prompt▌

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

Expert guide for optimizing LLM prompts through patterns, testing, and systematic refinement. \n \n Covers five core techniques: few-shot learning, chain-of-thought reasoning, prompt optimization, template systems, and system prompt design \n Includes progressive disclosure patterns that scale complexity from simple instructions to multi-example reasoning traces \n Provides practical examples for each pattern, from code review templates to bug analysis workflows \n Emphasizes testing and iterati

Analyze a draft prompt, critique it, match it to ECC ecosystem components, and output a complete optimized prompt the user can paste and run.

Transforms raw user prompts into optimized prompts using 11 established frameworks. \n \n Analyzes task type, complexity, and clarity to intelligently select the best framework(s) for the job \n Supports 11 frameworks including RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, and GROW \n Operates in \"magic mode,\" silently selecting frameworks without exposing technical jargon to users \n Blends multiple frameworks for complex tasks spanning different dimens