ralph-loop

Enforce hard context resets between iterations by persisting state in engram.

Updated Mar 15, 2026
One-click install
npx skills add https://github.com/JNZader/javi-ai --skill ralph-loop-jnzader
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ralph-loop
Source: https://github.com/JNZader/javi-ai/tree/main/own/skills/ralph-loop
Command: npx skills add https://github.com/JNZader/javi-ai --skill ralph-loop-jnzader

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Long SDD sessions accumulate stale context — hallucinated content, outdated decisions, and degraded performance. Ralph Loop enforces a hard context reset between iterations by persisting state in engram, ensuring each batch starts from a clean slate while preserving continuity.

Core Features & Use Cases

  • Enforces one batch per iteration to avoid cross-task context leakage.
  • Persists state externally (engram) to decouple agent context from long-running processes.
  • Supports multi-phase SDD apply by spawning fresh agents for each phase and accumulating results.
  • Provides a clear protocol for load, execute, save, and iterate with guardrails and decision logs.

Quick Start

Start a Ralph Loop by initializing the first iteration batch for your SDD change and letting the orchestrator spawn subsequent iterations with fresh context.

Frequently Asked Questions about ralph-loop

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

FAQPage Schema
How do I prevent context drift and hallucinations during long SDD automation sessions?

Preventing context drift in long SDD sessions requires enforcing a hard context reset between iterations. By persisting state externally, each batch starts from a clean slate, avoiding hallucinated content and degraded performance while preserving continuity.

What causes context rot in long-running agent orchestration and how does context reset help?

Context rot in long-running orchestration is caused by accumulating stale decisions and hallucinated content. A hard context reset helps by enforcing one batch per iteration and persisting state externally, decoupling agent context from long-running processes to prevent cross-task leakage.

How do I manage state across multiple iterations when spawning fresh agents for SDD phases?

To manage state across multiple SDD phases, you persist state externally in an engram. This enables spawning fresh agents for each phase, loading the saved state to accumulate results sequentially without suffering from context degradation.

Do I need an orchestration integration to run iteration-by-iteration SDD execution?

Yes, you need an orchestration integration to run iteration-by-iteration SDD execution. The loop relies on this orchestration integration to spawn fresh agents for each iteration and manage accumulated state across cycles.

What is the best way to decouple agent context from long-running software development processes?

The best way to decouple agent context from long-running software processes is to persist state externally in an engram. This enforces a clear protocol for load, execute, save, and iterate with guardrails and decision logs across cycles.

What are the limitations of using context resets between agent iterations?

A limitation of using context resets is the strict requirement for external state persistence to maintain continuity. Without an orchestration integration to manage the load, execute, save, and iterate protocol, the iteration-by-iteration execution will fail.