Skill UI

This skill provides a strategic framework for content creation within the AI Agent vertical. It emphasizes hands-on product testing, translating complex technical concepts for general audiences, community-driven distribution, and practical tutorials. Ideal for tech media creators, AI product reviewers, and content strategists aiming to build authority in the AI Agent space through verified experience and clear communication.

This deployment skill is designed to be integrated into the Playwright testing workflow. Its primary function is to validate the loading process of shared skills across the entire environment during the startup phase. It ensures that all necessary components and modules are correctly registered and available for reliable End-to-End (E2E) testing execution.

Stress-test core claims for Journal of Systems Engineering submissions: parameter sensitivity, initial conditions, alternative models and metrics, extreme scenarios, Monte Carlo repetitions, random seeds, software environments, data/code statements, and explicit failure boundaries.

Generate decision-ready reports from AI agent evaluation runs. This skill ensures autonomous, assisted, failed, and timed-out outcomes remain distinct and comparable. It defines metrics with correct denominators, handles latency and cost populations honestly, and maps evidence to decision gates. Ideal for benchmarking, regression testing, and production readiness validation of AI agents.

This skill enables deterministic fault injection testing for agent workflows to verify recovery mechanisms, state preservation, and safety boundaries. It generates fault matrices and event timelines to distinguish between recovered, contained, and unrecoverable failures without impacting production systems or real user data.

Main agent orchestrator that coordinates a specialized squad of agents across requirements, planning, architecture, implementation, review, testing and deployment, using compressed summaries and reference-based artifacts to preserve context.

Generate tailored AGENTS.md and companion rules by analyzing your codebase. It detects package managers, validates commands, and creates specific guidelines for architecture, testing, and workflows. Supports full, minimal, update, and dry-run modes to ensure project-specific AI agent configurations without generic templates.

This skill covers implementing service mesh patterns for AI inference traffic management using Istio or Linkerd. It includes configuring mTLS for security, setting up authorization policies for service accounts, managing egress for model endpoints, and implementing traffic splitting for canary releases and A/B testing. It ensures cross-cluster resilience and protects GPU-backed services from cascading failures without modifying application code.

This skill enables structured AI red team exercises to evaluate model resilience against jailbreaks, data exfiltration, and harmful outputs. It helps identify exploitable failure modes before attackers do, suitable for new LLM features, third-party model evaluation, and compliance audits. Includes adversarial prompt libraries and automated testing scripts for security assessments.

Enables agents to perform authorized security assessments, blue team exercises, and penetration testing on AI products, agents, and infrastructure. Uses first-principles reasoning to identify vulnerabilities like prompt injection and tool abuse, generating evidence-based security reports without relying solely on payload libraries.

A comprehensive guide to building production-ready Apache Airflow Directed Acyclic Graphs (DAGs). This skill covers best practices for operators, sensors, testing, deployment, and designing robust data pipelines. Use this when orchestrating complex workflows, managing ETL processes, or ensuring reliable batch job execution in a production environment.

This skill automates end-to-end (E2E) functional testing for Android applications. It parses an XML specification defining a multi-step user 'journey.' The executor sequentially performs interactive UI actions (taps, swipes, text input) using ADB commands and verifies application states through assertions. It outputs a detailed, standardized JSON report, making it ideal for robust CI/CD integration and quality assurance.