skills.rest
dheerkt
dheerktCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

ralph-wiggum

Orchestrate iterative AI development loops with Kiro CLI.

Updated Nov 2, 2025
Search Tags:#automation#product-management#ai-workflow#iterative-development#ralph-wiggum#task-iteration#kiro-cli
One-click install
npx skills add https://github.com/dheerkt/dotfiles --skill ralph-wiggum-dheerkt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ralph-wiggum
Source: https://github.com/dheerkt/dotfiles/tree/main/opencode-work/skill/ralph-wiggum
Command: npx skills add https://github.com/dheerkt/dotfiles --skill ralph-wiggum-dheerkt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Iterative AI development loops that start, monitor, review, and iterate on tasks using the Ralph Wiggum technique with Kiro CLI, activated only when explicitly requested by the user.

Core Features & Use Cases

  • Structured iteration across multiple tasks with defined verification criteria
  • Separation of conversation history via Kiro CLI to avoid context contamination
  • Supports PRD-driven plans, Verify, Promise, Free Run, and Resume modes for flexible workflows
  • Facilitates review and next-step decision-making to improve outcomes

Quick Start

Activate Ralph only when the user explicitly requests it and begin iterative cycles that progressively refine the output.

Frequently Asked Questions about ralph-wiggum

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate iterative AI development loops for multi-step tasks?▼

Iterative AI development loops are orchestrated by structuring tasks into planning, execution, monitoring, and review cycles that progressively refine the output until completion. This process requires explicit user activation to begin the iteration.

How do I prevent context contamination when running multiple AI task iterations?▼

Context contamination is prevented by separating conversation history via Kiro CLI during task iteration. This isolation ensures ongoing AI workflow cycles remain distinct and structured across multiple development tasks.

What modes are available for PRD-driven AI task automation?▼

PRD-driven AI task automation supports Verify, Promise, Free Run, and Resume modes. These modes provide flexible workflows for structured iteration, allowing defined verification criteria and next-step decision-making throughout the process.

When should I use an iterative AI workflow instead of a single prompt execution?▼

An iterative AI workflow is needed for multi-step tasks that require planning, execution, monitoring, and review until completion. It provides structured iterations with explicit verification criteria rather than a single unmanaged execution.

Do I need to explicitly activate iterative task automation for my AI workflow?▼

Yes, you must explicitly activate the iterative task automation technique to begin the structured cycles. The workflow will not start automatically, ensuring you maintain control over the planning, execution, and review phases.

Related Skills

Explore More Product & Management Skills→
fabricioctellesfabricioctelles
47

ralph-loop-kiro-specs

Automate iterative Kiro spec task implementation with self-correction and progress tracking.

Community
Advanced
breeze4breeze4

session-recovery

Record status, phase, and last completed task in an execution log.

Community
Intermediate
BrennonTWilliamsBrennonTWilliams
7

ll-capture-issue

Create project issues from natural language input or conversation context.

Community
Intermediate
hathbangerhathbanger

ralph-tui

Orchestrate multi-step AI task execution from a JSON PRD with quality gates.

Community
Intermediate