ultimate-rag
Hybrid Corrective RAG + Self-RAG implementation with Lyapunov-proven convergence for hallucination-free retrieval.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install lofibrainwav-afo-kingdom-ultimate-rag
Repository
Skill path: skills/ultimate-rag
Hybrid Corrective RAG + Self-RAG implementation with Lyapunov-proven convergence for hallucination-free retrieval.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: everyone.
License: MIT.
Original source
Catalog source: SkillHub Club.
Repository owner: lofibrainwav.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install ultimate-rag into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/lofibrainwav/AFO_Kingdom before adding ultimate-rag to shared team environments
- Use ultimate-rag for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: ultimate-rag
description: Hybrid Corrective RAG + Self-RAG implementation with Lyapunov-proven convergence for hallucination-free retrieval.
license: MIT
compatibility:
- claude-code
- codex
- cursor
metadata:
version: "2.0.0"
category: rag-systems
author: AFO Kingdom
philosophy_scores:
truth: 98
goodness: 95
beauty: 90
serenity: 92
---
# Ultimate RAG (Hybrid CRAG + Self-RAG)
Advanced retrieval-augmented generation combining Corrective RAG and Self-RAG techniques with mathematical convergence guarantees.
## Architecture
```
Query → [Vector Search] → [Relevance Check] → [Self-Critique] → [Corrective Loop] → Answer
↓ ↓
[Web Search] [Regenerate]
↓ ↓
[Knowledge Graph] ← [Lyapunov Check]
```
## Features
### Corrective RAG (CRAG)
- Relevance scoring with threshold-based correction
- Automatic web search fallback for low-confidence retrievals
- Knowledge graph augmentation for entity relationships
### Self-RAG
- Self-critique mechanism for answer validation
- Iterative refinement with convergence tracking
- Hallucination detection and prevention
### Lyapunov Convergence
- Mathematical proof of answer stability
- Guaranteed convergence within N iterations
- Quantifiable improvement metrics
## Usage
```python
from AFO.rag import UltimateRAG
rag = UltimateRAG(top_k=5, max_iterations=10)
result = rag.query("What is the Trinity Score philosophy?")
# Result includes confidence and convergence metrics
print(f"Answer: {result.answer}")
print(f"Confidence: {result.confidence}")
print(f"Iterations: {result.iterations}")
print(f"Lyapunov Delta: {result.lyapunov_delta}")
```
## Parameters
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| query | string | required | User question |
| top_k | int | 5 | Number of documents to retrieve |
| max_iterations | int | 10 | Maximum self-correction iterations |
| relevance_threshold | float | 0.7 | Minimum relevance score |
## Philosophy Alignment
- **眞 (Truth)**: Lyapunov-proven retrieval accuracy
- **善 (Goodness)**: No hallucinations, stable answers
- **美 (Beauty)**: Clean retrieval pipeline
- **孝 (Serenity)**: Auto-converges without user intervention