proxy-mode-reference
Reference guide for using PROXY_MODE with external AI models. Use when running multi-model reviews, understanding which agents support PROXY_MODE, or debugging external model integration issues.
Découvrez les skills tools llm ai, comparez leurs dépôts et consultez leurs instructions.
Reference guide for using PROXY_MODE with external AI models. Use when running multi-model reviews, understanding which agents support PROXY_MODE, or debugging external model integration issues.
Provides guidelines and principles for bridging the gap between simulated robotics environments and real-world hardware deployment in Physical AI.
Generate working Python/rclpy code examples for Physical AI & Humanoid Robotics textbook with tier-specific variants (Simulation/Jetson/Robot).
Vision-Language-Action model integration patterns for cognitive robotics with ROS 2.
NVIDIA Isaac Sim and Isaac ROS integration patterns for Physical AI applications.
Authoritative guidelines for writing production-grade ROS 2 (Humble) Python 3.11+ code (leveraging 3.12+ features where compatible) for Physical AI applications.
Use macOS text-to-speech via the `say` command for voice feedback, audio narration, and spoken output.
Generate 20 fun League of Legends trivia questions based on player data. Use when creating entertaining French questions about LoL performance, champions, and player stats.
Comprehensive templates, patterns, and best practices for creating Claude Code subagents and skills. Use when building new agents/skills or need reference examples for proper structure and formatting.
SubAgent組み合わせパターン・選択ロジック提供。14種類のAgent定義・選択原則・Phase特性別組み合わせパターン・SubAgent責務境界判定・並列実行判断ロジック。Step開始時のSubAgent選択に使用。
Prevents generic AI-generated designs by guiding typography, color, motion, and background choices. Use when creating frontend designs, landing pages, dashboards, or any UI/UX work. Helps avoid the "AI slop" ae...
Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
Provide accurate, evidence-based answers by referencing sources from documents, textbooks, or research papers. Use when user asks for factual or source-backed responses.
Extract and explain formulas used in Physical AI and Humanoid Robotics from text, lecture notes, or papers. Use when user asks to identify or understand relevant formulas.
Summarize robotics-related text, research papers, or lecture notes into concise, clear, and structured summaries. Use when user asks to condense or simplify robotics content.
Generate clear, measurable, and curriculum-aligned learning objectives for any topic. Use when user asks to define goals, outcomes, or objectives for a lesson or course.
Create new Agent Skills with proper structure, templates, and best practices. Use when building custom skills for Claude Code, GitHub Copilot, or other Agent Skills-compatible tools.
Generate project documentation including CONTENTS.md navigation files and AGENTS.md instructions. Use when creating documentation structure, project indexes, or AI agent configuration files.
Analyze emotional tone, sentiment polarity, and psychological impact of text. Execute Python script for detailed sentiment analysis. Use when analyzing mood, attitude, or emotional content.
Comprehensive text analysis coordinator. Execute Python script for multi-aspect text analysis. Use when you need grammar, sentiment, readability, and SEO analysis combined.
This skill should be used when commands need to generate specifications or implementation plans, when the user asks about "how to write specs", "how to create plans", "spec structure", "plan hierarchy", or when...
This skill should be used when the user asks about "conductor methodology", "context-driven development", "conductor directory structure", "conductor files", "tracks.md", "plan.md", "spec.md", or when commands...
Implement ML pipelines, model serving, and feature engineering. Handles TensorFlow/PyTorch deployment, A/B testing, and monitoring. Use PROACTIVELY for ML model integration or production deployment.
Build ML pipelines, experiment tracking, and model registries. Implements MLflow, Kubeflow, and automated retraining. Handles data versioning and reproducibility. Use PROACTIVELY for ML infrastructure, experime...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.