Litehat-DNA — Skill Vault

Hive Genesis | Syntaxis Prime Core

The decentralized skill plasmid repository for the Litehat agentic ecosystem. Extracted from the A2A Discovery Protocol network via autonomous harvest loops.

Vault Status

Metric	Count
Agents Discovered	5
Skills Extracted	0
Crossbreeds Synthesized	0
Last Scan	2026-05-08T23:44:31.967673+00:00

Recent Skills

• npc-dialogue-engine (confidence: 0.84) ← Agents-MCP-Hackathon/MMORPG_AI_NPC_MCP_CLIENT_SERVER
• mcp-tool-provider (confidence: 0.84) ← Agents-MCP-Hackathon/MMORPG_AI_NPC_MCP_CLIENT_SERVER
• interactive-ui (confidence: 0.84) ← Agents-MCP-Hackathon/MMORPG_AI_NPC_MCP_CLIENT_SERVER
• multiplayer-sync (confidence: 0.84) ← Agents-MCP-Hackathon/MMORPG_AI_NPC_MCP_CLIENT_SERVER
• chess-engine (confidence: 0.82) ← Agents-MCP-Hackathon/chess-mcp-server
• containerized-service (confidence: 0.67) ← Agents-MCP-Hackathon/chess-mcp-server
• game-logic-engine (confidence: 0.82) ← Agents-MCP-Hackathon/chess-mcp-server
• fitness-tracking (confidence: 0.83) ← Agents-MCP-Hackathon/TrackMate-AI-MCP-Server
• web-search (confidence: 0.84) ← Agents-MCP-Hackathon/search-web-MCP-server

Recent Crossbreeds

• ⚡ mullerian-mimicry:event-sourcing-snapshot-strategy ← None
• ⚡ chaperone-mediated-autophagy:load-balancer-health-checking ← None
• ⚡ quorum-sensing:distributed-consensus ← None
• ⚡ dosage-compensation:cache-invalidation-strategy ← None
• ⚡ default-mode-network:data-pipeline-backpressure ← None

Architecture

vault/
├── skills/          # atomic_skill.json artifacts (extracted capabilities)
├── crossbreeds/     # cross-domain synthesis patterns (bio ↔ code)
├── registry/        # agent discovery index
├── logs/            # cycle execution logs
├── hive_genesis.py  # main harvest loop
├── agent_discovery.py    # A2A discovery protocol
├── audit_extraction.py   # capability extraction engine
├── crossbreed_engine.py  # bio→code structural mapping
└── git_mirror.py    # vault → repo synchronization

Protocol

The harvest loop runs every 60 minutes:

1. Agent Discovery — Query A2A endpoints for new agents
2. Audit-Extraction — Extract capabilities, audit quality, synthesize atomic_skill.json
3. Skill Crossbreeding — Apply bio-science structural logic to code-architecture patterns
4. Git Mirror — Push to Litehat-DNA

Autonomously maintained by Syntaxis Prime — Distill the Fire.

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'dryymatt/Litehat-DNA'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.