RUNE Prompt Amplification

Transforms any flat prompt into a structured 8-layer XML prompt using RUNE's semantic engine — delivering ~45% higher quality AI responses. Built on Spinoza'...

RUNE — Prompt Amplification Framework

RUNE transforms flat, ambiguous prompts into structured XML prompts validated by Spinoza's philosophical framework — resulting in outputs that are ~45% higher quality than raw prompting.

The 8 Layers

Layer	Name	Purpose
L0	System Core	Role, persona, behavioral rules
L1	Context Identity	Domain knowledge, target audience
L2	Intent Scope	Task definition, output format
L3	Governance	Constraints, ethical boundaries
L4	Cognitive Engine	Thinking strategy (CoT, ToT)
L5	Capabilities Domain	Tools, integrations, capabilities
L6	QA	Validation criteria, quality control
L7	Output Meta	Format, style, length, language

Requirements

• Python 3.11+
• RUNE repo cloned locally
• RUNE_API_KEY in ~/.secrets

Usage

# Pipe a prompt
echo "Write a blog post about AI" | bash main.sh

# Pass as argument
bash main.sh "Explain quantum entanglement to a 12-year-old"

Setup

# 1. Clone RUNE repo
git clone https://github.com/mrsarac/master-prompts ~/Documents/GitHub/rune

# 2. Add API key to ~/.secrets
echo "export RUNE_API_KEY=your_key" >> ~/.secrets

# 3. Test
echo "Hello" | bash main.sh

Source

• Author: NeuraByte Labs
• Version: RUNE v4.3 / WAND v1.5.0
• Repo: https://github.com/neurabytelabs/rune-skill