Skill UI

Provides a structured workflow for defining agent-agnostic subagents with isolated context, covering purpose definition, metadata configuration, prompt writing, and common specialist patterns for verification, debugging, security auditing, and review.

Assesses AI/ML models and datasets for ethical risks, fairness concerns, and bias, generating reports and mitigation guidance via the ai-ethics-validator plugin whenever a user requests an ethics review or bias detection.

Use Nano Banana 2 plus Gemini 3.1 Pro Preview to iteratively craft publication-quality scientific schematics—neural nets, pathways, or system diagrams—matching document-type quality thresholds.

Audits prose for 21 AI-writing patterns, swaps flagged terms via a 43-entry table, rewrites the passage with human phrasing, and runs a second-pass review so content sounds natural before publishing.

Python toolkit that queries the arXiv Atom API to search papers by keywords, authors, categories, or IDs, filter results, download PDFs, and emit structured JSON for AI/ML literature reviews and monitoring.

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

This skill implements an autonomous, multi-round review loop for refining complex research or codebases. It iteratively reviews content using specified external backends (e.g., Codex, manual review), implements necessary fixes based on feedback, and re-reviews until the positive assessment criteria are met or the maximum round limit is reached. It manages state persistence and allows for configurable difficulty levels, making it ideal for projects requiring rigorous, self-correcting quality assurance.

This skill automates the research refinement process by entering an autonomous, multi-round review loop. It leverages any OpenAI-compatible LLM to score, critique, and guide necessary fixes until the work meets a high, expert-defined standard. It manages state persistence across rounds, simulating the rigorous process of professional peer review.

This skill implements an autonomous, multi-round research review loop, simulating the rigorous peer-review process of top-tier conferences. It iteratively reviews, identifies weaknesses, suggests fixes, and re-reviews a research document using the MiniMax API. The system manages state persistence across multiple rounds, ensuring continuous improvement until a 'ready' assessment is achieved or the maximum round limit is reached. Ideal for academic writing, thesis refinement, or comprehensive project documentation.

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.