User Journey Tracking

Organization

nexus-labs-automation

user-journey-tracking

Track user journeys with intent context and friction signals. Use when instrumenting onboarding, checkout, or any multi-step flow where you need to understand WHY users fail.

Overview

Publisher	nexus-labs-automation
Repository	`mobile-observability`
Skill name	`user-journey-tracking`
Stars	116
Forks	12
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 nexus-labs-automation on GitHub. Read the source before you install it.

Installation

Install the User Journey Tracking AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/nexus-labs-automation/mobile-observability/tree/main/skills/user-journey-tracking

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/nexus-labs-automation/mobile-observability.git /tmp/mobile-observability
mkdir -p .claude/skills
cp -r /tmp/mobile-observability/skills/user-journey-tracking .claude/skills/user-journey-tracking

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable User Journey Tracking 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 User Journey Tracking 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 User Journey Tracking 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.

User Journey Tracking

Track not just WHAT users do, but WHETHER they accomplished their goal.

Core Principle

Every journey event should help answer: "Why did users fail to complete their intended task?"

Key Context to Attach

Field	Example	Purpose
`job_name`	"checkout"	User's intended task
`job_step`	"payment"	Current step in journey
`job_progress`	"3/4"	How far they got
`outcome`	"success" / "friction" / "abandon"	Did they succeed?

Friction Signals to Track

Detect user struggle before they contact support:

Signal	Detection
Rage taps	3+ taps same element in 1s
Retry exhaustion	3+ retries of same action
Quick abandonment	Exit within 5s of error
Navigation loops	3+ back navigations without progress

Outcome Quality

Not just success/failure:

Completed smoothly — no friction
Completed with friction — retries, errors, slow
Abandoned after friction — struggled, then quit
Abandoned immediately — no engagement

"Completed with friction" is often the most actionable signal.

When to Use This Skill

Onboarding flows
Checkout/payment funnels
Signup/registration
Any multi-step process
Feature adoption tracking

Implementation References

Topic	Reference
Full methodology	`references/user-focused-observability.md`
Job-based patterns	`references/jtbd.md`
Friction detection code	`references/user-journeys.md`
Journey correlation	`references/user-journeys.md`

Decision Tree

Before adding journey instrumentation:

Does this help identify what the user was trying to do? → Add intent context
Does this help determine if they succeeded? → Track outcomes
Does this help explain why they failed? → Add friction signals

If no to all three → probably don't need it.

Related Skills

See skills/instrumentation-planning for prioritization framework
Combine with skills/interaction-latency for friction detection on key actions
Combine with skills/navigation-latency for screen transition context

Frequently asked questions

What does the User Journey Tracking AI skill do?

Track user journeys with intent context and friction signals. Use when instrumenting onboarding, checkout, or any multi-step flow where you need to understand WHY users fail.

Why use User Journey Tracking on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nexus-labs-automation/mobile-observability/tree/main/skills/user-journey-tracking. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use User Journey Tracking?

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 User Journey Tracking?

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

Is the User Journey Tracking AI skill free?

Yes. It is published on GitHub by nexus-labs-automation 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.

View all

Caveman Compress

JuliusBrussee

CommunityPopular

Compress a memory file such as CLAUDE.md or a todo list into caveman format to save input tokens, keeping a readable backup. Trigger: /caveman-compress.

Caveman Discover

Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.

Caveman Manage

Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.

Caveman Setup

Wire a repository through the Caveman Cloud gateway so every LLM request is measured, with no behavior change. Use for "set up caveman" or adding LLM spend observability.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

User Journey Tracking

Core Principle

Key Context to Attach

Friction Signals to Track

Outcome Quality

When to Use This Skill

Implementation References

Decision Tree

Related Skills

Frequently asked questions

Related AI Skills

Set up your own AI workspace now