Orchestrate iterative AI agent loops to implement user stories and verify acceptance criteria.
npx skills add https://github.com/ytqh/agent-skills --skill ralph-loop-ytqh
Please help me install this Agent Skill. Skill: ralph-loop Source: https://github.com/ytqh/agent-skills/tree/main/skills/ralph-loop Command: npx skills add https://github.com/ytqh/agent-skills --skill ralph-loop-ytqh
This Skill provides a structured framework for orchestrating iterative AI agent-driven development by defining features as user stories with testable acceptance criteria and driving automated agent loops until all stories pass.
Define your first set of user stories in the recommended format, then start the Ralph Agent Loop to begin automated development and monitor until all stories pass.
High-intent search queries and answers about installing and using this skill.
To automate feature development from user stories and acceptance criteria, you can orchestrate iterative AI agent loops that implement requirements and verify testable conditions until all stories pass. This structured workflow decomposes features into actionable stories and logs progress for subsequent iterations.
An AI agent loop for software development workflows is an iterative automation process where agents execute user stories, verify acceptance criteria, and log progress. It drives automated implementation runs until all testable criteria pass, accelerating feature delivery through continuous agent execution.
You structure user stories for automated AI agent orchestration by defining features with clear, testable acceptance criteria. The recommended format guides automated work by decomposing requirements into actionable stories that AI agents can implement and verify in iterative loops.
Yes, you can use AI agents to verify acceptance criteria automatically by running them in orchestrated loops. The agents implement features from user stories, test the acceptance criteria, and log progress for subsequent iterations until all defined criteria pass successfully.
The best way to orchestrate iterative agent runs for feature delivery is to define features as user stories with testable acceptance criteria, then start an automated agent loop. This approach monitors progress and logs iterations, ensuring all stories pass before completion.
Logging progress during automated AI agent development loops records each iteration's execution state, acceptance criteria verification results, and implementation status. This structured log captures progress for subsequent iterations, enabling continuous improvement until all user stories pass.
shynlee04Run AI agents in a loop to implement and verify user stories.
AdeptMindValidate completed development work against acceptance criteria and architectural designs.
RobinNorbergDrive PRD-driven task iteration until all user stories pass architect verification.
sdebaunOrchestrate a multi-agent development team to implement features from TDD specifications and PRDs.
