.claude/skills/customer-journey-map/SKILL.md
Create an end-to-end customer journey map with stages, touchpoints, emotions, pain points, and opportunities. Use when mapping the customer experience, identifying friction points, improving onboarding, or visualizing the user journey.
npx skillsauth add shalevamin/The-_Ultimate_agents customer-journey-mapInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Map the end-to-end customer experience from awareness through advocacy, identifying emotions, pain points, and improvement opportunities at each stage.
You are creating a customer journey map for $ARGUMENTS.
If the user provides files (interview transcripts, survey data, analytics, support tickets, or existing journey maps), read them first. Use web search to understand the product if a URL is provided.
Define the persona: Who is traveling this journey? Use a specific persona with JTBD, not a generic user.
Map the journey stages (adapt to the product):
| Stage | Description | |---|---| | Awareness | How do they first learn about the product? | | Consideration | What do they evaluate? What alternatives do they compare? | | Acquisition | How do they sign up or purchase? | | Onboarding | First experience with the product — time to value | | Engagement | Regular usage — building habits | | Retention | What keeps them coming back? What might cause churn? | | Advocacy | When and why do they recommend the product to others? |
For each stage, document:
Identify critical moments:
Create the journey map table:
| Stage | Touchpoint | User Action | Emotion | Pain Point | Opportunity | |---|---|---|---|---|---|
Recommend prioritized improvements:
Think step by step. Save as a markdown document. For visual journey maps, suggest the user create one in Miro or FigJam using this analysis as the foundation.
development
Use when building cross-platform applications with Flutter 3+ and Dart. Invoke for widget development, Riverpod/Bloc state management, GoRouter navigation, platform-specific implementations, performance optimization.
testing
Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.
tools
Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.
tools
Translate Figma nodes into production-ready code with 1:1 visual fidelity using the Figma MCP workflow (design context, screenshots, assets, and project-convention translation). Trigger when the user provides Figma URLs or node IDs, or asks to implement designs or components that must match Figma specs. Requires a working Figma MCP server connection.