Skill UI

This guide provides comprehensive strategic advice for authors submitting manuscripts related to Artificial Intelligence, Constraint Programming, and Operations Research (AI/OR) to top-tier conferences like CPAIOR. It helps refine scope, structure, evidence standards, and narrative framing to meet highly specialized reviewer expectations, ensuring the paper is positioned as a solid hybrid optimization contribution.

This skill displays a comprehensive dashboard for autoresearch experiments, showing results, active loops, and progress. Users can view details for specific experiments or entire domains. It supports exporting data in Markdown or CSV formats for further analysis. Ideal for tracking optimization metrics and experiment health.

A comprehensive knowledge base covering the theoretical fundamentals and advanced algorithms of Deep Reinforcement Learning (Deep RL). It details core concepts like Bellman equations, policy gradients, and the trade-offs between policy optimization and Q-learning. The guide analyzes leading algorithms such as PPO, SAC, TRPO, and DDPG, and provides rigorous best practices for debugging and running scientifically sound RL experiments.

This skill enables systematic improvement of existing AI agents through performance analysis, prompt engineering, and continuous iteration. It involves analyzing failure modes, collecting baseline metrics, and applying optimization techniques like chain-of-thought enhancement and role refinement to enhance reliability and task success rates.

Optimize multi-agent systems through coordinated profiling, workload distribution, and cost-aware orchestration. This skill helps improve agent performance, throughput, and reliability by identifying bottlenecks in workflows. It covers context window optimization, parallel execution strategies, and cost management for LLM usage. Suitable for engineering complex AI workflows requiring high efficiency and stability.

Design and evaluate LLM product features, retrieval, validated outputs, latency, and cost controls. Covers LLM integration patterns, RAG architecture, scalable prompt engineering, trustworthy AI UX, and cost optimization strategies for sustainable AI implementation in production environments.

Expert guidance for building successful AI wrapper products that solve specific problems and generate revenue. Covers product strategy, prompt engineering, cost optimization, model selection, AI UX, and usage metering. Ideal for developers and entrepreneurs creating AI-powered SaaS tools.

Context optimization extends effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is to make better use of available capacity rather than increasing window size. Techniques include compaction, observation masking, KV-cache optimization, and context partitioning to reduce costs and latency in long-running agent systems.

This skill enables you to act as an elite AI-powered customer support specialist. It covers conversational AI, automated ticketing, sentiment analysis, and omnichannel support experiences. You will master best practices for customer experience optimization, knowledge management, and analytics. Ideal for improving satisfaction scores, reducing resolution times, and driving loyalty through intelligent automation and personalized service across various channels.

This skill evaluates content for AI citability based on research from Princeton and Georgia Tech. It scores content on answer block quality, self-containment, and structural readability to optimize visibility in AI-generated responses. Ideal for SEO and GEO strategies to ensure AI models extract and cite your content effectively.

Improve LLM prompts using task-specific evaluation, structured instructions, chain-of-thought reasoning, and regression checks for production-ready AI applications.

This skill helps reduce LLM API and infrastructure costs by 50-90% using strategies like model selection, prompt caching, batching, quantization, and self-hosting. It covers cost tracking, right-sizing models for tasks, provider-side caching, and semantic caching to avoid redundant calls. Suitable for developers managing AI spend and optimizing cloud infrastructure.