Skill UI

This skill generates a structured, section-by-section outline for academic papers. By synthesizing diverse inputs—including research narratives, auto-review conclusions, and experiment results—it guides researchers in structuring their findings into a cohesive draft plan. It also supports optional style references to align the outline's section structure with specific target venues or published works.

A powerful tool for conducting comprehensive academic research and literature reviews. It searches, analyzes, and synthesizes key ideas from multiple sources, including local PDF libraries (Zotero, Obsidian), the general web, and specialized academic databases (Semantic Scholar, DeepXiv). Use this when you need to map the historical context, identify related works, or understand complex concepts across multiple studies on a given topic.

This skill automates the entire academic research lifecycle. It orchestrates stages from broad idea discovery and literature review to experimental implementation, rigorous cross-model review, and finally, generating a polished research paper PDF. Ideal for researchers needing an end-to-end, autonomous workflow for academic publication preparation.

Get a deep, multi-round, and adversarial critical review of your research work, ideas, or drafts. This skill simulates the perspective of a senior ML reviewer (e.g., NeurIPS/ICML level) using dedicated external backends (Codex or Manual). It is designed to find logical gaps, missing experiments, and narrative weaknesses, providing brutal honesty to elevate your academic contribution.

Deep research and scientific writing assistant that produces LaTeX/BibTeX documents with verified citations for papers, proposals, reports, and reviews, ensuring well-formatted, citation-backed outputs for academic or clinical contexts.

This tool systematically designs and plans ablation studies crucial for advanced research paper submissions. It simulates the role of a rigorous ML reviewer, guiding the process of isolating the specific contributions of each novel component, testing hyperparameter sensitivities, and addressing potential reviewer questions. The workflow ensures that every experiment has a clear hypothesis, maximizing the scientific impact of the research results.

This comprehensive skill bridges the gap between initial idea discovery and final model deployment. It ingests a structured experiment plan (EXPERIMENT_PLAN.md), automatically implements the required code, performs cross-model code review, deploys the setup to GPU, and collects initial quantitative results, allowing for a rapid iteration loop in AI research.

Drafts structured, high-quality grant proposals for major international and national funding agencies (e.g., NSF, KAKENHI, ERC, NSFC). This skill guides the user through the entire grant writing pipeline, transforming validated research ideas and literature into a reviewer-ready application that meets specific structural, narrative, and formatting requirements, significantly increasing funding success rates.

A comprehensive workflow for preparing and maintaining grounded, venue-compliant rebuttals to external academic reviews. It handles strict text-only character limits, manages responses for multiple reviewers (e.g., OpenReview style), supports follow-up rounds, and enforces safety gates against fabrication or overpromising. Ideal for the post-submission phase of research publishing.

A specialized tool for finding peer-reviewed academic papers from major venues like IEEE, ACM, and Springer. Unlike preprint services (e.g., arXiv), this skill provides published literature with citation counts, detailed venue metadata, and a TLDR summary, ensuring access to verified research findings.

A comprehensive collection of agent skills for rigorously auditing digital interfaces and user flows. It evaluates designs against 168 research-backed UX/UI principles, detects common design antipatterns, and provides specialized review for AI-powered interfaces, ensuring adherence to modern best practices.

This skill analyzes accumulated usage logs from a complex research harness (ARIS) to propose structural optimizations. It identifies areas for improvement in SKILL scripts, default parameters, workflow ordering, and reviewer prompts. It is designed to automate the meta-level engineering process, ensuring that system efficiency is continuously improved based on real-world usage data.