Skill UI

This toolkit provides six production-ready AI engineering workflows 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 engineering partners with structured methodologies, quantitative scoring, and checklists for consistent results in LLM system optimization and security.

Apply Istio/Linkerd service mesh patterns to AI inference traffic: strict mTLS, per-service authorization, egress allowlists, canary and A/B model routing, circuit breaking, and cross-cluster resilience for GPU-backed model services.

This skill structures a highly controlled, bounded development life cycle for AI agents. It mandates a structured three-phase workflow: Planning (Spec), Implementation (Build), and Verification (Review). It is essential for building complex features from scratch, especially when dealing with ambiguous requirements, high risk, or needing explicit human approval gates to ensure the resulting code meets all defined specifications.

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.

A comprehensive guide detailing the aesthetic and technical implementation of AI-native user interfaces for web and mobile applications. It emphasizes conversational flow, adaptive layouts, and generative state visual cues, providing CSS, SwiftUI, and Flutter code examples.

This skill guides the development of AI-powered products, focusing on LLM integration, RAG architecture, scalable prompt engineering, trustworthy AI UX, and cost optimization. It ensures products are production-ready rather than failing demos.

This skill enables structured AI red team exercises to test for jailbreak resistance, data exfiltration risks, and harmful output controls. It helps discover exploitable failure modes before attackers do, suitable for new LLM features, third-party model evaluation, and security audits. Includes Python scripts for automated testing against various attack vectors like prompt injection and tool abuse.

Build AI-focused SRE incident response practices for LLM outages, degraded quality, runaway cost events, and safety regressions. Includes alert rules, playbooks, and severity framework.

A specialized skill for generating hyper-realistic, humanized images using Google AI Studio (Gemini). It simulates the imperfections of real photography—such as natural lighting, slight camera shake, and depth of field—to create content that looks genuinely captured by a smartphone. Ideal for creating high-quality assets for social media (influencer style) or educational materials.

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.

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.