Skill UI

scikit-bio is a comprehensive Python library designed for advanced bioinformatics and biological data analysis. It provides robust tools for sequence manipulation (DNA, RNA, Protein), performing sophisticated sequence alignments (global, local, multiple), constructing and analyzing phylogenetic trees, and conducting complex ecological statistics. Key features include calculating diversity metrics (alpha/beta), running ordination analyses (PCoA), and handling diverse biological file formats (FASTA, Newick, BIOM). Ideal for researchers in microbiology, genomics, and ecology.

Vaex is a high-performance Python library designed for lazy, out-of-core DataFrames. It enables processing and visualization of massive tabular datasets (billions of rows) that exceed available RAM. Users can perform fast statistics, aggregations, and build robust ML pipelines on big data files (CSV, Parquet, HDF5) without relying on distributed clusters, making it ideal for single-machine, large-scale analytics.

The AI Dev Jobs MCP provides AI agents with real-time access to a massive index of AI and Machine Learning job listings across hundreds of companies. Users can search by role, location, or company, retrieve full job details, analyze salary ranges, list hiring companies, and get aggregate market statistics. It is essential for advanced job searching, talent matching, and labor market analysis workflows.

This utility collects comprehensive diagnostic evidence for Algolia support tickets or internal troubleshooting. It gathers index statistics, API key access control lists (ACLs), recent query logs, and network diagnostic information using API endpoints and shell scripting. Use it when diagnosing search performance or unexpected service behavior.

A comprehensive tool designed to collect all diagnostic evidence for troubleshooting failed or problematic Apify Actor runs. This bundle gathers run metadata, detailed logs, dataset samples, usage statistics, and environment information, making it essential for preparing detailed support tickets or performing in-depth debugging.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.

Generates a complete, self-contained HTML dashboard for analytics, admin, or control panel views. It structures the layout with a fixed sidebar, top navigation, KPI cards, and detailed charts (using inline SVG). Ideal when the brief requires visualizing key metrics, performance over time, or operational statistics.

Provides real-time, non-AI estimated statistics on token consumption and potential cost savings for the current session. This skill reads directly from the session log, offering transparent metrics like rule overhead and net savings, allowing users to accurately gauge efficiency and cost impact of complex AI operations.

This tool provides a read-only view over per-turn decision logs, aggregating data on skill co-occurrence patterns, meta-skill usage statistics, and router fixtures. It returns a structured JSON summary, making it ideal for analyzing historical skill usage, optimizing workflows, and feeding insights into downstream LLM models.

This skill scrapes Substack Notes analytics by extracting key engagement metrics (likes, comments, restacks) and content details. It overcomes the lack of a public API, allowing users to analyze performance data from a specified date range into a highly formatted, actionable Excel spreadsheet. The output includes detailed per-note data, total engagement calculations, and summary statistics.

A comprehensive team management dashboard designed for operational oversight. It features three main tabs: Members, Team Details, and Activity Log. The dashboard provides key performance indicator (KPI) statistics, role distribution charts, online presence tracking, and activity logs. It supports data export to CSV, making it an ideal tool for monitoring team performance and operations.