Pattern Based Agentic Design Guidance

via “web application development framework pattern extraction”

FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Traycer AI, VSCode Agent, Warp.dev, Windsurf, Xcode, Z.ai Code, Dia & v0. (And other Open Sourced) System Prompts

Unique: Catalogs web development patterns from production AI tools (v0, Lovable, Same.dev) including design system enforcement, component generation conventions, and integration patterns — reveals how tools balance code generation flexibility with design consistency and framework best practices

vs others: Provides comparative analysis of web development patterns across multiple AI tools rather than single-tool documentation; enables informed design of web-focused AI agents

via “agent architecture pattern documentation and comparison”

A one stop repository for generative AI research updates, interview resources, notebooks and much more!

Unique: Organizes agent architecture around explicit decision points and evaluation frameworks rather than just listing components. Maps architectural choices to specific evaluation benchmarks (e.g., ToolBench for tool usage, ClemBench for collaboration) that measure the effectiveness of those choices.

vs others: More comprehensive than individual framework documentation (LangChain, AutoGen); provides cross-framework architectural patterns and explicit evaluation methodologies, whereas framework docs focus on their specific implementation details.

via “agent architecture principles and design patterns”

https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成

Unique: Provides explicit 12-factor agent architecture framework (analogous to 12-factor app) with dedicated sandbox guide and agent evaluation complete guide, addressing production concerns beyond typical agent tutorials

vs others: Treats agent architecture as a first-class concern with explicit principles; most agent tutorials focus on capability building rather than production architecture

via “design system resource documentation and guidelines”

A library of Agent Skills designed to work with the Stitch MCP server. Each skill follows the Agent Skills open standard, for compatibility with coding agents such as Antigravity, Gemini CLI, Claude Code, Cursor.

Unique: Organizes design system knowledge in a structured resources/ directory that agents can reference during code generation, treating design system documentation as a queryable knowledge base rather than static documentation. This approach enables agents to make informed decisions about component selection, styling, and accessibility without explicit instruction.

vs others: More accessible than external design system documentation because resources are co-located with skill logic, and more actionable than unstructured documentation because resources are organized by type (checklists, style guides, API docs).

via “vibe coding to agentic engineering progression framework”

from vibe coding to agentic engineering - practice makes claude perfect

Unique: Provides a structured progression framework from exploratory 'vibe coding' to production-grade agentic engineering, with documented patterns, anti-patterns, and best practices at each maturity level. This is unique because it acknowledges the learning journey and provides guidance for each stage rather than assuming production-ready practices from the start.

vs others: More comprehensive than isolated best practices because it provides a progression framework; more practical than academic patterns because it's based on community experience and includes anti-patterns and common pitfalls.

via “agentic-ai-system-instruction-documentation”

LEAKED SYSTEM PROMPTS FOR CHATGPT, CLAUDE, GEMINI, GROK, PERPLEXITY, CURSOR, LOVABLE, REPLIT, AND MORE! - AI SYSTEMS TRANSPARENCY FOR ALL! 👐

Unique: Extends system prompt documentation to agentic AI systems with tool-calling capabilities, capturing not just behavioral constraints but also tool-calling schemas and agent-specific decision-making instructions. The repository documents how agents are instructed to use tools like code execution, file access, and external APIs.

vs others: Provides unified documentation of agent system prompts alongside tool-calling schemas, whereas most agent documentation is scattered across provider docs without centralized transparency analysis.

via “reasoning-driven image generation with domain-specific skill templates”

Multi-modal Generative Media Skills for AI Agents (Claude Code, Cursor, Gemini CLI). High-quality image, video, and audio generation powered by muapi.ai.

Unique: Expert Library skills encode professional knowledge (atomic design principles, branding psychology, cinematography rules) into reusable prompt templates and multi-step workflows; identity-lock mechanism uses seed-based generation with consistency validation to produce coherent portrait sets

vs others: Encodes domain expertise that competitors require manual prompt engineering to replicate; identity-lock portrait generation is unique vs. standard image generators which produce uncorrelated variations

via “design pattern application and structural guidance”

AI Pundit Magic offers features such as Design to Code, Pundit Toolbox, Code Editor, request history management, and chat. It seamlessly integrates web-based React frameworks (Raaghu, Ant Design, Chakra, Material UI, Fluent UI), Angular frameworks (Angular Material, NG-Zorro, and PrimeNG), mobile pl

Unique: Automatically identifies and applies design patterns to generated code, ensuring structural consistency with recognized best practices. Provides guidance for both architectural patterns (application structure) and code patterns (component organization) specific to React, Angular, and Flutter.

vs others: Offers automated pattern application beyond manual code review, but lacks the flexibility and domain-specific knowledge of experienced architects or pattern-specific tools.

via “character creation and design pattern documentation”

Awesome curated collection of images and prompts generated by GPT-4o and gpt-image-1. Explore AI generated visuals created with ChatGPT and Sora, showcasing OpenAI’s advanced image generation capabilities.

