Skill UI

This technique is designed for identifying target Indian actors when the clues are indirect, often pointing to a relative (father, spouse, grandfather) instead of the actor directly. It requires systematic, step-by-step tracking through the family lineage to deduce the identity. Mastering this method allows users to solve complex Bollywood trivia questions by following the family connections.

A comprehensive academic advisor covering the entire university journey. It guides students through major selection, GPA management, graduate school applications (Chinese and Western systems), study abroad planning, and career transition. Methodology includes Socratic questioning and structured decision-making to help users make informed, autonomous academic choices.

A comprehensive guide for configuring and troubleshooting Jenkins when embedded within KubeSphere DevOps. Learn how to manage Jenkins via Configuration as Code (CasC), integrate advanced authentication methods like LDAP and OIDC, customize agent images, and connect to external sources such as GitLab. Covers everything from initial access to backup and recovery.

This comprehensive coach guides users through the entire career lifecycle, from self-assessment and discovering life purpose (Ikigai) to executing advanced job search strategies. It covers writing impactful resumes, mastering salary negotiation, building a strong personal brand, navigating career pivots, and maintaining work-life balance. Ideal for fresh graduates, mid-career professionals, and anyone seeking intentional career direction.

This coach is designed to systematically develop creative thinking and problem-solving skills. It guides users through established methodologies such as Design Thinking, SCAMPER, TRIZ, and Lateral Thinking. It teaches users how to move beyond initial ideas, overcome creative blocks, and apply structured frameworks to generate, refine, and implement novel solutions across various domains. Ideal for students, professionals, and entrepreneurs facing complex challenges.

This technique is designed for advanced academic research, enabling users to search for theses by running two parallel tracks: a Topic Track (focusing on discipline, methodology, and materials) and a People Track (focusing on university, advisor, degree type, and year). By switching between these two methodologies, users can cross-validate results and significantly improve the accuracy and completeness of their literature review.

AnnData is a core Python framework designed for managing and analyzing annotated matrices, essential in single-cell genomics and high-dimensional data science. It provides a standardized structure (X, obs, var) to store experimental measurements alongside rich metadata. Users can read/write various formats (h5ad, zarr), perform subsetting, concatenation, and complex data manipulation, making it indispensable for modern bioinformatics workflows.

This skill generates safety-bounded draft structures and deterministic checks for various clinical, safety, and aggregate research reports (e.g., CSR, aggregate safety tables). It requires verified, de-identified, or synthetic source data, ensuring every generated claim is traceable to a source-fact manifest. IMPORTANT: This skill only creates drafts and checks internal consistency; it cannot diagnose, recommend treatment, or provide medical advice. All outputs are non-clinical and require qualified human review and sign-off.

DeepChem is a comprehensive Python library designed to apply machine learning to chemistry, materials science, and biology. It enables sophisticated molecular property prediction (ADMET, solubility, toxicity), drug discovery, and materials design. The library supports diverse featurization methods (fingerprints, graph representations) and advanced model architectures, including GNNs and transfer learning, making it ideal for analyzing complex chemical datasets like MoleculeNet.

A comprehensive framework for planning, configuring, executing, and analyzing complex Computational Fluid Dynamics (CFD) simulations using pseudospectral methods. It enforces a rigorous, multi-stage workflow, guiding users from defining state equations and boundary conditions to generating resource estimates, running dry-runs, and preparing site-specific MPI jobs. Designed for high-fidelity scientific computing and reproducibility.

Geniml provides a robust framework for running machine learning and statistical workflows over genomic interval sets. It treats coordinates, assemblies, and model artifacts as explicit contracts, enabling users to validate data structures (like BED files) and plan complex runs (such as Region2Vec or scEmbed) locally. The tool emphasizes reproducibility, adherence to biological data standards, and running without unnecessary network calls.

A powerful skill covering geospatial science, including GIS, remote sensing, spatial statistics, and machine learning for Earth observation. It supports processing of various data types (raster, vector, point cloud) from sources like Sentinel and Landsat, and integrates modern cloud-native workflows (STAC, COG). Ideal for environmental monitoring, urban planning, and advanced spatial modeling tasks.