What problem does it solve? Emails land in the Junk folder for identifiable structural and content reasons, but senders rarely know which ones apply to their message. This Skill inspects an email draft before sending or analyzes a received .eml file to explain why it was classified as spam, naming each finding with evidence and a concrete correction. ## Core Features & Use Cases - Deterministic rule checks: A bundled Python script (standard library only, Python 3.11+) detects mismatched link text vs. link target, broken List-Unsubscribe headers per RFC 8058, self-referencing thread headers, visible mail-merge artifacts, hidden tracking pixels, domain scatter, and unsourced numbers or superlatives. - Two operating modes: Check your own draft before sending, or parse a received .eml file including the receiving side's verdict from Microsoft headers (CAT, DIR, SCL, BCL, RF), gateway X-Spam-Status, and SPF/DKIM/DMARC results. - Strict guardrails: Fixes defects rather than disguising them, never sends or schedules anything, never fetches links or tracking pixels from inspected messages, and makes no deliverability predictions. - Use Case: A marketer's campaign draft keeps landing in Junk. Run the script on the draft, fix all hard findings (e.g., a visible link text pointing to a different domain than the target), then hand the revised copy to a language-polishing skill. ## Quick Start Ask the assistant to check your email draft for spam-filter triggers with the zustellbarkeit skill, or point it at a saved .eml file to explain why that message was sorted into Junk.
