Skill UI

This guide provides a comprehensive framework for designing statistically rigorous A/B tests, split tests, and multivariate experiments. It covers everything from formulating strong hypotheses and calculating sample sizes to selecting appropriate primary, secondary, and guardrail metrics for various business contexts.

This expert framework guides users through the systematic process of conducting rigorous A/B tests and growth experiments. It covers all stages: formulating strong hypotheses, selecting appropriate metrics (primary, secondary, guardrail), calculating necessary sample sizes, and choosing the best implementation method (client-side vs. server-side). Use this when planning to compare two versions or build a systematic growth experimentation program.

A specialist tool designed to pressure-test AI Management Systems (AIMS) against the ISO 42001 standard. It executes six critical compliance checks, covering scope definition, risk mapping, policy adherence, and framework integration. Essential for pre-certification readiness, annual internal audits, and new system onboarding.

This skill automatically generates comprehensive unit tests based on provided source code. It supports multiple programming languages (Python, JavaScript, Java, Go, Rust) and popular testing frameworks (pytest, Jest, JUnit). Use it to improve code coverage, test boundary conditions, and validate error handling efficiently, saving manual testing time.

This framework serves as a rigorous thought partner, designed to challenge underlying assumptions, separate raw evidence from interpretation, and ensure that all conclusions are fully defensible. It guides users to define clear decision goals, test counterfactuals, and calibrate claims strictly to the available data quality. Ideal for high-stakes projects where the cost of error is significant.

This skill provides a comprehensive framework for evaluating Power BI reports. It assesses report quality, usage adoption rates, performance metrics, and structural integrity across different lifecycle stages (development, testing, active use). It analyzes usage data, audience reach, and performance bottlenecks, generating actionable feedback and optimization recommendations to ensure reports are effective and maintainable.

Implements a robust, four-stage coding discipline (Plan, Isolate, Test First, Double Review) to prevent premature code delivery. This skill forces the AI to structure complex tasks by planning, implementing in isolation, writing tests first, and performing deep self-review, significantly improving code correctness and quality.

Generates a comprehensive and actionable UX research plan for any product feature or question. This skill covers the entire research lifecycle, including defining objectives, selecting methodology (e.g., usability testing, interviews), creating participant screeners, writing discussion guides, and structuring a synthesis framework. Ideal for product managers and UX researchers needing a structured approach to validate assumptions and drive design decisions.

CHIPSEC is an open-source framework designed to assess the low-level security configuration of x86 platform firmware and hardware. It verifies critical security controls such as SPI flash write protection, BIOS locks, SMM/SMRR configuration, and Secure Boot variable integrity. Users can run automated tests, dump SPI flash memories for forensics, and detect potential firmware implants that persist below the operating system layer.

An automated framework for continuous, repeatable red-teaming of LLM applications within CI/CD pipelines. It integrates Promptfoo and DeepTeam to test against recognized vulnerabilities like OWASP LLM Top 10, OWASP Agentic, and MITRE ATLAS (e.g., Prompt Injection, Jailbreaks). This ensures that security vulnerabilities do not regress after code or model changes, providing a critical merge-blocking security gate for production LLM apps.

A comprehensive framework for authorized AWS penetration testing and red-teaming. This skill automates end-to-end cloud exploitation using Pacu, covering session management, deep IAM enumeration, automated privilege escalation scanning, and establishing persistence backdoors. It is designed to assess the full blast radius of compromised cloud credentials.

An open-source automation framework for conducting multi-turn, adversarial red-teaming against conversational LLMs. PyRIT simulates real-world attacker behavior using advanced orchestrators (Crescendo, TAP) and scorer feedback loops. It is designed for comprehensive security assessments of AI agents and chatbots, going far beyond single-shot prompt testing to evaluate robustness under gradual or adaptive attacks.