Review ML research papers through multi-round adversarial critiques.
npx skills add https://github.com/dogekiki/SP-test --skill research-review-dogekiki
Please help me install this Agent Skill. Skill: research-review Source: https://github.com/dogekiki/SP-test/tree/main/.trae/skills/research-review Command: npx skills add https://github.com/dogekiki/SP-test --skill research-review-dogekiki
This skill addresses the lack of rigorous, critical feedback in the research process by providing an automated, multi-round adversarial review system that identifies logical gaps, methodological weaknesses, and narrative flaws.
Invoke the research-review skill to perform a deep critical audit of the research project located in the current directory.
High-intent search queries and answers about installing and using this skill.
You can perform a deep critical audit of ML research papers, experimental results, and project narratives to identify logical gaps, methodological weaknesses, and narrative flaws for strengthening contributions.
Yes, the system supports multi-round conversations through external reviewer backends like Codex or manual review interfaces to refine research, design minimal experiments, and structure paper outlines.
Yes, executing a deep-audit analysis requires integration with MCP-based reviewer tools and adherence to strict review tracing and documentation policies.
The best way is to use an automated, multi-round adversarial review system that applies ultra-reasoning to audit experimental results and project narratives, highlighting why your work might be rejected and how to fix it.
GRIND-Lab-CoreReview research papers and experimental results through iterative dialogue.
loujcIdentify logical gaps and narrative weaknesses in research via multi-round Codex MCP review.
czh-ee-2023Verify mathematical proofs in LaTeX files via external reviewer backends.
olaTechieCoordinate five reviewer personas to generate reports and an editorial decision.
