Skill UI

This skill helps users maintain a structured and comprehensive record of their daily activities, progress, key decisions, and learning outcomes. It creates a dedicated file for each day using the ISO date format (yyyy-mm-dd), ensuring that all entries are organized chronologically. By adhering to concise, third-person reporting, it helps users build a valuable, reviewable knowledge base of their journey and accomplishments.

This skill guides the systematic process of identifying and verifying candidate football entities (players or clubs). Use it when initial search results suggest a lead, or when the question provides enough detailed constraints (e.g., nationality, era, league) to hypothesize a candidate. The technique involves constructing targeted name-based searches and cross-checking every constraint (birth year, career timeline, etc.) against the found profile to ensure accuracy.

A comprehensive guide to setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines within KubeSphere. This tool integrates with Jenkins, supports both graphical editing and Jenkinsfile-based workflows, and enables automated deployments across multi-cluster environments using ArgoCD for GitOps management. Ideal for managing the full software delivery lifecycle.

WizTelemetry Logging is a core extension component within the KubeSphere Observability Platform, designed for comprehensive log collection, processing, and storage. It supports advanced log collection architectures, including collecting container stdout/stderr logs (via vector-logging) and collecting logs directly from the container filesystem (via logsidecar-injector). Use this guide to understand its configuration, dependencies, and how to query logs using the dedicated API.

A comprehensive Python library for professional astronomy and astrophysics data analysis. It provides essential tools for handling celestial coordinates, physical units, FITS file I/O, time systems, and cosmological calculations, making it vital for scientific research in space science.

Biopython is a comprehensive Python toolkit for computational molecular biology. It supports essential bioinformatics tasks, including sequence manipulation, file parsing (FASTA, GenBank, PDB), phylogenetic analysis, sequence alignment (BLAST/Multiple), and programmatic access to major databases like NCBI using Bio.Entrez. Ideal for building custom, robust bioinformatics pipelines and conducting research in genomics and structural biology.

A specialized toolkit designed for generating and validating research-only artifacts for Clinical Decision Support (CDS) systems. It supports evidence-profile generation, cohort analysis, survival modeling, biomarker evaluation, and governance documentation. IMPORTANT: This skill is strictly for aggregate research and documentation; it must never be used for patient care, diagnosis, or live clinical operations.

deepTools is a comprehensive command-line toolkit designed for processing and analyzing high-throughput sequencing data. It handles core genomics tasks including BAM to bigWig conversion, quality control assessment (PCA, correlation), normalization, and generating publication-quality visualizations (heatmaps, profiles) for ChIP-seq, RNA-seq, and ATAC-seq data. Essential for reproducible genomic research workflows.

A structured system designed to extract the cognitive fingerprint of any text's author. Utilizing the DHDNA framework, it analyzes 12 dimensions—such as Analytical Depth, Emotional Processing, and Strategic Thinking—to provide deep insights into the author's unique reasoning patterns, decision-making style, and overall cognitive signature. Ideal for content analysis, psychological profiling, and understanding underlying thought processes.

DiffDock is a state-of-the-art, diffusion-based deep learning tool for predicting 3D binding poses of drug-like ligands to protein targets. It supports both PDB files and amino acid sequences for proteins, and accepts diverse ligand formats (SMILES, SDF, MOL2). It is designed for structure-based drug discovery, lead optimization, and high-throughput virtual screening campaigns, providing predicted poses and confidence scores.

This skill provides bounded, local exploratory data analysis for diverse scientific data files, including CSV, JSON, HDF5, and image metadata. It rigorously audits data structure, detects missing values, and identifies outliers in a secure, sandboxed environment. It is designed exclusively for pre-modeling data integrity checks and must not be used to infer scientific meaning or make causal claims.

FlowIO is a specialized, low-level library designed for reading, inspecting, and writing Flow Cytometry Standard (FCS) files (versions 2.0, 3.0, and 3.1). It allows users to extract event data as NumPy arrays, inspect detailed metadata (HEADER, TEXT, ANALYSIS), and handle multi-dataset structures. It is crucial for data preparation, format conversion, and foundational metadata management in bioinformatics pipelines, but does not perform complex analysis like gating or compensation.