Skill UI

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.

This guide outlines security best practices for managing Bright Data zone credentials, API tokens, and webhooks. It covers crucial steps like credential inventory, environment variable security, zone isolation across environments, scheduled credential rotation, and implementing pre-commit hooks to prevent sensitive data leakage, maximizing data security and compliance.

This skill guides the secure configuration of Figma API access across development, staging, and production environments. It details best practices for managing separate API tokens (PATs), file keys, and cache settings. The focus is on implementing robust environment guards and utilizing secret management solutions to prevent cross-environment data leakage and accidental operations.

This guide details how to configure Shopify applications across isolated Development, Staging, and Production environments. It establishes separate app instances, API credentials, and stores for each environment, ensuring secure, structured deployments and preventing cross-environment data leakage. Essential for professional SaaS development workflows.

This tool provides a comprehensive security assessment framework for Retrieval-Augmented Generation (RAG) systems and vector databases. It tests critical vulnerabilities such as embedding inversion, cross-tenant data leakage, knowledge-base poisoning, and retrieval manipulation, aligning with OWASP LLM08:2025 standards. It is essential for validating the security posture of multi-tenant and vector-search applications.

This skill utilizes NVIDIA's open-source garak framework to conduct comprehensive red-teaming assessments on Large Language Models (LLMs). It tests for critical vulnerabilities such as prompt injection, jailbreaks, data leakage, and toxic content generation by sending thousands of adversarial probes. Ideal for pre-deployment security validation, API guardrail testing, and generating defensible evidence for AI risk assessments.

This guide provides a structured approach to red-teaming Large Language Models (LLMs) to detect sensitive data leakage from system prompts. It simulates advanced attacks—including prompt injection, instruction override, and encoding tricks—to identify embedded secrets, API keys, and proprietary business logic. Essential for validating adherence to OWASP LLM07 and securing AI applications against data exfiltration.

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

A comprehensive tool for detecting and flagging Personally Identifiable Information (PII) within text, code, and configuration files. It is essential to use this before committing code, logging data, or generating model responses to prevent the accidental leakage of sensitive data such as emails, passwords, SSNs, or API keys.

This utility switches the active operational credential profile to manage different brands within a multi-client agency environment. It performs mandatory, deep validation of API keys, OAuth tokens, and environment variables before switching, ensuring that all subsequent platform operations (e.g., ad spending, reporting) are correctly routed to the target brand's account. It is crucial for preventing cross-client data leakage and maintaining audit integrity.

This comprehensive guide provides best practices for designing and presenting scientific experiments for high-impact academic journals. It covers structuring research around clear Research Questions (RQs), selecting strong and fair baselines, ensuring statistical rigor (mean ± standard deviation, significance testing), performing detailed ablation studies, and preventing common issues like data leakage and evaluation pollution. It is essential for authors aiming to validate complex computational findings.

A comprehensive guide for achieving experimental reproducibility and verifiability in academic writing, specifically targeting top-tier journals. It covers crucial aspects like fixing computing environments, managing random seeds, ensuring data provenance, preventing data leakage, and structuring scripts for external review, transforming research rigor into actionable steps.