Synthesis Inbox Cleanup

A manifest-driven email cleanup engine that scales the same human-curated rules across three account tool stacks on macOS: iCloud / generic IMAP, Microsoft 365 / outlook.com via Mail.app AppleScript, and Gmail via the workspace-mcp Gmail API (with optional native server-side filters).

The engine is deterministic. Email content does not change rules at runtime. When an LLM is invoked — for new-sender categorization or for higher-risk paths like body-reading digests — sanitization defenses run first. The skill ships adversarial test fixtures so prompt-injection regressions surface in CI rather than in production.

v1.6.0 — Impersonation scanning: the taxonomy had no cell for hostile

scripts/scan_impersonation.py (read-only) adds the adversarial pass the disposition taxonomy structurally lacked. Every existing class sorts mail by DESIRABILITY — marketing, newsletter, transactional, keep — so a phishing message is not merely misfiled by this engine, it is invisible to it: a sweep that only files things tidily walks straight past an attack.