A hospital worker who writes science explainers in his spare time reviewed 13 Chinese open-source 'de-AI-flavor' writing projects and found most of their rule lists had never been validated: only lieflat-less-ai-tone had tested anything, having five models write 60 pieces each (300 AI articles) and comparing them with 329 human-written public articles (~2.83 million characters), scoring 26 circulating criteria — just 11 held up, while 15 were refuted, including sentence-length uniformity. The author then built a Claude Code skill that tiers rules by evidence and adds hard constraints such as information conservation (every fact in the final draft must exist in the source) and no changing the strength of claims, plus a scanning script that uses only the Python standard library. On the Chinese HC3 dataset, 3.4% of 12,000-plus human answers were flagged as AI-like, versus 18.9% for GPT-3.5 answers and 85% for Claude answers and 83% for 12 science drafts.