Skill UI

This skill provides comprehensive patterns for handling sensitive data when integrating with Mistral AI. It covers PII redaction (emails, phone numbers, etc.), implementing data retention policies, and enforcing compliance with global privacy regulations like GDPR and CCPA. Key features include safe API wrappers, dataset sanitization for fine-tuning, and conversation history management with TTL and user erasure rights.

A comprehensive toolkit designed to manage the complex data flow associated with Perplexity search workflows. This skill implements critical security measures, including PII sanitization in user queries, validates external citations for accuracy, optimizes performance via result caching with freshness policies, and manages conversation context to maintain coherence and reliability. Essential for building robust, compliance-aware AI applications.

Provides comprehensive security best practices for integrating the Perplexity API. This guide covers secure API key management using environment variables, critical query sanitization (stripping PII like emails, phone numbers, and credit cards), and implementing domain filters for compliance. Essential for developers building production applications that handle sensitive user data.

This guide provides comprehensive strategies for implementing privacy-safe analytics using PostHog. It addresses critical compliance requirements like GDPR and CCPA, covering explicit consent management (opt-in/opt-out), real-time property sanitization (stripping PII like IP or tokens), and methods for handling Data Subject Access Requests (SAR) and the Right to Erasure. Use this to build highly compliant and privacy-preserving analytics pipelines.

Provides comprehensive security guidelines for integrating Anthropic Claude API. Covers essential best practices including secure API key management (rotation, isolation), defending against prompt injection attacks by separating system instructions from user input, robust input sanitization, and output validation to prevent accidental data leakage. Essential for building production-grade, secure AI applications.

A comprehensive guide to implementing secure integration practices for Linktree APIs. Covers critical topics including secure secret management, robust webhook signature verification (HMAC-SHA256), input sanitization against XSS, and data redaction of Personally Identifiable Information (PII). Essential for developing resilient and secure third-party integrations.

This skill guides the process of converting complex, repetitive, and often private operational workflows originating in the Hermes environment into safe, reusable, and public skills for the ECC workflow layer. It focuses heavily on data sanitization, requiring users to strip out all sensitive information such as absolute paths, private credentials, personal identifiers, and proprietary data before publication. Use this when an internal 'operator' routine needs to become a standardized, shareable business asset.

This skill deploys Remote Browser Isolation (RBI) as a core component of a Zero Trust security architecture. It implements sophisticated isolation policies utilizing URL categorization, risk-based routing, and Content Disarming and Reconstruction (CDR) for file sanitization. It provides robust data loss prevention controls, effectively mitigating zero-day exploits, phishing, and data exfiltration across isolated web sessions, and integrating with SWG and ZTNA platforms.

This skill enforces a rigorous data validation protocol by establishing hard boundaries at every point data enters the system (API requests, user input, environment variables, webhooks, etc.). It mandates a four-phase process: detection, trust classification, mandatory filtering (schema validation, sanitization, type checking), and final verification. This prevents untrusted data from reaching core business logic, mitigating risks like SQL injection and XSS.

This skill guides the process of converting complex, repetitive, and often private operational workflows originating in the Hermes environment into safe, reusable, and public skills for the ECC workflow layer. It focuses heavily on data sanitization, requiring users to strip out all sensitive information such as absolute paths, private credentials, personal identifiers, and proprietary data before publication. Use this when an internal 'operator' routine needs to become a standardized, shareable business asset.