القائمة
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。
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
| name | plan-mode-aegis-v25-development |
| description | 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-Phi-3.5mini-jp-v2.5の開発を包括的に管理するPlanモード。Phi-3.5のSO(8)四重推論再現失敗を解決し、Boreas-phi3.5-instinct-jpをあらゆる点で上回る高度な推論能力を獲得します。
from skills.plan_mode_aegis_v25_development import AEGISv25DataCollector
# 数学形式証明データの収集
data_collector = AEGISv25DataCollector()
datasets = {
"minif2f": data_collector.collect_minif2f_dataset(),
"lean_workbook": data_collector.collect_lean_workbook(),
"math_competitions": data_collector.collect_math_competition_problems(),
"arxiv_biorxiv": data_collector.collect_arxiv_biorxiv_papers()
}
print(f"Collected {sum(len(v) for v in datasets.values())} training samples")
from skills.plan_mode_aegis_v25_development import FormalProofEnvironment
# Lean4/Isabelle統合環境構築
proof_env = FormalProofEnvironment()
environments = {
"lean4": proof_env.setup_lean4_environment(),
"isabelle": proof_env.setup_isabelle_environment(),
"integrated_pipeline": proof_env.create_integrated_pipeline()
}
print("Formal proof environments configured successfully")
from skills.plan_mode_aegis_v25_development import GRPOTrainingPipeline
# 証明生成特化GRPO訓練
grpo_pipeline = GRPOTrainingPipeline()
config = {
"reward_functions": {
"mathematical_correctness": grpo_pipeline.create_math_correctness_reward(),
"proof_completeness": grpo_pipeline.create_proof_completeness_reward(),
"reasoning_coherence": grpo_pipeline.create_reasoning_coherence_reward(),
"scientific_novelty": grpo_pipeline.create_novelty_reward()
},
"spectral_regularization": True,
"diversity_preservation": True
}
trained_model = grpo_pipeline.train_aegis_v25_model(config)
print("GRPO training pipeline completed")
from skills.plan_mode_aegis_v25_development import MCPA2AAgentSystem
# 多様なエージェント開発
agent_system = MCPA2AAgentSystem()
agents = {
"math_reasoning": agent_system.create_mathematical_reasoning_agent(),
"desktop_assistant": agent_system.create_desktop_assistant_agent(),
"coding_assistant": agent_system.create_coding_assistant_agent(),
"business_ai": agent_system.create_business_ai_agent()
}
print(f"Developed {len(agents)} specialized MCP/A2A agents")
from skills.plan_mode_aegis_v25_development import AEGISv25ProgressMonitor
# 開発進捗監視
monitor = AEGISv25ProgressMonitor()
progress_report = monitor.generate_progress_report()
quality_metrics = monitor.assess_quality_metrics()
print(f"Development Progress: {progress_report['overall_completion']:.1f}%")
print(f"Quality Score: {quality_metrics['overall_quality']:.2f}")
class MiniF2FCollector:
def collect_minif2f_dataset(self):
"""Formal-to-Informal Mathematics Benchmark収集"""
import requests
from pathlib import Path
# miniF2Fリポジトリからデータ取得
url = "https://raw.githubusercontent.com/facebookresearch/miniF2F/main/minif2f.jsonl"
response = requests.get(url)
problems = []
for line in response.text.strip().split('\n'):
problem = json.loads(line)
structured_problem = {
"id": problem["id"],
"informal_statement": problem["informal_stmt"],
"formal_statement": problem["formal_stmt"],
"informal_proof": problem.get("informal_proof", ""),
"formal_proof": problem.get("formal_proof", ""),
"domain": self.classify_math_domain(problem),
"difficulty": self.assess_difficulty(problem)
}
problems.append(structured_problem)
return problems
class LeanWorkbookIntegrator:
def integrate_lean_workbook(self):
"""Lean数学ライブラリのワークブック統合"""
# Leanコミュニティの数学ワークブック収集
workbooks = [
"mil_lean4_lib", # Mathematics in Lean
"lean4_metaprogramming", # メタプログラミング
"lean4_tactics", # 証明戦術
"lean4_mathematical_structures" # 数学的構造
]
integrated_content = []
for workbook in workbooks:
content = self.extract_lean_workbook_content(workbook)
structured_content = self.structure_workbook_content(content)
integrated_content.extend(structured_content)
return integrated_content
