rebuttal

To structure peer-review feedback into a formal rebuttal, the skill atomizes each reviewer concern into individual items, maps them to wiki claims, and generates both plain-text and rich-text responses with traceable evidence.

Claim-mapping verifies evidence by linking each atomized reviewer concern directly to supporting wiki claims, ensuring that all rebuttals rely on traceable wiki experiments rather than unsupported assertions.

The best way to anticipate reviewer questions is to run a Review LLM stress-test, which simulates reviewer scoring and queries against your mapped claims to identify gaps before generating the final rebuttal.

Yes, you can generate both plain-text and rich-text rebuttals from reviewer comments by feeding the review text and wiki context into the end-to-end workflow, which outputs both formats simultaneously.

You need to provide the reviewer feedback text and the relevant wiki context to run the automated rebuttal workflow, as the system requires these inputs to map concerns to evidence and generate responses.

Safety checks prevent fabrication by verifying that every mapped claim has traceable supporting evidence from the wiki context, ensuring the generated rebuttals avoid over-promising or incomplete coverage of reviewer concerns.