Skill UI

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.

This skill provides programmatic access to the Cancer Dependency Map (DepMap), a comprehensive resource characterizing genetic dependencies in cancer cell lines. Use it to identify genes essential for specific cancer types, discover synthetic lethal interactions, and validate potential oncology drug targets using CRISPR and RNAi data.

This advanced tool utilizes the Digital Human DNA (DHDNA) framework to extract a comprehensive cognitive signature from any piece of text. It analyzes the author's unique thinking patterns, reasoning methods, decision-making styles, and core values across 12 detailed dimensions (e.g., Analytical Depth, Ethical Reasoning, Emotional Processing). It provides a structured profile, revealing the topology of the mind behind the writing, without judgment.

Latch is a robust Python framework designed for building, deploying, and managing complex, scalable bioinformatics workflows as serverless pipelines. It supports integrating multiple analysis tools (like Nextflow/Snakemake) and handles cloud data management (LatchFile/LatchDir) and detailed resource configuration (GPU, CPU, memory). Ideal for running large-scale analyses such as RNA-seq, AlphaFold, and single-cell analysis in a reproducible, production-ready environment.

Modal is a powerful serverless cloud platform designed specifically for running AI and Machine Learning workloads. It allows users to deploy, serve, and scale complex models (like LLMs) without managing infrastructure. Key features include on-demand access to high-end GPUs (A100, H100), serverless function scaling, persistent storage, and the ability to define entire infrastructure using pure Python code, eliminating the need for separate YAML or Dockerfiles.

This skill automates the process of transforming high-level product specifications, requirements, and design documents into a detailed, actionable, and executable task plan. It follows a multi-stage workflow, including context loading, parallel research, and two rigorous review gates (Task Plan Review and Task-Graph Sanity Review). The output provides properly sized (1-3 hours), logically sequenced tasks, ensuring every requirement is covered and dependencies are explicitly managed, dramatically improving project planning accuracy.

This guide provides comprehensive instructions for setting up monitoring and observability for Qdrant deployments. It helps users track critical performance metrics, check system health, and diagnose complex production issues such as memory growth or slow query requests. Use cases include setting up Prometheus, Grafana, and implementing robust health checks.

This guide identifies and corrects the most common programming mistakes and anti-patterns when integrating with Adobe APIs. It covers critical topics such as migrating from deprecated JWT to OAuth, implementing token caching, handling asynchronous job polling, and secure file uploads. Essential reading for building robust, production-ready Adobe integrations.

A comprehensive guide to setting up a full-cycle local Web3 development workflow. This skill utilizes Alchemy as the RPC provider, Hardhat for local unit testing, and integrates Mainnet forking and Sepolia testnet deployment. It covers initializing the project, configuring smart contract interactions, and implementing advanced testing patterns.

A comprehensive guide for migrating code from deprecated libraries like alchemy-web3 to the modern alchemy-sdk (v3). This resource details handling breaking changes, understanding new API namespaces (core, nft, ws), and updating web3 interactions to ensure seamless blockchain functionality.

A comprehensive guide to setting up robust CI/CD pipelines for Algolia indexing. This includes running unit and integration tests against live indices, validating index settings against defined configurations, and implementing automated reindexing upon deployment (e.g., triggering on version tags). It ensures the stability and data consistency of your search service.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.