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Use when user needs ML model deployment, production serving infrastructure, optimization strategies, and real-time inference systems. Designs and implements scalable ML systems with focus on...
Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions...
Development skill from everything-claude-code
Development skill from everything-claude-code
Development skill from everything-claude-code
Development skill from everything-claude-code
Development skill from everything-claude-code
Development skill from everything-claude-code
Design and implement a complete ML pipeline for: $ARGUMENTS
Design and implement a complete ML pipeline for: $ARGUMENTS
Design and implement a complete ML pipeline for: $ARGUMENTS
[Alpha] エンタープライズ Azure 環境構築スキル。AVM 活用、VM アプリ初期化、App Service/AKS/Container Apps へのアプリデプロイ、サービス間設定連携、CI/CD 生成をサポート。Hub-Spoke, AKS, AI Foundry 等の複合アーキテクチャに対応。Bicep MCP と Microsoft Learn Docs MCP で最新スキーマを取得。
Create organized, visual study notes with folder structures, diagrams, and example-based learning from source materials (PDFs, lecture notes, documentation). Use when creating structured learning...
Start a new learning episode in the self-learning memory system with proper context. Use this skill when beginning a new task that should be tracked for learning from execution patterns.
Log entries to the current learning session. Use mid-session to save progress, capture concepts learned, or record corrections. Triggers on "log this", "save progress", "record what we learned".
End the current learning session. Use when done studying to generate summary, update learning plan, and log final entries. Triggers on "end session", "done learning", "finish studying", "wrap up".
Start a new learning session on a topic. Use when user wants to learn something new, begin studying, get taught a topic, or start a teaching conversation. Triggers on "teach me", "let's learn",...
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on...