Skill UI

This guide provides a comprehensive workflow for developing and testing with Persona's identity verification services in a controlled sandbox environment. It details setting up local environment variables, tunneling webhooks using ngrok, creating test inquiries via API, and mocking API responses for robust unit and integration testing.

Optimizes the performance of the Persona API for identity verification workflows. This guide demonstrates best practices such as implementing batch inquiry creation, caching verification results, and managing parallel polling to ensure efficient and reliable data retrieval when dealing with large volumes of identity data.

A comprehensive checklist and guide for deploying Persona identity verification features into a production environment. It covers prerequisites, API integration steps using Python examples, error handling (401, 429, 404), and critical post-deployment tasks like compliance review, webhook setup, and monitoring for secure, scalable identity solutions.

This skill provides a comprehensive guide on managing rate limits when interacting with the Persona API. It implements robust strategies such as exponential backoff and request queuing to ensure stable and reliable identity verification workflows, effectively handling 429 Rate Limit errors in Python.

A comprehensive reference guide and implementation example for integrating Persona into your Know Your Customer (KYC) service flow. This skill covers the service architecture, inquiry lifecycle management, webhook-driven processing, and standard compliance patterns. Use it when building identity verification systems that require robust, multi-step verification processes using the Persona API.

A comprehensive TypeScript client wrapper for interacting with the Persona API. It simplifies identity verification workflows by providing methods for creating, retrieving, and paginating inquiries and verifications. Key features include implementing the Singleton design pattern, guaranteeing type-safe responses, and an advanced error classifier that intelligently handles retry logic (e.g., distinguishing between rate limits and invalid keys). Ideal for integrating reliable, production-grade KYC flows into applications.

This guide details best practices for securing Persona identity verification implementations. It covers critical security measures such as API key rotation, webhook HMAC verification, encrypting Personally Identifiable Information (PII) at rest, and maintaining comprehensive audit logs. Use this when integrating Persona into a production system requiring high data security and compliance.

This skill provides methods for upgrading Persona API versions and managing breaking changes within identity verification workflows. Using Python, it demonstrates how to interact with the Persona API, ensuring data compatibility and seamless migration when API standards change. It is essential for developers building SaaS applications that require robust KYC and identity checking capabilities.

This guide demonstrates how to set up a secure and robust webhook endpoint to process identity verification events from Persona. It covers essential security practices, including HMAC signature verification using TypeScript and Express, handling critical KYC lifecycle events (like inquiry approval/declination), and implementing idempotent processing to prevent data duplication. Ideal for integrating third-party identity services into a web application.

A comprehensive guide for handling sensitive Personally Identifiable Information (PII) within Salesforce. This pattern implements critical compliance measures, including GDPR and CCPA requirements, data subject access requests (DSAR), the Right to Erasure, and field-level encryption using Salesforce Shield. Use this when developing features that interact with regulated user data.

This skill provides a structured framework for capturing, organizing, and maintaining persistent facts about the user. By segmenting facts into topic-specific files (e.g., coding preferences, tech stack, goals), it creates a reliable, objective knowledge base. This structure is crucial for AI agents and applications to achieve highly personalized interactions and provide accurate context.

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.