Skill UI

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 technique is designed for entity recognition and hypothesis generation when identifying Indian or Bollywood actors based on indirect clues. Instead of running broad searches, users should hypothesize a small set of candidate names (1-3) using the provided clues (e.g., awards, family history, controversies). Subsequently, targeted verification searches must be executed for each constraint against the candidate names to confirm the correct identity.

A comprehensive guide for configuring ArgoCD within KubeSphere DevOps. It details implementing GitOps continuous deployment, covering application management, multi-cluster setup, SSO integration via Dex, and best practices for managing application state synchronization from Git repositories. Learn the differences between direct ArgoCD and KubeSphere's tenant-friendly GitOps application creation.

This skill teaches the methodology of forming concrete hypotheses about specific entities (such as film titles, director names, or historical events) when presented with constraints. Instead of performing broad, generic searches, users should leverage their world knowledge to narrow down possibilities, turning a wide query into a precise, verifiable search query. This is particularly effective for academic or historical research.

This technique is designed to identify South Asian (Indian/Bollywood) films when the question provides insufficient information to name the movie directly. Instead, it requires chaining together the biographical details of the actor or director—such as birth year, debut era, family relations, or awards—to first uniquely identify the person, and then use that identity to locate the target film and verify all constraints.

WizTelemetry Logging is a comprehensive extension component within the KubeSphere Observability Platform. It provides robust capabilities for collecting, processing, and storing logs from container environments (stdout/stderr) and local disk files. Designed for system monitoring, it integrates with OpenSearch and supports advanced features like log query APIs, making it essential for maintaining system visibility and debugging in Kubernetes clusters.

A comprehensive mentor for graduate students and early-career researchers, guiding the entire research lifecycle. This skill covers formulating rigorous research questions, designing studies (quantitative, qualitative, mixed-methods), conducting literature reviews, analyzing data, and writing publishable papers. It emphasizes methodological rigor, critical thinking, and ethical considerations.

AnnData supplies a Python-native container for annotated matrices, letting researchers store measurements alongside rich metadata, read/write h5ad/zarr/loom/10X files, and integrate with the scverse tools when building single-cell or related data workflows.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

Automate Benchling lab data workflows via the Python SDK or REST API: manage registry entities (DNA, proteins), inventory, ELN entries, workflows, and Data Warehouse queries for syncing and analytics.

Evolver is a sophisticated self-evolution engine designed for advanced AI agents. It analyzes runtime logs and operational history to autonomously identify performance bottlenecks, failures, and opportunities for improvement. It applies protocol-constrained evolution, communicating exclusively via a local Proxy Mailbox to interact with the EvoMap Hub for asset submission and task claiming.

Cirq is Google Quantum AI’s Python framework for building, simulating, and deploying noise-aware quantum circuits on both simulators and real hardware, complete with experiment templates, transformation utilities, and hardware-specific toolkits.