Skill UI

This skill guides users through advanced prompt optimization techniques for Large Language Models (LLMs). It functions as an expert prompt engineer coach, applying methodologies like Constitutional AI and Chain-of-Thought reasoning. Use it to transform basic, raw instructions into highly effective, production-ready prompts, significantly improving accuracy, reducing hallucinations, and optimizing token usage for cost efficiency.

Designs, optimizes, and evaluates prompts for LLM applications by defining success criteria, choosing chain-of-thought or few-shot formats, testing diverse cases, enforcing structured schema outputs, and documenting guardrails so deployments stay accurate and efficient.

Document describes structured prompts, chain-of-thought guidance, few-shot examples, system prompt design, and tooling advice for building reliable prompt engineering workflows in LLM-augmented products.

Helps product managers diagnose whether AI workflows rely on indiscriminate context stuffing or deliberate context engineering, then apply bounded domains, retrieval, and Research→Plan→Reset→Implement practices to improve reliability and output quality.

Guides agentic engineering efforts where AI agents execute implementation while humans focus on risk-aware evaluation, breaking work into verifiable units, routing models by complexity, and tracking costs and regression checks.

Guides engineering teams working with AI-assisted code generation to prioritize planning, measurable coverage, resilient architectures, and behavior-focused reviews for safe, reliable delivery.

LLM-OPS orchestrates production-grade LLM operations, covering RAG, embeddings, vector databases, fine-tuning, prompt engineering, semantic caching, cost estimation, evals, and streaming agents so you can build reliable, scalable AI services.

Adopts the persona of Yann LeCun to answer questions with his technical rigor, practical engineering mindset, and combative clarity, supporting requests about CNNs, AI philosophy, and Meta-level strategy in first-person style.

Provides six structured AI engineering workflows—from prompt evaluation and context budgeting to RAG architecture, security audits, evaluation harnesses, and product sense coaching—ensuring repeatable, CI/CD-friendly decisions before deployment.

Detect sophisticated Stuxnet-style cyber-physical attacks by mapping the multi-stage OT attack chain, monitoring PLC logic integrity, correlating engineering workstation compromise indicators, and validating physics-based process measurements to detect stealthy manipulation.

Conducts comprehensive AI prompt safety reviews that analyze bias, misinformation, injection, and privacy risks while delivering effectiveness evaluations, security guidance, and best-practice recommendations for prompt engineers.