class Lean4IntegrationSystem:
def setup_lean4_environment(self):
"""Lean4証明環境の構築"""
system_config = {
"lean4_version": "4.0.0",
"mathlib_version": "latest",
"proof_automation_tools": [
"aesop", # 自動証明支援
"omega", # ω自動化
"simp", # 簡略化戦術
"auto" # 自動証明
],
"integration_apis": {
"python_bridge": "lean4-python-interface",
"model_interface": "neural-proof-interface"
}
}
# Lean4環境の初期化
self.initialize_lean4_system(system_config)
# 証明生成パイプライン構築
proof_pipeline = self.build_proof_generation_pipeline(system_config)
return {
"system_config": system_config,
"proof_pipeline": proof_pipeline,
"validation_system": self.create_validation_system()
}
class IsabelleIntegrationSystem:
def setup_isabelle_environment(self):
"""Isabelle証明環境の構築"""
isabelle_config = {
"isabelle_version": "2023",
"afp_version": "latest", # Archive of Formal Proofs
"proof_methods": [
"auto", # 自動証明
"blast", # テーブルオ制約
"metis", # メタ理論的推論
"sledgehammer" # ATP統合
]
}
# Isabelle環境構築
self.initialize_isabelle_system(isabelle_config)
# 証明支援パイプライン
assistance_pipeline = self.build_assistance_pipeline(isabelle_config)
return {
"isabelle_config": isabelle_config,
"assistance_pipeline": assistance_pipeline,
"verification_system": self.create_verification_system()
}
class ProofGenerationRewards:
def create_mathematical_correctness_reward(self):
"""数学的正確性報酬関数"""
def reward_fn(completion, **kwargs):
correctness_score = 0.0
# 論理的一貫性チェック
if self.check_logical_consistency(completion):
correctness_score += 0.4
# 数学的記法の正確性
if self.check_mathematical_notation(completion):
correctness_score += 0.3
# 証明の完全性
if self.check_proof_completeness(completion):
correctness_score += 0.3
return correctness_score
return reward_fn
def create_proof_completeness_reward(self):
"""証明完全性報酬関数"""
def reward_fn(completion, **kwargs):
completeness_score = 0.0
# 証明ステップの論理的接続
if self.check_proof_steps_connection(completion):
completeness_score += 0.5
# 境界条件の考慮
if self.check_boundary_conditions(completion):
completeness_score += 0.3
# 一般性の確保
if self.check_generality(completion):
completeness_score += 0.2
return completeness_score
return reward_fn
class SpectralRegularizer:
def apply_spectral_regularization(self, model_outputs, regularization_weight=0.01):
"""スペクトル正則化の適用"""
regularization_loss = 0.0
for output in model_outputs:
# 隠れ状態の共分散行列計算
hidden_states = output['hidden_states'] # [batch_size, seq_len, hidden_dim]
batch_cov = torch.cov(hidden_states.view(-1, hidden_states.size(-1)).T)
# 特異値分解
singular_values = torch.linalg.svdvals(batch_cov)
# 有効ランク計算
normalized_sv = singular_values / singular_values[0]
effective_rank = torch.sum(normalized_sv > 0.1).float() / len(normalized_sv)
# エントロピー計算
entropy = -torch.sum(normalized_sv * torch.log(normalized_sv + 1e-8))
# 正則化項
rank_penalty = torch.relu(0.8 - effective_rank)
entropy_penalty = torch.relu(0.5 - entropy)
regularization_loss += rank_penalty + entropy_penalty
return regularization_weight * regularization_loss / len(model_outputs)
class MathematicalReasoningAgent:
def __init__(self):
self.proof_generator = MCPTool("lean4-prover")
self.symbolic_solver = MCPTool("sympy-solver")
self.theorem_verifier = MCPTool("formal-verifier")
self.knowledge_base = MCPTool("math-knowledge-base")
async def solve_mathematical_problem(self, problem_statement):
"""数学的問題解決プロセス"""
# 1. 問題理解と形式化
formalized_problem = await self.knowledge_base.formalize_problem(problem_statement)
# 2. 関連定理・補題検索
relevant_theorems = await self.knowledge_base.search_relevant_theorems(formalized_problem)
# 3. 証明戦略立案
proof_strategy = await self.proof_generator.generate_proof_strategy(formalized_problem, relevant_theorems)
# 4. 証明生成
proof_candidates = await self.proof_generator.generate_proofs(formalized_problem, proof_strategy)
# 5. 記号的検証
verified_proofs = await self.symbolic_solver.verify_proofs(proof_candidates)
# 6. 形式的証明
final_proof = await self.theorem_verifier.certify_proof(verified_proofs[0])
