Skill UI

Generates professional, publication-ready clinical decision support (CDS) documents for pharmaceutical and clinical research settings. Capabilities include biomarker-stratified patient cohort analyses, evidence-based treatment recommendation reports (with GRADE grading), and advanced statistical analyses (e.g., hazard ratios, survival curves). Outputs are optimized for regulatory submissions and medical guidelines.

This skill performs comprehensive Exploratory Data Analysis (EDA) on specialized scientific datasets. It supports over 200 file formats across diverse domains, including chemistry (PDB), bioinformatics (FASTA, BAM), microscopy (TIFF), and spectroscopy. The system automatically detects the file type, assesses data quality and integrity, and generates detailed, format-specific markdown reports, providing deep insights and recommending downstream analyses.

This skill proactively detects and reports comprehensive system resources, including CPU cores, GPU types (NVIDIA, AMD, Apple Silicon), available memory, and disk space. It generates a detailed JSON report with strategic, context-aware recommendations for optimizing scientific computing workflows, such as recommending parallel processing libraries (Dask, joblib), out-of-core methods (Zarr), or appropriate GPU acceleration backends (Metal, ROCm). Use this before any large-scale data analysis, model training, or computationally intensive simulation to make informed architectural decisions.

This skill provides comprehensive integration with the LabArchives Electronic Lab Notebook (ELN) platform via its REST API. It allows users to programmatically manage notebooks, retrieve user data, automate entry creation, upload diverse attachments (PDF, CSV, images), perform full notebook backups, and generate detailed usage reports. Ideal for automating scientific workflows and integrating ELN data with tools like Jupyter or REDCap.

This advanced skill generates comprehensive, professional-grade market research reports (50+ pages) modeled after top consulting firm deliverables (McKinsey, BCG). It supports deep data analysis using research-lookup, multi-framework strategic assessment (PESTLE, SWOT, Porter's Five Forces), and extensive visual generation using scientific schematics and image generation tools. The output is in professional LaTeX format, compiled to PDF, suitable for strategic planning, investment decisions, and thought leadership.

A comprehensive guide and toolkit for systematic statistical analysis, covering the entire research lifecycle. Features include test selection guidance (t-test, ANOVA, regression), rigorous assumption checking (normality, homogeneity), power analysis, effect size calculation, and generation of professional, APA-formatted academic reports. Essential for academic researchers and data scientists.

This skill provides a comprehensive workflow for generating professional and structured financial reports. It guides the user through planning, writing, and editing various sections, and can also generate necessary chart configurations using visual tools. Ideal for condensing complex financial analysis into clear, actionable reports.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.

This skill guides users on automating fundraising workflow analysis using local data methods. Since Finta lacks a public API, it teaches how to export pipeline data (CSV) and use Python (Pandas) scripts to calculate key funnel metrics, conversion rates, and generate detailed weekly reports for investor tracking and reporting.

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.

This skill performs a structured Systematic Literature Review (SLR) by analyzing multiple academic papers on a given topic. It searches academic databases (like arXiv), extracts structured metadata (research questions, methodologies, key findings, limitations) from the entire set of papers, and synthesizes overarching themes and research trends. It is ideal for creating comprehensive literature surveys, annotated bibliographies, and cross-paper comparisons, outputting reports in professional citation formats like APA, IEEE, or BibTeX.

Generate data analysis reports by exploring database tables, running multidimensional SQL queries, performing deep attribution analysis, and outputting dynamic HTML visual reports with ECharts for business, trend, and KPI scenarios.