It may add purportedly “real data or results” that were not in the source copy
Source references: 1When source material is sparse, the Skill directs the model to proactively add “real data or results,” cases, and counterintuitive conclusions, but does not require evidence from the user, placeholder labels, or verification. Generated figures, experiences, or conclusions could therefore be presented as facts.
If published without review, this could create false performance claims, misleading cases, or unsupported commercial promotion, affecting audience decisions and exposing the user to reputational, platform-enforcement, or advertising-compliance consequences.
When the source lacks material, the live instructions tell the AI to “actively supplement” it, list “real data or results” as eligible additions, and then generate hooks from those additions. The workflow does not require requesting evidence, verifying claims, or marking them as placeholders. The model could therefore present unverified numbers, personal experiences, or cases as facts, misleading viewers and harming the user’s credibility. Users can ask the author to restrict output to source text or user-confirmed facts and clearly label anything else as a hypothesis or placeholder pending approval. This supports a plausible risk, not proof that fabrication occurred.
#### 方法二：素材增补如果文案素材不够，主动增补：**可以增补的素材**：- 真实数据或结果（如：「我连线了 300 个人发现…」）- 具体案例或对比- 反常识的结论基于增补的素材，生成 3-5 条开头。