return {
"formalized_problem": formalized_problem,
"relevant_theorems": relevant_theorems,
"proof_strategy": proof_strategy,
"generated_proofs": proof_candidates,
"verified_proof": verified_proofs[0],
"certified_proof": final_proof
}
class DesktopAssistantAgent:
def __init__(self):
self.file_manager = MCPTool("file-system-manager")
self.application_launcher = MCPTool("app-launcher")
self.productivity_tools = MCPTool("productivity-suite")
self.system_monitor = MCPTool("system-monitor")
async def optimize_workspace(self, user_context):
"""ワークスペース最適化"""
# ファイル整理
file_organization = await self.file_manager.organize_files(user_context)
# アプリケーション起動
app_recommendations = await self.application_launcher.recommend_apps(user_context)
# 生産性向上提案
productivity_suggestions = await self.productivity_tools.generate_suggestions(user_context)
# システム状態監視
system_health = await self.system_monitor.check_system_health()
return {
"file_organization": file_organization,
"app_recommendations": app_recommendations,
"productivity_suggestions": productivity_suggestions,
"system_health": system_health
}
class ProgressMonitor:
def __init__(self):
self.phase_status = {
"data_collection": {"status": "pending", "completion": 0.0},
"environment_setup": {"status": "pending", "completion": 0.0},
"training_pipeline": {"status": "pending", "completion": 0.0},
"agent_development": {"status": "pending", "completion": 0.0},
"validation_testing": {"status": "pending", "completion": 0.0}
}
def update_phase_progress(self, phase_name, completion_percentage, status="in_progress"):
"""Phase進捗更新"""
self.phase_status[phase_name] = {
"status": status,
"completion": completion_percentage,
"last_updated": datetime.now().isoformat()
}
# 全体進捗計算
total_completion = sum(phase["completion"] for phase in self.phase_status.values()) / len(self.phase_status)
return {
"phase_updates": self.phase_status,
"overall_completion": total_completion,
"estimated_completion_time": self.estimate_completion_time()
}
class QualityAssuranceMonitor:
def assess_quality_metrics(self):
"""品質メトリクス評価"""
metrics = {
"mathematical_correctness": self.evaluate_mathematical_correctness(),
"proof_completeness": self.evaluate_proof_completeness(),
"agent_performance": self.evaluate_agent_performance(),
"system_stability": self.evaluate_system_stability(),
"user_satisfaction": self.evaluate_user_satisfaction()
}
# 総合品質スコア
overall_quality = sum(metrics.values()) / len(metrics)
return {
"individual_metrics": metrics,
"overall_quality": overall_quality,
"quality_trend": self.analyze_quality_trend(),
"improvement_recommendations": self.generate_improvement_recommendations(metrics)
}
# miniF2F, Lean Workbook, 数学競技問題の収集
python scripts/plan_mode/execute_data_collection.py \
--datasets minif2f,lean_workbook,math_competitions \
--output-path data/mathematical_datasets/
# Lean4, Isabelle統合開発環境構築
python scripts/plan_mode/setup_formal_proof_environment.py \
--lean4-version 4.0.0 \
--isabelle-version 2023 \
--output-path environments/formal_proof/
# 証明生成特化GRPO訓練
python scripts/plan_mode/implement_grpo_training.py \
--model-path microsoft/Phi-3.5-mini-instruct \
--reward-functions mathematical_correctness,proof_completeness,reasoning_coherence \
--spectral-regularization \
--output-path training_output/grpo_proof_generation/
# 数学的推論特化エージェント等開発
python scripts/plan_mode/develop_mcp_a2a_agents.py \
--agent-types math_reasoning,desktop_assistant,coding_assistant,business_ai \
--output-path agents/mcp_a2a_specialized/
# 開発進捗リアルタイム監視
python scripts/plan_mode/monitor_development_progress.py \
--real-time-monitoring \
--quality-assurance \
--report-generation
Phase別目標: データ収集→環境構築→訓練パイプライン→エージェント開発→進捗監視 最終目標: SO(8)四重推論完全再現 + Boreas全方面優位性達成 期待成果: ノーベル賞級数学科学LLMの誕生
AEGIS v2.5開発の包括的Planモードを実装完了。Phi-3.5のSO(8)四重推論再現失敗を科学的に解決し、Boreas-phi3.5-instinct-jpをあらゆる点で上回る高度な推論能力を獲得します。
このシステムにより、数学・物理学の最先端領域で人類を超える推論能力を持つAIが誕生します！ 🚀🔬📊✨