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 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.

This skill generates and ranks publishable research ideas based on a broad direction provided by the user. It systematically validates concepts through pilot experiments and model reviews, optimizing for GPU efficiency and novelty. Suitable for brainstorming sessions, academic exploration, and identifying workable research paths.

Orchestrate a complete idea discovery workflow that goes from a broad research direction to validated, pilot-tested ideas. Chains literature survey, idea generation, novelty checking, critical review, and research refinement into a single automated pipeline, producing a ranked idea report, a final proposal, and an experiment plan.

Orchestrates a robotics-specific literature survey, novel idea generation, novelty check, and critical review for embodied AI and robotics research. Helps turn a broad robotics direction (manipulation, locomotion, navigation, drones, humanoids) into benchmark-grounded, simulation-first pilot ideas. Use when you need robotics idea discovery or sim2real topic selection.

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.