research-review

Use this research review tool to generate a deep critical review of your work by coordinating multi-round external LLM feedback through Codex MCP, preserving full traceability of all criticisms and responses.

Yes, iterative refinement through multi-round critical reviewing detects logical gaps, identifies missing experiments, and assesses narrative weaknesses until you converge on solid claims and supporting evidence.

A claims matrix is an actionable deliverable mapping your experimental results to specific claims, showing exactly what evidence supports each conclusion and highlighting where additional experiments are needed.

This Skill generates venue-level critique by identifying narrative weaknesses, logical gaps, and missing experiments, then proposes targeted improvements to meet NeurIPS or ICML submission standards.

No external dependencies are required, but the Skill leverages Codex MCP to coordinate multi-round external LLM feedback, ensuring comprehensive context briefing and self-contained documentation of all criticisms.

You receive an experiment plan, a paper outline, and a results-to-claims matrix, providing concrete improvements and a minimal, highest-impact experiment package to strengthen your research.