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    CJM Builder Pro

    by Ehab

    2

    Turn raw research into offline HTML journey maps with emotion curves and a live English/Arabic toggle.

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    See it in action

    You say

    Run /cjm on the ./user-interviews folder to create an AS-IS map and report for our onboarding flow. Generate it in both English and Arabic.

    Your agent does

    Completed. I analyzed 12 interview transcripts. Found 4 critical pain points in the 'Account Setup' phase.

    • Generated: cjm-workspace.html
    • Features: 5-step journey, emotion curve, evidence-linked pain points, and RTL Arabic support. Open the file in any browser to view.

    What you get

    Convert raw transcripts into visual AS-IS journey maps with evidence tagging.Generate future-state TO-BE maps with target emotion curves.Create bilingual journey maps with full RTL Arabic support for presentations.Export journey data to CSV or JSON for integration with other product tools.

    About this skill

    The problem

    Synthesizing hundreds of interview notes and survey results into a structured journey map takes days of manual tagging. Traditional design tools are often locked behind subscriptions, lack RTL support for global clients, and require uploading sensitive research data to the cloud.

    What it does

    • Generates a portable, offline HTML workspace containing AS-IS and TO-BE journey maps plus a research report.
    • Builds a column-aligned emotion curve that visually maps user sentiment across every journey phase.
    • Includes a live English and Arabic (RTL) toggle to flip the entire interface and content for bilingual presentations.
    • Traces every pain point back to specific evidence found in the source research files.
    • Exports data to Markdown, CSV, JSON, and print-ready PDF directly from the generated workspace.

    Why this beats prompting it yourself

    Standard LLM prompts struggle to maintain alignment between emotion curves and journey steps in a visual format. This skill uses a specialized engine to ensure data integrity across swimlanes while keeping the entire output in a single, local HTML file that requires zero hosting or API dependency.

    Use cases

    • Turn a folder of raw interview transcripts into a client-ready discovery report.
    • Brainstorm future-state service designs by generating TO-BE maps alongside current friction points.
    • Present research findings to Middle Eastern stakeholders using the native RTL Arabic toggle.
    • Audit research gaps by identifying where data is too thin to support a journey phase.

    Known limitations

    The tool requires structured text input (.md or .txt) and cannot ingest raw audio or video files without prior transcription.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Recently published to Agensi

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    Verified clean 1 month ago

    Listed1 month ago

    Creator

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    Ehab

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