prompt▌

Advanced AI agent runtime security. Works 100% offline with 600+ bundled patterns. Optional API for early-access and premium patterns.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

../../../engineering-team/senior-prompt-engineer/SKILL.md

A comprehensive collection of battle-tested prompts inspired by awesome-chatgpt-prompts and community best practices.

Generate comprehensive, production-ready prompts for UI/UX prototype creation. Transform user requirements into detailed technical specifications that include design systems, color palettes, component specifications, layout structures, and implementation guidelines. Output prompts are structured for optimal consumption by AI tools or human developers building HTML/CSS/React prototypes.

Advanced techniques for maximizing LLM reliability, consistency, and output quality through systematic prompt design. \n \n Five core patterns: few-shot learning with 2-5 examples, chain-of-thought reasoning for multi-step logic, systematic optimization through A/B testing, reusable template systems with variables, and system prompts for persistent global behavior \n Progressive disclosure approach starts simple and adds complexity only when needed, with four escalation levels from direct instru

Advanced prompt patterns for few-shot learning, chain-of-thought reasoning, optimization, templates, and system prompt design. \n \n Covers five core pattern categories: few-shot example selection with semantic diversity, chain-of-thought reasoning traces, iterative optimization workflows with A/B testing, modular template systems with variable interpolation, and comprehensive system prompt architecture \n Includes structured implementation workflows for creating new prompts, optimizing existing

Curated collection of role-based, task-specific, and analytical prompt templates for common development workflows. \n \n Includes 15+ ready-to-use prompts across categories: expert developer, code reviewer, technical writer, system architect, debugging, refactoring, testing, and security review \n Covers prompt engineering techniques like chain-of-thought, few-shot learning, persona patterns, and structured output formatting \n Provides a prompt improvement checklist covering objective clarity,

Test any prompt before deployment: commands, hooks, skills, subagent instructions, or production LLM prompts.

Expert guidance on prompt optimization, LLM evaluation, and agentic system design for production AI applications. \n \n Covers advanced prompt engineering patterns, few-shot learning, chain-of-thought techniques, and structured output design for Claude, GPT-4, and other LLMs \n Includes RAG optimization frameworks, LLM evaluation methodologies, and performance tuning strategies with reference documentation \n Provides agentic system design principles, multi-agent orchestration patterns, and depl