dvcrn 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_KEYin~/.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