Hr Employee Journey Mapping logo

Hr Employee Journey Mapping

Community
tuanductran
hr-employee-journey-mapping

Help employee experience and HR teams map the end-to-end employee journey to identify moments that matter and friction points. Use when asked to map the employee journey, build a journey map for [stage], identify moments that matter, find friction points in the employee experience, or redesign a specific stage of the employee lifecycle.

Overview

Publishertuanductran
Repositoryhr-skills
Skill namehr-employee-journey-mapping
Stars
78
Forks
20
Bundled files
3
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.

  • 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 tuanductran on GitHub. Read the source before you install it.

Installation

Install the Hr Employee Journey 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/tuanductran/hr-skills/tree/main/skills/hr-employee-journey-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/tuanductran/hr-skills.git /tmp/hr-skills
mkdir -p .claude/skills
cp -r /tmp/hr-skills/skills/hr-employee-journey-mapping .claude/skills/hr-employee-journey-mapping
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hr Employee Journey 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 Hr Employee Journey 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 Hr Employee Journey 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.

Employee journey mapping

Map the end-to-end employee journey — from attraction through alumni — to identify moments that matter, surface friction points, and redesign specific stages around what employees actually experience.

Supported tasks

  • Building end-to-end employee journey maps across the full lifecycle
  • Mapping detailed journeys for specific stages (onboarding, promotion, parental leave)
  • Identifying "moments that matter" that disproportionately shape employee sentiment
  • Surfacing friction points and pain points within a specific journey stage
  • Combining qualitative (interviews, surveys) and quantitative (systems data) journey inputs
  • Comparing intended journey design against actual lived employee experience
  • Designing journey improvements and prioritizing which to fix first
  • Building persona-specific journey maps (new grad, senior IC, people manager, returning parent)
  • Facilitating journey mapping workshops with cross-functional stakeholders
  • Translating journey map findings into concrete process or policy changes
  • Measuring the impact of journey redesign on experience metrics
  • Maintaining and updating journey maps as processes evolve

Key prompts

Building journey maps

  1. "Build an end-to-end employee journey map from attraction through alumni for [company/role type]."
  2. "Map a detailed journey for the [onboarding / promotion / parental leave / offboarding] stage, including touchpoints, emotions, and systems involved."
  3. "Design a persona-specific journey map for [new graduate / senior IC / returning parent] that reflects their distinct experience."
  4. "What data sources — surveys, exit interviews, HRIS timestamps — should feed into an accurate journey map for [stage]?"

Identifying moments and friction

  1. "Identify the 'moments that matter' in our employee journey — points that disproportionately shape how employees feel about the company."
  2. "Where are the biggest friction points in our [specific stage] journey based on this qualitative and quantitative data?"
  3. "Compare our intended [journey stage] design against what employees report actually experiencing — where's the gap?"
  4. "How do we distinguish a true moment that matters from a minor annoyance that doesn't significantly shape the employee experience?"

Redesign and prioritization

  1. "Prioritize which friction points in this journey map to fix first, based on impact and feasibility."
  2. "Redesign the [journey stage] to address the top friction points identified, with specific process or policy changes."
  3. "Design a journey mapping workshop agenda to run with cross-functional stakeholders for [stage]."
  4. "Draft a business case for investing in [journey stage] redesign that quantifies the cost of the current friction."

Measuring impact

  1. "What metrics should we track to measure whether our journey redesign for [stage] actually improved the employee experience?"
  2. "How should we maintain and update this journey map as our processes and systems evolve?"
  3. "What is the right cadence for revisiting a journey map to ensure it stays accurate as the organization changes?"
  4. "How do we attribute an improvement in retention or engagement scores to a specific journey redesign versus other factors?"

Tips

  • Ground journey maps in real employee input, not assumptions — HR's mental model of a journey often differs meaningfully from what employees actually experience.
  • Focus on "moments that matter" rather than mapping every touchpoint with equal weight; a few high-impact moments usually drive most of the sentiment.
  • Combine qualitative color (how it felt) with quantitative data (where drop-off or delay happens) for a complete picture.
  • Prioritize fixes by impact and feasibility together — the most emotionally significant friction point isn't always the easiest or highest-leverage to fix first.
  • Revisit journey maps periodically; processes, tools, and policies change, and a map built two years ago may no longer reflect the current experience.

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 Hr Employee Journey Mapping AI skill do?

Help employee experience and HR teams map the end-to-end employee journey to identify moments that matter and friction points. Use when asked to map the employee journey, build a journey map for [stage], identify moments that matter, find friction points in the employee experience, or redesign a specific stage of the employee lifecycle.

Why use Hr Employee Journey Mapping on TypingMind?

Because you install it once and use it with any model. Hr Employee Journey 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 Hr Employee Journey Mapping in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tuanductran/hr-skills/tree/main/skills/hr-employee-journey-mapping. 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 Hr Employee Journey 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 Hr Employee Journey Mapping?

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

Is the Hr Employee Journey Mapping AI skill free?

Yes. It is published on GitHub by tuanductran 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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