Skill UI

Provides comprehensive tools for managing enriched lead data in compliance with global privacy regulations like GDPR and CCPA. This skill guides practitioners through data classification (identifying PII sensitivity), input validation, deduplication, implementing data retention metadata, and anonymizing sensitive fields for secure reporting and analytics.

This skill provides a comprehensive guide on implementing robust data governance patterns within Delta Lake, leveraging Unity Catalog. It covers essential compliance topics such as data classification tagging, handling GDPR 'right-to-deletion' requests for PII, and establishing automated data retention policies. Use this when designing secure, compliant, and auditable data pipelines in a data lake environment.

A comprehensive framework for managing availability and reliability incidents (outages, degradations, failed deploys). It guides the entire lifecycle, from automated severity classification (SEV1-SEV4) and timeline reconstruction to multi-team coordination and structured post-incident review (PIR). Essential for SRE and DevOps teams leading major incident responses.

This skill provides a comprehensive, multi-phase guided workflow for professional incident response management. Following modern SRE principles and the Incident Command System (ICS), it guides users from initial detection and triage (P0-P3 classification) through deep investigation (observability, debugging, security checks), culminating in structured resolution and post-mortem analysis. Use this when managing critical system outages or performance degradation.

This skill provides comprehensive guidelines for handling sensitive people datasets from Juicebox AI. It covers critical data classification (PII, search results), required technical controls (AES-256 encryption, field-level masking), and adherence to international privacy regulations like GDPR and CCPA. It ensures users implement lawful data processing, proper consent tracking, and secure data export mechanisms.

This comprehensive guide defines the secure data lifecycle management for OpenEvidence, an AI-powered platform for clinical evidence synthesis. It outlines data classification (e.g., clinical queries, patient responses), strict adherence to global regulations (HIPAA, GDPR, FDA), and technical requirements for handling Protected Health Information (PHI). Key processes include PHI de-identification, data validation, secure API integration, and compliant data export, ensuring the system maintains the highest level of patient privacy and regulatory compliance.

This guide outlines the strict multi-environment setup for the OpenEvidence clinical AI system, crucial for maintaining HIPAA compliance. It details how to configure and validate separate environments (Development, Staging, Production) to manage different data classifications—synthetic, de-identified, and full PHI. The process emphasizes environment validation checks, BAA adherence, and robust audit logging to prevent regulatory violations and PHI exposure.

This skill provides comprehensive methods for implementing intelligent model routing using the OpenRouter API. It allows developers to dynamically select the optimal Large Language Model (LLM) based on specific criteria: task complexity (e.g., classification, code generation), required capability (e.g., deep reasoning vs. simple Q&A), cost budget, and latency tolerance. Implement cost-aware, task-based, and complexity-based routing to maximize system efficiency and minimize operational expenses when building multi-model applications.

This skill helps users create a new Git branch that strictly adheres to established project naming conventions (e.g., Sentry standards). It automatically determines the appropriate branch type (such as `feat`, `fix`, `chore`, etc.) and generates a descriptive, standardized branch name by analyzing the provided work description or local code differences (diff). It guides the entire branching workflow, ensuring proper lineage and classification for code submissions.

This solution guides the implementation of a phishing reporting button within email clients, creating a critical feedback loop for security teams. By integrating with SOAR platforms, reported suspicious emails undergo automated triage, including IOC extraction, sandbox analysis, and threat intelligence checks. The workflow enables rapid classification, automated remediation (e.g., mass retraction, sender blocking), and provides detailed feedback to the user reporter, significantly enhancing overall security posture.

This guide details the process of developing precise YARA and YARA-X rules for advanced malware detection. It teaches analysts how to analyze unpacked malware artifacts—such as unique strings, byte sequences, and PE header traits—to extract stable detection anchors. This skill is crucial for threat hunting, malware family classification, and automating the creation of robust security signatures while aggressively minimizing false positives.

A comprehensive security tool designed to detect prompt injection attacks targeting Large Language Models (LLMs). It employs a multi-layered approach combining regex signature matching, structural anomaly scoring, and advanced DeBERTa transformer classification. It is essential for securing input validation layers in chatbots, AI agents, and RAG pipelines, and for retrospectively auditing LLM interaction logs against OWASP standards.