Skill UI

A comprehensive guide detailing security best practices for integrating with Instantly.ai. Learn how to implement least-privilege API scopes (e.g., campaigns:read), manage secrets using environment variables or dedicated secret managers, execute secure API key rotations, and validate incoming webhooks using custom headers. This skill is essential for developers building robust, secure, and scalable SaaS integrations.

This guide provides comprehensive steps and code examples for migrating Instantly.ai integrations from the deprecated API v1 to the modern REST API v2. It details critical changes, including updating authentication from query-string API keys to Bearer token authorization, standardizing endpoints, and restructuring complex request bodies. Use this skill to ensure your applications remain functional after the v1 deprecation.

This comprehensive guide provides patterns and best practices for implementing robust testing strategies in modern JavaScript and TypeScript applications. It covers setting up test infrastructure, writing unit tests for functions and classes, creating integration tests for APIs, and implementing end-to-end tests for user flows, supporting TDD and CI/CD integration.

This skill acts as an expert architect for scaffolding robust, production-ready applications using TypeScript. It generates complete, structured project templates for various types, including Next.js full-stack apps, React/Vite SPAs, and Node.js APIs. It incorporates modern tooling like pnpm, Vite, and Next.js features, ensuring type safety, proper dependency management, comprehensive testing setups, and adherence to current industry best practices. Use it to accelerate project setup and maintain scalable architecture.

Auto-activating tool that guides technical documentation work by providing jsdoc comment templates, best practices, and validation for APIs, guides, and automation workflows.

Configure automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for Juicebox AI data analysis integrations. This setup utilizes GitHub Actions to run comprehensive unit tests (with mocked datasets) and integration tests (connecting to live APIs) on every PR and merge to main. It ensures that data upload logic, analysis execution, and result parsing workflows are robust and reliable before deployment.

This comprehensive guide addresses common technical and business logic errors encountered when integrating with the Juicebox API. It covers HTTP status codes (e.g., 401, 403, 429), including authentication failures, quota exhaustion, rate limiting, data validation issues, and analysis timeouts. The resource provides detailed causes, specific fixes, and best practices for building reliable and robust integrations into recruiting and research workflows.

This bundle collects comprehensive diagnostic evidence from the Juicebox platform. It gathers API connectivity status, dataset health reports, analysis quota usage, and rate limit information into a single archive. Use it to systematically troubleshoot complex issues such as failed dataset uploads, stalled AI analysis runs, API authentication failures, or quota exhaustion.

This skill provides a comprehensive guide and implementation for deploying a production-ready, containerized integration service using Docker. It connects to the Juicebox API to manage datasets, execute advanced AI-powered analyses, and retrieve structured business insights. The service includes health check mechanisms and supports zero-downtime rolling updates, ensuring reliable real-time data processing and analysis.

A comprehensive runbook detailing incident response procedures for failures within the Juicebox AI platform. It guides technical users through diagnosing and resolving critical issues such as API outages, data corruption, quota exhaustion, and export failures, ensuring minimal downtime for recruiting and talent intelligence workflows.

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 provides comprehensive metrics, instrumentation code (TypeScript), health check logic, and alerting rules for monitoring a SaaS platform like Juicebox. It covers critical metrics such as search latency (p95), data ingestion rates, API error rates, and quota usage, ensuring the maintenance of an optimal user experience and data integrity. Use this to establish robust operational monitoring dashboards and alerts.