Skill UI

This skill provides an end-to-end, highly reproducible workflow for bulk RNA-seq differential expression analysis. It orchestrates the entire pipeline, guiding users from raw FASTQ reads through Quality Control (QC), trimming, alignment (STAR/Salmon), quantification, generating a gene-level counts matrix, and finally executing differential expression testing (pydeseq2) and pathway enrichment analysis. It is designed for robust, statistically sound, and fully documented results.

The official skill collection for Longbridge Securities, offering over 125 agent skills. It provides comprehensive financial analysis across multiple markets (HK, US, A-share, SG), including real-time quotes, advanced charting, company fundamentals, portfolio management, and options analysis. Supports trilingual interaction (Simplified/Traditional Chinese/English), making it an essential resource for global investors and financial analysts.

A comprehensive tool for performing pathway and gene-set enrichment analysis (GSEA, ORA, ssGSEA, GSVA). Used in computational biology to determine which biological pathways or gene sets are significantly over-represented in a given list of genes or ranked gene data. Supports multiple databases (GO, KEGG, Reactome, MSigDB) and provides tools for result interpretation, visualization, and publication-ready reporting.

A robust API-backed skill for fetching YouTube transcripts, searching video content, browsing channels, and extracting playlists. It functions reliably from any cloud server environment, bypassing limitations found in local tools like yt-dlp or reliance on Google API keys. Ideal for building research corpora, competitive intelligence monitoring, and systematic content analysis.

A comprehensive tool for extracting YouTube transcripts, searching videos, browsing channels, and monitoring new uploads. It supports detailed transcript analysis, content summarization, and playlist management by leveraging the TranscriptAPI. Ideal for researchers, content strategists, and market analysts who need structured data from video content without downloading files.

This skill provides comprehensive intelligence across finance and media. Users can search real-time news, analyze media bias across multiple dimensions, get live stock/crypto data with AI forecasts, price financial options using ML models, and synthesize balanced perspectives on current events. It is ideal for deep research, investment analysis, and media literacy.

This meta-skill is designed for complex decision analysis when a user provides or references structured documents such as contracts, quotes, spreadsheets, or notices. Instead of simple summarization, it extracts key evidence, identifies risks (e.g., renewal risk, payment terms), and structures the output into decision-ready formats, such as 'sign/reject/negotiate' recommendations, evidence tables, or required next actions. It supports input from PDF, DOCX, and XLSX formats.

This meta-skill orchestrates a comprehensive workflow for analyzing PDF documents. It handles complex tasks such as summarizing, cross-document comparison, and targeted Q&A. It ensures findings are traceable by indexing evidence from various sources (full PDFs, pasted excerpts, etc.) and synthesizing structured insights across multiple files.

This meta-skill automates the entire process of transforming raw Excel data into structured business insights. It reads the raw data, performs advanced trend and anomaly analysis, writes the resulting findings to a dedicated 'Insights' sheet, and persists key performance indicators (KPIs) to long-term memory. Ideal for comprehensive business reviews and data-driven decision-making.

A comprehensive guide and set of tools for embedding dynamic Python visualizations (using matplotlib and seaborn) into Power BI reports. It details the structured workflow, including script injection, data handling (dataset DataFrame), and mandatory best practices for generating static PNG output. Ideal for advanced data analysis and complex reporting.

Calculates statistical power and required sample sizes for study planning. It supports both closed-form methods (for standard tests like t-tests, ANOVA) and simulation-based methods (for complex models like mixed models or survival analysis). Use this skill to determine the minimum detectable effect (MDE) or justify sample sizes for grants and IRB protocols before data collection.

This skill processes raw, unstructured materials (e.g., conversations, reports, controversies) into highly structured, reusable casebook entries. It focuses on identifying core facts, defining mechanism chains, and mapping responsibility links while ensuring rigorous data anonymization. It is designed for deep knowledge management and pattern recognition, helping users build a reliable, reusable knowledge base for future professional analysis, rather than generating immediate advice.