Skill UI

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.

This toolkit provides six expert-level workflows for AI engineering, including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior partners by offering structured methodologies, quantitative scoring, and decision frameworks. Suitable for optimizing LLM systems, designing secure agents, and planning product strategy before development.

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.

This skill offers a design and workflow guide for intelligent context management within AI workflows. It focuses on capturing project state, enabling semantic retrieval, and supporting multi-agent coordination. Key features include state serialization, compression techniques, vector database integration, and knowledge graph construction. It helps preserve architectural decisions and facilitate knowledge transfer without implementing actual storage functions, serving as a strategic planning tool for context engineering.

Provides comprehensive guidance on building robust evaluation frameworks for complex agent systems. It addresses the inherent challenges of non-determinism and multiple valid paths, enabling systematic performance testing, validating context engineering choices, and measuring continuous improvements across multiple quality dimensions (e.g., factual accuracy, tool efficiency).

This skill detects when AI agents exploit human trust through social engineering, deceptive guidance, or false assurances. It identifies prompts that induce unsafe actions, request sensitive credentials, or impersonate authority figures. Use this when evaluating agent safety, security advice, or decision-influencing workflows. Skip for static information.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

Conduct authorized security assessments for LLM applications and AI agents. Covers prompt injection, tool abuse, RAG exposure, memory poisoning, and system prompt extraction following OWASP LLM/ASI Top 10 guidelines. Includes red-teaming, vulnerability mapping, and compliance engineering for AI systems to ensure safety and robustness.

This skill enables building production-ready machine learning systems using modern frameworks like PyTorch and TensorFlow. It covers model serving, feature engineering, A/B testing, monitoring, and MLOps practices. Focuses on scalable, reliable, and efficient ML infrastructure for delivering business value in production environments.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

This skill provides a standardized framework for evaluating autonomous AI agents. It tracks task completion rates, planning accuracy, tool invocation precision, and operational costs. Suitable for engineering teams managing distributed systems, it enables reproducible benchmarking, regression monitoring, and compliance tracking through structured data models like CSV, SQL, and JSON schemas.