Skill UI

Mandates Taskwarrior integration for every coding request by decomposing work into tracked tasks, starting them, syncing Timewarrior time tracking, and enforcing the full add→start→done lifecycle before writing code.

Master the art of integrating high-performance, immersive 3D elements into web applications. This skill covers implementing advanced techniques using libraries like Three.js and React Three Fiber, optimizing WebGL performance, and designing interactive scenes. Focus is placed on balancing visual wonder with real-world usability and performance constraints, including model optimization and scroll-driven animations.

Provides automated guidance for ab test analyzer workflows, covering SQL queries, visualization, statistical analysis, and BI validation so teams can run reliable experiments within data analytics contexts.

Guides teams through planning, designing, and running statistically sound A/B tests with hypothesis framing, sample sizing, metric selection, implementation strategies, and analysis checkpoints for systematic growth experimentation.

Generates and iterates high-performing ad creative—headlines, descriptions, primary text, and full variations—for any platform, using performance data to discover winning angles, stay within platform specs, and scale creative production.

AddressSanitizer accelerates fuzz testing by instrumenting C/C++ and unsafe Rust code to detect buffer overflows, use-after-free, and related memory corruption bugs at runtime, with configurable ASAN_OPTIONS for verbosity, abort behavior, and leak control.

Provides best-practice guidance for building production-ready ADK agents with modular code, structured tools, regression tests, and deployment automation across Python, Java, or Go runtimes.

Provision production-grade Vertex AI ADK Agent Engine infrastructure with Terraform, covering Agent Engine runtime, code execution sandbox, Memory Bank, VPC Service Controls, and secure IAM setup for multi-agent deployments.

This framework addresses the gap between academic benchmarks and real-world production performance for LLM agents. It teaches advanced quality engineering practices, such as behavioral contract testing, adversarial testing, and statistical analysis, ensuring agents maintain reliability and robustness when deployed. It helps developers move beyond simple pass/fail rates to comprehensive capability assessment.

This skill guides the design and implementation of robust memory architectures for advanced AI agents. It moves beyond simple data storage, focusing on the critical art of memory retrieval. Learn strategies for chunking, selecting appropriate vector stores, and integrating various memory types (episodic, semantic, working) to ensure agents maintain long-term consistency and intelligence across millions of interactions. Ideal for building sophisticated, stateful autonomous agents.

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.