Skill UI

This skill guides the installation and configuration of the Juicebox PeopleGPT API. It enables developers to access AI-powered people search capabilities across millions of professional profiles. Use this when setting up the initial connection or performing deep people search functionality within your application.

This setup configures a comprehensive local development workflow for integrating with the Juicebox API. It provides a fast feedback loop by allowing developers to mock profile searches and candidate enrichment data. Users can easily toggle between mock mode for rapid iteration and sandbox mode for validating against the real API, ensuring efficient development without consuming live API credits.

This guide details the setup and management of the Juicebox AI platform across multiple isolated environments: Development, Staging, and Production. It ensures workspace isolation, controls data access, and maintains data integrity by differentiating sample datasets (Dev), full datasets with restricted exports (Staging), and full-featured production environments. Follow this workflow to safely deploy and test analyses from development to live production.

Automated guidance for jwt token validator tasks within the Security Fundamentals domain, offering step-by-step help, industry best practices, and production-ready code/configuration generation when you mention jwt token validator or seek secure authentication patterns.

Auto-activating helper that provides step-by-step guidance, best practices, and validation for generating production-ready k6 load test scripts, configs, and benchmarking workflows.

This skill provides step-by-step guidance for creating production-ready Kubernetes manifests. It covers essential resources including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Use this skill when defining complete, robust containerized application architectures, managing network connectivity, stateful workloads, and ensuring adherence to Kubernetes best practices like resource limits and security contexts.

Automated backend guidance for Kafka producer and consumer workflows, offering best practices, code samples, and configuration help for Node.js, Python, Go, databases, caching, messaging, and microservices scenarios.

Automates Kafka stream processor workflows within data pipelines, offering step-by-step guidance, best practices, production-ready code and configurations, and validation to support ETL, data transformation, workflow orchestration, and streaming data processing.

This guide details how to implement webhooks for Kling AI video generation tasks. Instead of inefficiently polling the API status, you can provide a callback URL when submitting a task. When the task completes (whether successful or failed), Kling AI will automatically send a POST request containing the full result to your designated endpoint. This mechanism eliminates polling overhead and is essential for building robust, event-driven video processing pipelines.

Automates KPI dashboard template tasks within enterprise workflows by offering guidance, best-practice patterns, production-ready code/configuration, and validation for compliance, governance, and integration scenarios.

Automates Kubernetes ConfigMap handler tasks by providing step-by-step guidance, best practice patterns, and production-ready code as part of an advanced DevOps workflow focused on Terraform, Helm, and infrastructure-as-code.

This specialized, end-to-end workflow guides the deployment of applications onto Kubernetes clusters. It systematically covers all critical production phases, including Docker container preparation, generating K8s manifests, structuring applications with Helm charts, configuring advanced service meshes (like Istio), enforcing robust security policies (RBAC, NetworkPolicy), and implementing full observability and GitOps pipelines for reliable, scalable deployments.