combined customer journey + service blueprint format. Use when the user wants to map user journeys, create journey maps, build service blueprints, or understand user flows from code. Prompts for codebase path if not supplied. Works with any tech stack.

Overview

Publisherjoeyvansommeren
Repositoryjourney-mapper
Skill namejourney-mapper
Stars
95
Forks
6
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Journey Mapper 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/joeyvansommeren/journey-mapper/tree/main/skills/journey-mapper
  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/joeyvansommeren/journey-mapper.git /tmp/journey-mapper
mkdir -p .claude/skills
cp -r /tmp/journey-mapper/skills/journey-mapper .claude/skills/journey-mapper
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Journey Mapper 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 Journey Mapper 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 Journey Mapper 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.

Journey Mapper

Reads a codebase, thinks like a service designer, and writes a single browser-ready HTML file with all inferred user journeys and service blueprints.

See REFERENCE.md in this skill's base directory for the JSON schema, NN/g methodology notes, and emotional arc heuristics.

Quick start

If the codebase is not already in scope, ask:

"Which directory should I scan? And where should I save the output HTML?"

Workflow

1. Gather context — ask for anything not already in scope:

InputDefault
Codebase pathRequired — ask if missing
Output pathjourney-map.html in codebase root
Product nameUsed in the HTML title and rail heading
Extra contextDesign docs, research, README, API specs
ScopeFull scan, or specific subdirectory for large codebases

2. Scan the codebase — dispatch Explore, or use Grep + Read for targeted sweeps. Extract:

  • Routes, screens, page components, entry points
  • Auth roles, user types, permission models, tenant structures
  • Onboarding flows, auth gates, redirect chains, feature flags
  • API endpoints, background jobs, third-party integrations
  • Email templates, push notifications, in-app alerts, webhook payloads
  • Error states, empty states, loading patterns, blocked or warning states

For large codebases: routes first → components → API layer → notifications.

3. Organise into NN/g structure — think as a service designer:

  • 2–5 actors — who uses the system (infer from auth roles, user types, API consumers)
  • 3–6 categories — thematic groups (Onboarding, Core workflow, Admin, Recovery, Alternative paths…)
  • 4–12 journeys — one per meaningful end-to-end scenario; cover golden paths first, then error/recovery
  • Each journey → 2–5 stages → 2–4 moments per stage

Per-moment fields to fill — every inferred value gets [Assumption] prefix:

FieldNotes
doingWhat the user physically does. Factual. No [Assumption].
frontstageWhat they see: UI, email, native prompt. Factual.
backstageWhat the code does behind the scenes. Factual.
supportWhich system, service, or API underpins this. Factual.
thinking[Assumption] — inferred user thought at this moment.
feelingInteger 1–5 (1 = very frustrated, 5 = delighted). Use 2–3 for friction-heavy moments.
pain[Assumption] — inferred friction point.
opportunity[Assumption] — inferred improvement idea.
evidenceLeave blank "" — the human fills this from user research.

See REFERENCE.md for emotional arc heuristics and a worked example.

4. Build the HTML with build.py (in this skill's base directory, shown above when the skill loads). It validates the JSON, fills in the template, and writes the map. Do not retype TEMPLATE.html yourself.

  1. Write the JSON to a temporary file, e.g. journeys.json in your scratch or temp directory — not in the user's codebase.
  2. Run:
    bash
    python3 <skill-dir>/build.py journeys.json <output-path> --product "<Product name>"
  3. Exit code 1 means the JSON has errors; the script lists each one and writes nothing. Fix them and run again. Warnings (missing [Assumption] prefixes, unused actors, counts outside the structural guidance) don't block the build, but fix the ones that matter.
  4. Keep the product name stable across regenerations of the same product — the page title keys the browser's saved annotations.

If Python 3 isn't available, check and build by hand instead:

  • Every journey.actor and journey.category matches an actors[].id / categories[].id; every actor appears in at least one journey.
  • All id values are kebab-case and unique; moment.id values are unique across the whole file. feeling is an integer 1–5, or omitted.
  • Copy TEMPLATE.html to the output path (e.g. with cp), then edit three spots in the copy: <title>Journey Maps</title> → <title>[Product name] — Journey Maps</title>, <h1>Journey Maps</h1> → <h1>[Product name]</h1>, and the contents of the <script type="application/json" id="journeys-data">…</script> block → your JSON. Change nothing else.
  • In that JSON, escape every </ inside string values as <\/, or text such as </script> ends the data block early and the page renders blank.

5. Report back:

  • Path the file was saved to
  • Journey count · moment count · category count (build.py prints these)
  • Coverage gaps: flows or actor types with thin codebase evidence
  • 2–3 standout pain points inferred from error states, friction-heavy flows, or complex conditionals

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 Journey Mapper AI skill do?

Scans a codebase and generates a self-contained HTML service-design journey map — NN/g combined customer journey + service blueprint format. Use when the user wants to map user journeys, create journey maps, build service blueprints, or understand user flows from code. Prompts for codebase path if not supplied. Works with any tech stack.

Why use Journey Mapper on TypingMind?

Because you install it once and use it with any model. Journey Mapper 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 Journey Mapper in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/joeyvansommeren/journey-mapper/tree/main/skills/journey-mapper. 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 Journey Mapper?

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 Journey Mapper?

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

Is the Journey Mapper AI skill free?

It is published on GitHub by joeyvansommeren. Check the repository for licensing terms. 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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