3 Repos
Specialized input forms used by AI agents to gather missing information from users to refine prompts.
Distinct from Forms: Focuses on the agent-driven loop of clarifying intent, not general form UI construction.
Explore 3 awesome GitHub repositories matching user interface & experience · Intent Clarification. Refine with filters or upvote what's useful.
Open Design is an AI design orchestration platform and LLM agent workspace designed for generating prototypes, presentations, and motion graphics. It functions as a local-first environment that integrates various command-line AI agents and coding tools into a unified design workflow, allowing users to migrate design specifications into brand-compliant source code. The project is distinguished by its ability to enforce visual consistency through a design system manager that defines brand contracts and tokens using Markdown. It features a specialized motion graphics generator that converts HTML
Renders structured question forms to clarify user intent and feed answers back into the AI conversation.
Prompt Master is an AI skill that automates prompt engineering by detecting the target AI system and applying the correct prompt architecture automatically. It generates optimized prompts for over 30 different AI tools, adapting format and syntax to each target system without requiring manual conversion. The system distinguishes itself through several integrated capabilities. It extracts missing dimensions of intent from vague requests by asking up to three targeted clarifying questions before generating a final prompt. A memory block of prior decisions and constraints is prepended to maintai
Asks up to three targeted clarifying questions to fill missing dimensions of intent before generating prompts.
This project is a framework for managing multi-agent software development workflows built on the Model Context Protocol. It functions as an AI-driven task orchestrator that decomposes complex development objectives into atomic units, tracks their lifecycle, and coordinates specialized agents to execute, verify, and refine work. By maintaining persistent project context and history, the system ensures continuity across sessions, allowing agents to retain state and adhere to established coding standards. The system distinguishes itself through its dependency-graph task management and multi-agen
Uses specialized agent-driven loops to gather missing information from users and refine implementation plans.