Unique: Provides documented patterns for character specification, consistency maintenance, and pose/expression control with working examples, enabling systematic character design rather than random generation attempts

vs others: More structured than generic character generation tips; documents specific techniques for consistency, attribute specification, and pose control with visual examples demonstrating effectiveness

via “pattern-based agentic design guidance”

Agentic Engineering Patterns

Unique: Focuses specifically on agentic systems, providing a curated set of patterns that are not commonly found in general software engineering resources.

vs others: More specialized than generic design pattern resources, offering targeted insights for building autonomous agents.

via “agent instruction generation with tool configuration”

Templates and workflow for generating PRDs, Tech Designs, and MVP and more using LLMs for AI IDEs

Unique: Implements a transformation hub that converts human-readable documentation into machine-actionable agent instructions with tool-specific configurations, using a guided prompt template that decomposes comprehensive specifications into modular files. This differs from manual configuration by automating the translation from documentation to agent-consumable format.

vs others: More efficient than manually creating agent configurations because it automatically generates tool-specific files and modular instruction structure from existing documentation, reducing manual configuration overhead by 70-80% compared to hand-crafted agent setups.

via “agent behavior customization through system prompts and role definitions”

yicoclaw - AI Agent Workspace

Unique: Provides structured role definition system that separates personality, constraints, and output format from core agent logic, enabling reusable role templates across projects

vs others: More maintainable than ad-hoc prompt engineering because role definitions are declarative and version-controlled, making it easier to audit and update agent behavior

via “agent-driven code generation with iterative refinement”

Capable of designing, coding and debugging tools

Unique: Implements multi-turn agent-driven code generation with built-in validation and refinement loops, where the agent autonomously decides when code meets requirements rather than relying on single-pass LLM output

vs others: Differs from Copilot or Cursor by using agentic reasoning to iteratively improve code quality rather than relying on context-window code completion, enabling more complex tool generation

via “agent instruction and role definition with customizable system prompts”

Agency Swarm framework

Unique: Separates agent behavior definition from implementation by accepting natural language instructions that are passed directly to OpenAI's Assistants API, enabling prompt engineering and behavioral tuning without modifying agent code or tool definitions

vs others: Provides more flexibility than hard-coded agent behavior, and enables non-technical stakeholders to tune agent behavior through prompt engineering rather than requiring code changes

via “component usage pattern and best practice retrieval”

Shopify Polaris UI Components MCP Server for AI assistants

Unique: Curates Polaris-specific patterns and best practices into queryable knowledge that AI assistants can reference during code generation, enabling pattern-aware generation rather than purely schema-driven generation.

vs others: Provides Shopify design system context that generic LLMs lack, improving code quality and accessibility compliance vs. LLM-only generation without domain-specific pattern guidance.

via “ai-driven-design-intent-interpretation”

Gensbot uses AI to craft personalised printed merchandise. One prompt creates one unique product to fit your needs.

via “agent prompt engineering and instruction design”

A book about building AI agents with tools, memory, planning, and multi-agent systems.

Unique: Treats prompt engineering as a systematic discipline with patterns for role definition, constraint encoding, and output formatting rather than ad-hoc trial-and-error

vs others: More agent-focused than generic prompt engineering guides because it addresses multi-step reasoning, tool use, and error recovery in prompts

via “pattern-to-design-recommendation synthesis”

Unique: Automatically translates statistical patterns into design-actionable recommendations using a pattern-to-design mapping engine, rather than requiring designers to manually interpret data — includes segment-specific design direction

vs others: More automated than manual design synthesis from data, but less customizable than bespoke design strategy workshops; bridges data and design without requiring data science expertise

via “style-adaptive design recommendation”

via “ai-assisted design suggestion generation”