ProcessOn Mind Map Generator

The Core Problem: Rapid Structuring of Unstructured Information

Engineers and knowledge workers frequently grapple with information overload: a lengthy technical document, minutes from a brainstorming session, a paper to deconstruct, or logical diagrams scattered across images and PDFs. Manually organizing this into a clear, reusable knowledge framework is time-consuming, error-prone, and risks missing critical layers or logical connections. The core demand is to transform information into an editable, understandable mindmap structure that supports subsequent decisions, reports, or learning.

How It Works: From Content Understanding to Structured Output

This skill is not a mere format converter; it is a process of content distillation and logical modeling. Its core capabilities and key steps are:

1. Precise Content Ingestion and Anchoring:

   • Prioritizes processing user-uploaded attachments (e.g., Markdown, PDF, images) via text extraction or recognition to obtain core content.
   • Analyzes material type and user intent to distill a single core theme as the mindmap's root node, ensuring all subsequent content revolves around this focus.

2. Structured Decomposition and Logic Mapping:

   • Applies the MECE (Mutually Exclusive, Collectively Exhaustive) principle to decompose complex information into clear hierarchies. If the source document has a clear table of contents or headings, it preserves the skeleton first, only refining and condensing the content at the deepest level into a one-sentence core insight or a key list.
   • Intelligently identifies various relationships between information (hierarchical, causal, procedural, comparative, etc.) and maps them to corresponding diagram structures. For example:
     • For divergent content, knowledge synthesis, use mind_free (Free Mind Map).
     • For linear processes, outline steps, use mind_right (Logic Chart).
     • For problem root cause analysis, use mind_ishikawa_left (Fishbone Diagram).

3. Content Enhancement and Visual Control:

   • Based on content type, proactively applies bold for key concepts, inline code for technical parameters or commands, and unordered lists for clear enumeration. For topics suited to visual understanding (e.g., processes, classifications), placeholder images can be inserted for illustrative support.
   • Matches different visual themes (e.g., "Minimalist Black & White", "Aurora Dark") based on use case (e.g., technical report, creative brainstorming), specifying the theme name in the output.

4. Pure Markdown Output and Cloud Synchronization:

   • The final output is render-ready Markdown, strictly prohibiting analysis processes or HTML tags. Hierarchy adheres to Markdown specifications (# to ######, switching to lists beyond six levels).
   • Uses a specific script (e.g., processon_mindmap_client.py) to sync the generated Markdown content to the cloud, returning a viewable and editable online link.

Usage Boundaries and Important Considerations

• Input Dependency: Generation quality is highly dependent on the clarity and structural completeness of the input text. For scanned documents or handwritten notes, OCR recognition may be affected by image quality.
• Version Check Mechanism: The skill has a built-in version check process that may perform an online comparison before each use. If a new version is detected, handling the update prompt takes priority, potentially interrupting the current mapping process.
• Output Environment Requirement: The generated Markdown requires the host environment (e.g., a chat client or editor that supports Markdown rendering) to display correctly. The returned online and image links are independent of local rendering.
• Default Structure Selection: When user intent is ambiguous, the skill defaults to the mind_free structure. Explicitly specifying a structure type (e.g., requesting a timeline or an organizational chart) yields a more tailored output.