brand-guideline-extractionlisted

# Brand Guideline Extraction Reverse-engineer a brand's visual and verbal DNA from existing design assets, and codify it so a human designer or a generative pipeline can reproduce the look and feel. The core insight: a brand guideline extracted from N assets is only as good as (a) how representative the corpus is, and (b) how rigorously you separate *rules* (what the brand always does) from *variations* (what it sometimes does) and *exceptions* (one-offs). The whole workflow is built around that separation. Work through five phases in order. Phases 2's subagent fan-out is the expensive step; everything else is cheap. ## Phase 1 — Asset inventory Locate the assets. Usually the user provides a folder; if they name external sources (Meta Ad Library, a website, social profiles) gather what's accessible first, but never block on missing sources — work with what exists and note coverage gaps in the final document. Build `work/inventory.json`, one entry per asset: ```json {"file": "path", "kind": "image|video|source_file", "width": 1080, "height": 1920, "format_class": "square|portrait|story|landscape", "content_type": "best guess: product-ad|announcement|seasonal|job-ad|...", "campaign_hint": "from filename/folder", "date_hint": "from filename if present"} ``` Folder names and filenames are metadata — Turkish, German, or any-language campaign names, dimensions in filenames ("1080x1920px"), dates, and platform markers ("META", "Google", "Pmax") all go into the inventory. K