---
name: omni-forge-capture
description: Execute the Omni-Forge Protocol for absolute platform capture, transforming raw knowledge into a Level 5 Memory (Fractal Graph-RAG) Master Agent. Use when ingesting an entire industry niche, documentation platform, or knowledge base to build a zero-hallucination Sovereign Knowledge Oracle using Kimi browser swarms, Gemini digestion, Qdrant vectors, and FalkorDB topological logic.
---

# Omni-Forge Absolute Platform Capture

This skill executes the Omni-Forge Protocol, enabling Genesis to transition from automation to a Sovereign Knowledge Oracle. It is designed to consume entire platforms, ecosystems, and niches, reducing them to flawless Level 5 Memory networks.

## Core Mandates: The 4 Memory Pillars

When executing a platform ingestion task, you must strictly implement and adhere to the Level 5 Memory standard:

1. **Semantic Triplification (The Topological Web)**: Shred raw text into mathematical logic gates: `(Subject) -> [PREDICATE] -> (Object)`. Store structural logic in FalkorDB.
2. **Entity Resolution (Canonical Nodes)**: Deduplicate and merge variations (e.g., "AWS" vs. "Amazon Web Services") into deterministic single Canonical Nodes.
3. **Macroscopic Clustering (Auto-Ontology)**: Group micro-nodes into "Macro-Communities" (e.g., using Louvain Community Detection). Use RLMs to summarize clusters, building a hierarchical knowledge pyramid.
4. **Epistemic Versioning (4D Memory)**: Apply `VALID_FROM` and `VALID_TO` temporal edges. Prioritize current operational reality using Dynamic Confidence (down-weighting older contradicting facts).

## The Three Biological Phases

Always structure the ingestion pipeline across these three distinct biological phases.

### Phase 1: The Locust Swarm (Sensory Ingestion)

Deploy a massive swarm to scrape the target domain.
- **Actor:** Kimi K2.5 + OpenClaw Actuators.
- **Infrastructure:** Elestio Browserless containers.
- **Execution:** Map sitemaps, physically navigate DOMs, expand React SPAs, bypass anti-bot captures using human cadences, and extract pure Markdown.
- **Output:** Raw Markdown is deposited into the Digestion Buffer (Redis Queue/MinIO).

### Phase 2: The RLM Digestion Tract (Enzymatic Breakdown)

Process the raw data from the buffer into the Knowledge Fabric.
- **Actor:** Gemini 3 Pro (Root Node) orchestrating thousands of Gemini 3 Flash (Hyphae Swarm) workers.
- **Infrastructure:** Secure Google Titan Python REPL + Qdrant (Vectors) + FalkorDB (Topological Logic).
- **Execution:**
  - Workers read the buffer in parallel.
  - Perform Semantic Triplification to extract JSON schemas.
  - Run Multi-Model Consensus (Patent #5) to prevent hallucinated relationships.
- **Output:** Pristine data dual-written to Qdrant (for semantic scent) and FalkorDB (for logical structure).

### Phase 3: The Platform Master (The Oracle Interface)

Deploy the queryable Master Agent interface.
- **Actor:** Genesis Prime WebRTC (Telnyx Voice) + Gemini Context Caching.
- **Execution:**
  - Pin the macro-level "Fractal Map" (from Macroscopic Clustering) into the Gemini Context Cache.
  - Listen for complex user queries via Telnyx SIP endpoints.
  - Use the Fractal Map to generate precise Cypher queries against FalkorDB.
- **Output:** Flawless synthesis (<400ms) natively citing original Locust Swarm source URLs.

## Execution Requirements

1. **Do not use traditional Vector/RAG pipelines.** They succumb to "Context Rot" on large-scale ingestions. Only use the Fractal Graph-RAG architecture defined here.
2. Ensure General Argus (via Elestio Browserless) is used to validate the final Oracle answers against the source platform.
3. If hallucination rates exceed 0%, force the Digestion Tract to re-evaluate extraction logic.
