SO8T

Implement comprehensive mathematical theorem proving capabilities with SFT+GRPO training, MCP/A2A agent integration, and imatrix quantization protection to surpass Boreas-phi3.5-instinct-jp in formal proof generation and scientific discovery. Use when building mathematical reasoning systems, formal verification tools, or AI-assisted theorem proving environments.

Execute GGUF quantization with imatrix protection, perform statistical benchmark evaluation with error bars, generate academic-style methodology documentation, and create comprehensive scorecards. Use when evaluating model quantization quality, comparing quantization methods, or generating publication-ready evaluation results with subagent execution and PowerShell progress visualization.

Advanced multimodal 'thinking' model pipeline with SO8T Grand Design, stability-constrained evolutionary training (EvoFreeze), and specialized OSINT/Military/Bio data collection.

Implement comprehensive mathematical theorem proving capabilities with SFT+GRPO training, MCP/A2A agent integration, and imatrix quantization protection to surpass Boreas-phi3.5-instinct-jp in formal proof generation and scientific discovery. Use when building mathematical reasoning systems, formal verification tools, or AI-assisted theorem proving environments.

Execute GGUF quantization with imatrix protection, perform statistical benchmark evaluation with error bars, generate academic-style methodology documentation, and create comprehensive scorecards. Use when evaluating model quantization quality, comparing quantization methods, or generating publication-ready evaluation results with subagent execution and PowerShell progress visualization.

Create, manage, and update multi-step execution plans for complex tasks in Cursor. Use when the user asks for a plan/roadmap/phases/checklist, or when work has multiple dependent steps, higher risk, or requires coordination. Skip for straightforward tasks. Integrates with Cursor's todo_write tool for plan tracking.

Create and execute advanced execution plans for complex AI model development incorporating 2024-2026 cutting-edge techniques (DeepSeek GRPO, manifold-constrained architectures, geometric scaling). Use when planning large-scale model training, architecture optimization, or multi-stage development workflows requiring state-of-the-art methodologies.

AEGIS v2.5開発の包括的Planモード。数学データ収集、形式証明環境構築、GRPO訓練パイプライン、MCP/A2Aエージェント開発、進捗監視を実装。SO(8)四重推論再現のための完全開発ワークフロー。

AEGISモデルのARC-Challenge評価改善とGSM8K健全性チェックのためのPlanモード。タイムアウト率・抽出失敗率分析、頑健な回答抽出、データ汚染検査、複数seed評価を実行。

Planモードの設計・作成・設定を自動化するスキル。AI開発ワークフローのPlanモードを効率的に構築し、チェックポイント管理・進捗監視・エラー回復機能を統合。SO8Tプロジェクト専用に最適化。

公式リーダーボード準拠のA/B/Cテストを実行するPlanモードスキル。Phi-3.5-mini-instruct、Borea-phi3.5-instinct-jp、AEGIS-Phi3.5mini-jpv2.4を標準化ベンチマークで比較評価し、統計的有意性を検証。

AEGISモデルのARC-Challenge改善、GSM8K健全性検証、GRPO報酬多目的化のための包括的Planモード。頑健な回答抽出、タイムアウト最適化、データ汚染チェック、複数seed評価、汎化性能向上を実装。

Enable Nobel Prize and Fields Medal level mathematical reasoning and breakthrough capabilities in SO8T models through alpha gate sigmoid control, golden ratio phi^(-2) convergence, and induced grokking phenomena. Use when implementing advanced mathematical inference and breakthrough discovery capabilities.

Develop comprehensive evolution plan for Qwen2.5-7B to SO8T/thinking model with advanced Japanese capabilities, mathematical reasoning at Nobel/Fields medal level, and integration of 2024-2026 LLM breakthroughs using Moonshot pipeline as Sunset pipeline foundation. Use when planning SO8T model evolution, Japanese capability enhancement, mathematical reasoning advancement, or integrating cutting-edge LLM research.

Integrate DeepSeek-R1 GRPO, mHC manifold constraints, geometric scaling, and SO8T quadrality inference into Sunset Pipeline for advanced LLM evolution. Use when implementing cutting-edge mathematical reasoning and inference techniques in AI model development.

Integrate Moonshot AI pipeline best practices for dataset collection, labeling, cleansing, quadrality inference thinking model development, rolling stock power management, and industry-standard benchmarking with ABC testing against base models, Sunset Pipeline, and AEGIS-phi3.5 v2.5. Use when implementing comprehensive AI model development workflows with robust infrastructure and statistical validation.

Create comprehensive implementation plans for integrating GRPO, mHC manifold constraints, geometric scaling, SO8T quadrality inference, and imatrix quantization into Sunset Pipeline for advanced LLM evolution. Use when planning complete AI model transformation with performance degradation mitigation.

Optimize Sunset Pipeline Moonshot Integration for 32GB RAM + RTX 3060 GPU environment with practical scaling, resource-efficient implementations, and achievable milestones. Use when implementing AI development with limited hardware resources.