Skill UI

Guides developers through diagnosing and fixing Juicebox API errors, covering authentication, configuration conflicts, resource lookups, and how to collect diagnostic info when integrations fail.

Deploy Juicebox integrations straight to cloud platforms with structured configuration steps, secret management, and health checks so production-ready services can be rolled out consistently and monitored post-deployment.

Walks through installing the Juicebox SDK/CLI, configuring JUICEBOX_API_KEY authentication, and verifying the connection in Node.js or Python, ideal for initial integrations before building AI-powered people searches.

Sets up a Juicebox local development workflow with environment config, mock data, test utilities, and hot reload scripts so integration engineers can validate search flows and iterate quickly without hitting production services.

Guides configuring Juicebox across development, staging, and production with isolated secrets, Kubernetes overlays, and environment-aware clients so teams can bootstrap secure, per-environment deployments and pipeline guards.

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 generating production-ready Kubernetes manifests. It covers essential infrastructure components including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Use this skill when defining complex microservices infrastructure, ensuring robust network connectivity, managing configuration state, or setting up stateful workloads while adhering to Kubernetes best practices (e.g., resource limits, health checks).

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.

Advanced Kling AI camera control guides cinematographers through planning, selecting, and configuring pan, tilt, zoom, and dolly motions so cinematic videos can be generated with precise storytelling intent.

Provides step-by-step instructions to configure structured logging, request tracing, timing metrics, and alerts for Kling AI integrations, helping teams monitor requests and diagnose issues with consistent debug reports.