Skill UI

This skill provides a comprehensive workflow for querying documented, authoritative public APIs across scientific, environmental, and medical domains (e.g., NASA, PubChem, USGS). It ensures that data retrieval is reproducible, auditable, and traceable by defining explicit endpoints, handling pagination, and verifying data provenance, rather than relying on general knowledge inference.

A robust job queue designed to orchestrate large-scale, multi-seed, and multi-configuration Machine Learning experiments on remote GPU clusters. It handles complex workflows, including wave transitions, dependency chaining (teacher-student), Out-of-Memory (OOM) retries, and stale screen cleanup, ensuring reliable execution for demanding scientific computing tasks.

A comprehensive AI tutor for K-12 students covering Physics, Chemistry, and Biology. This skill employs Socratic Questioning, the 5E Model, and phenomenon-based learning to guide students toward scientific discovery. It adapts to various curricula (US, IB, Chinese) and focuses on building deep, conceptual understanding rather than simply providing answers.

This skill guides the systematic process of conducting comprehensive academic literature reviews. It allows users to search multiple biomedical, scientific, and technical databases (e.g., PubMed, arXiv, Semantic Scholar) to synthesize current knowledge. The output generates professionally formatted reports (Markdown/PDF) with verified citations, and mandatorily integrates AI-generated scientific schematics to enhance visualization and depth, suitable for research papers, meta-analyses, and state-of-the-art reports.

An all-in-one toolkit designed for advanced web tasks, including web searching, content extraction from URLs, batch data enrichment, and deep, comprehensive research reporting. It prioritizes academic and scientific sources for accurate and authoritative results, making it ideal for academic, scientific, and professional data analysis workflows.

A powerful command-line interface (CLI) tool, modeled after kubectl/docker, designed for comprehensive management of GPU compute jobs on the Qizhi platform. It supports job submission (interactive/non-interactive), resource discovery, node availability checking, and batch processing for distributed training and HPC workloads. Ideal for machine learning engineers and data scientists running large-scale scientific computations.

A comprehensive resource hub for ML researchers working in scientific domains such as biology, chemistry, genetics, and medicine. It provides a curated catalog of high-signal datasets, pre-trained models, and interactive demos, streamlining the discovery and use of specialized AI/ML resources beyond general search.

This utility evaluates complex arithmetic expressions using arbitrary-precision decimal mathematics. It is specifically designed for scientific, financial, or engineering calculations where standard floating-point arithmetic causes unacceptable rounding errors (e.g., 0.1 + 0.2). Supports addition, subtraction, multiplication, division, exponents, and parentheses.

A comprehensive server providing research-grade access to arXiv. Users can search papers by topic, author, or date, download full texts, and analyze the scientific landscape by traversing citation graphs. It also supports local semantic search over downloaded documents and enables monitoring for new papers with real-time alerts. Ideal for academic research and literature review.

This skill automates the complex compilation process required for LaTeX documents, specifically implementing the sequence: xelatex -> bibtex -> xelatex -> xelatex. It takes pre-assembled content (such as abstracts and body text) and compiles them into a cohesive, structured academic paper. Ideal for the final steps of scientific and technical writing.

This utility serves as a stub experiment generator, primarily for demonstration purposes within meta-paper writing workflows. Given a specific topic phrase, it deterministically produces a consistent CSV file containing simulated experimental results. This function allows users to prototype and test complex paper structures and formats without needing access to real scientific data.

This tool guides users through the critical process of designing experiments *before* data collection. It helps select appropriate structures—such as factorial, crossover, or blocked designs—to ensure that collected data is interpretable and allows for robust causal inference. Use it when planning scientific studies, assigning subjects, or optimizing responses across multiple factors.