ralph

Spawn dedicated subagents per task across a six-phase workflow.

1|Updated Mar 6, 2026
One-click install
npx skills add https://github.com/adiman9/mnemos --skill ralph-adiman9
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ralph
Source: https://github.com/adiman9/mnemos/tree/main/core/skills/ralph
Command: npx skills add https://github.com/adiman9/mnemos --skill ralph-adiman9

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Processing AI agent tasks at scale requires strict isolation and reliable orchestration. Ralph provides a self-contained workflow that spawns a dedicated subagent per task and per phase to ensure fresh context and prevent cross-task contamination.

The system coordinates a multi-phase pipeline (extract, create, enrich, reflect, reweave, verify) with queue-driven progression, batch handling, and cross-sibling linkage to maintain traceability across related claims.

By enforcing end-to-end, deterministic behavior and standardized handoffs, it enables predictable outcomes and easier debugging in complex agent pipelines.

Core Features & Use Cases

  • Subagent-per-task phase isolation to prevent context leakage.
  • Serial and parallel processing modes with strict phase progression.
  • Batch-aware reflect/reweave and cross-link validation for consistency.
  • Mandatory spawn of subagents for every task and RALPH HANDOFF outputs for chainable pipelines.
  • Automatic queue advancement and per-claim note creation across phases.

Quick Start

Process the next N tasks from the queue in serial mode, spawning a subagent for each task.

Frequently Asked Questions about ralph

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

FAQPage Schema
How do I prevent context leakage when running multiple AI agent tasks in a pipeline?▼

Subagent orchestration prevents context leakage by spawning a dedicated subagent for every task and phase, ensuring fresh context isolation and preventing cross-task contamination during complex AI agent workflows.

What is the best way to orchestrate multi-phase AI agent workflows with strict task isolation?▼

Multi-phase AI agent orchestration coordinates a six-phase pipeline (extract, create, enrich, reflect, reweave, verify) with queue-driven progression and mandatory subagent spawning to enforce deterministic behavior and predictable outcomes.

Can I process task queues in parallel while maintaining traceability across related claims?▼

Queue processing supports serial and parallel modes with batch-aware reflect and reweave phases, utilizing cross-sibling linkage to maintain traceability and consistency across related claims during parallel task execution.

How do I chain AI agent pipelines together using standardized handoffs?▼

Pipeline chaining uses RALPH HANDOFF payloads to connect workflows, providing standardized handoff outputs that enable deterministic behavior and easier debugging across complex, multi-stage AI agent processing pipelines.

Does subagent task isolation work without external dependencies for complex agent orchestration?▼

Subagent task isolation operates as a self-contained workflow with no external dependencies, managing queue advancement, batch handling, and per-claim note creation entirely through its internal phase progression system.