marp-image-generator
Generate optimized images for Marp slides using Playwright MCP with theme-matching color palettes and guideline-compliant sizes
Generate optimized images for Marp slides using Playwright MCP with theme-matching color palettes and guideline-compliant sizes
とまだの文字起こし・音声入力(Whisper等)の誤変換・誤字脱字を自動修正するスキル。AI駆動開発、Claude Code、MCP、プログラミング用語の誤変換を専門的に修正。「文字起こしを修正」「誤変換を直して」「誤字脱字を修正」「音声入力を整理」「テキストをクリーンアップ」「変換ミスを直して」と言われたときに使用。Obsidianノートの文字起こし整理、技術用語の表記統一に最適。Use PROACTIVELY when fixing transcriptions, correcting voice input errors, or cleaning up Whisper output.
Enhanced docs validation with AI-powered features. Enhanced with Context7 MCP for up-to-date documentation.
End-to-end machine learning pipelines on Databricks including data exploration, feature engineering, model training with hyperparameter optimization, MLflow experiment tracking, model registration to Unity Catalog, and deployment as DABs. Use when building ML workflows, training models, or deploying ML pipelines.
Layer 1 Real-Time Social Stream Monitoring via MCP with DuckDB persistence
Manage Unity Catalog resources including catalogs, schemas, and tables. Handles discovery, creation, updates, and deletions with proper naming conventions and governance. Use when exploring catalogs, creating schemas, managing tables, or setting up data governance.
Execute code on Databricks clusters using MCP Command Execution API. Supports stateless quick validation and stateful iterative development. Use when testing Python/SQL code on clusters, debugging pipelines, or validating transformations.
Parallel thread/DuckLake discovery with XOR uniqueness from gay_seed. Finds "say" or MCP usage, cross-refs with all DuckDB sources, launches bounded parallel ops.
Package and deploy Databricks Asset Bundles with proper parameterization, multi-environment support, and serverless compute. Handles project structure, databricks.yml generation, validation, and deployment. Use when packaging tested code for production, deploying pipelines, or managing multi-environment deployments.
Production data engineering pipelines following medallion architecture (Bronze/Silver/Gold layers) with data ingestion, transformation, quality checks, Delta Lake optimization, and orchestration. Use when building ETL pipelines, medallion architecture, data lakes, or data transformation workflows.
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
Meta-audit skill for spellbook development. Spawns parallel subagents to factcheck docs, optimize instructions, find token savings, and identify MCP candidates. Produces actionable report.
Local Microsoft Agent Framework documentation reference. Use when asked about Microsoft Agent Framework, building AI agents in .NET or Python, MCP servers/clients, durable agents, agent tools, Teams/WebChat adapters, or agent-to-agent communication.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Semantic code search with VectorCode using embeddings for finding code by meaning, not just keywords. Use when searching for code patterns, similar implementations, concept-based search, or when keyword search fails. Automatically available via MCP.
Quick restart to reload configuration changes (skills, settings, hooks, MCP services). Use PROACTIVELY after modifying .claude/ files. Preserves conversation history.
Expert SuperClaude prompt engineering assistant that analyzes user needs and crafts optimal prompts using the full SuperClaude framework - commands, flags, personas, MCP servers, wave orchestration, parallel execution patterns, continuous execution directives, and the new PM Agent orchestration system with PDCA cycles and Serena memory integration.
LLM이 잘 설계된 도구를 통해 외부 서비스와 상호작용할 수 있게 하는 고품질 MCP(Model Context Protocol) 서버 생성 가이드. Python(FastMCP)이나 Node/TypeScript(MCP SDK)로 외부 API나 서비스를 통합하는 MCP 서버를 구축할 때 사용한다.
MANDATORY invocation for ALL LLM-related work. Invoke immediately when: - ANY mention of model names, IDs, or versions - ANY configuration of AI providers or APIs - ANY defaults/constants for LLM settings - ANY prompt engineering or modification - ANY discussion of model capabilities or features - ANY changes to AI-related dependencies - Reading/writing .env files with AI config - Modifying aiProviders.ts, prompts.ts, or similar - Reviewing AI-related pull requests - Debugging LLM integration issues CRITICAL: Training data lags reality by months. ALWAYS research first. Use WebSearch, Exa MCP, or Gemini CLI before making ANY LLM decisions.
Direct access to GitHub Copilot MCP server tools for AI-powered development assistance