I already have a flowchart template, some Python snippets, and a set of LLM-based rules. I now need the whole lot wired together inside n8n so that I can run an automated image-and-pattern analysis pipeline that also generates derivative outputs. Scope • Build a clean n8n workflow that follows my existing flowchart. • Embed my provided Python code in the relevant n8n Function nodes (or external scripts, if that is cleaner) so the flow can carry out image and pattern analysis as well as generation tasks. • Respect the LLM rules I will share; they govern how prompts are assembled and how results are post-processed. • Accept PNG files as the primary image input and GeoJSON data as an accompanying spatial layer. • Output the processed results to the destinations specified in the template (currently a local directory and a webhook endpoint). Key expectations – Nodes are clearly named, commented, and logically grouped. – Any environment variables or credentials are surfaced through n8n’s built-in credential manager. – The finished workflow is exportable/importable as a single .json file and runs error-free on the latest n8n build. – A brief hand-off document (or Loom video) shows me how to tweak parameters, update the Python snippets, and extend the LLM rules in future. If you have prior experience melding Python, n8n and large-language-model hooks—especially for image or geospatial data—you’ll hit the ground running.