Skill UI

Simulates a highly structured, sequential peer-review process using multiple specialized AI agents (e.g., Skeptic, Constraint Guardian, User Advocate). This skill is designed to rigorously stress-test a design, surface hidden assumptions, identify potential failure modes, and enforce non-functional constraints before implementation. It transforms initial concepts into robust, validated specifications by simulating formal critique and achieving consensus via a mandatory Decision Log.

Structured manuscript and grant review guidance that walks reviewers through methodology, statistics, reporting compliance, and constructive feedback for rigorous scientific evaluation across disciplines.

This skill automates the iterative process of improving a written academic paper. It simulates an advanced peer review cycle (Review → Fix → Recompile) using an external LLM (GPT-5.4) over multiple rounds. It fixes theoretical inconsistencies and enhances presentation quality, going beyond simple review to actively refine the manuscript.

This skill executes an autonomous, iterative research review loop, simulating a rigorous peer-review process. It repeatedly uses an external OpenAI-compatible LLM via API calls to score, critique, and identify weaknesses in a given research topic or document. The process continues to refine the content by implementing suggested fixes until the LLM provides a positive assessment or the maximum round limit is reached. Ideal for academic writing, technical documentation, and refining complex proposals.

This skill automates the iterative process of academic paper refinement, simulating the rigorous peer-review cycle of top-tier conferences (e.g., NeurIPS, ICML). It connects with an external LLM (MiniMax) to receive multi-round, critical feedback, tracks state persistence, and guides the user through necessary revisions until the research is deemed submission-ready. Ideal for maximizing the quality of technical writing.

This skill searches for published academic papers from major venues like IEEE, ACM, and Springer via the Semantic Scholar API. It is ideal for finding peer-reviewed journal articles and conference proceedings, providing crucial metadata such as citation counts, venue details, and TLDR summaries. Use it when you need verified, published literature beyond raw arXiv preprints.

Generates structured peer-review quality critiques of academic papers, covering summaries, methodology scrutiny, contribution evaluation, literature positioning, and recommendations across domains whenever a user requests reviews, analyses, or summaries of research publications.

Structured manuscript and grant review guidance that walks reviewers through methodology, statistics, reporting compliance, and constructive feedback for rigorous scientific evaluation across disciplines.

An all-in-one toolkit for deep web research, encompassing web search, content extraction, data enrichment, and multi-source deep reporting. It specializes in academic and scientific sources, prioritizing peer-reviewed papers, preprints, and scholarly databases. Use this skill for comprehensive investigations, literature reviews, or structured data collection from the web.

Drafts self-assessments, peer reviews, and upward feedback using WorkIQ evidence from emails, meetings, and contributions. It helps you write honest, impact-focused performance reviews in your own voice with STAR-based structure.

A comprehensive 12-agent pipeline designed to guide users through the entire academic writing lifecycle. It covers literature search, structural planning, full-text drafting, bilingual abstract generation, simulated peer review, and professional formatting (LaTeX/DOCX/PDF). Features include Style Calibration and Writing Quality Checks to ensure publication-ready quality.

This advanced agent simulates a comprehensive international journal peer review process. It automatically configures 5 independent reviewer personas (EIC, 3 peers, Devil's Advocate) who evaluate the submitted paper from diverse perspectives: methodology, core domain knowledge, cross-disciplinary impact, and fundamental argument challenges. The system outputs a structured Editorial Decision and a detailed Revision Roadmap, supporting full review, re-review, and calibration modes for rigorous academic assessment.