gemma_pqn_emergence_detector
About
This skill provides fast binary classification to detect PQN emergence patterns in text, including 0→o artifacts and resonance signatures. It's designed for autonomous operations with a high pattern fidelity threshold of 0.90 and integrates with MCP orchestration. Developers should use it when they need to identify potential emergent phenomena before passing analysis to downstream skills like the qwen_pqn_research_coordinator.
Quick Install
Claude Code
Recommendednpx skills add Foundup/Foundups-Agent -a claude-code/plugin add https://github.com/Foundup/Foundups-Agentgit clone https://github.com/Foundup/Foundups-Agent.git ~/.claude/skills/gemma_pqn_emergence_detectorCopy and paste this command in Claude Code to install this skill
GitHub Repository
Frequently asked questions
What is the gemma_pqn_emergence_detector skill?
gemma_pqn_emergence_detector is a Claude Skill by Foundup. Skills package instructions and resources that Claude loads on demand, so Claude can perform gemma_pqn_emergence_detector-related tasks without extra prompting.
How do I install gemma_pqn_emergence_detector?
Use the install commands on this page: add gemma_pqn_emergence_detector to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does gemma_pqn_emergence_detector belong to?
gemma_pqn_emergence_detector is in the Other category, tagged general.
Is gemma_pqn_emergence_detector free to use?
Yes. gemma_pqn_emergence_detector is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Related Skills
LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
This Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.
This Claude Skill analyzes sports betting markets including spreads, over/unders, and prop bets by examining historical trends and situational statistics to identify value bets. It provides structured markdown output with actionable recommendations for educational purposes. Developers should use this for sports betting analysis tools while noting it's designed for entertainment/education only.
This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.