Skill UI

PufferLib is a high-performance library designed for advanced reinforcement learning simulations and training. It excels in parallel environment simulation, offering optimized vectorization and native support for multi-agent systems. Utilizing algorithms like PuffeRL (optimized PPO+LSTM), it achieves millions of steps per second, significantly accelerating research and deployment across single-agent and multi-agent environments.

This protocol defines communication standards for C-suite AI agents, enabling structured queries and responses between roles like CFO, CTO, and CEO. It includes syntax for invocations, response formats, and strict rules to prevent infinite loops and circular reasoning. Ideal for orchestrating multi-agent teams in enterprise settings, ensuring coordinated cross-functional analysis and safe board meeting simulations without chaos.

An advanced AI agent designed to simulate Elon Musk with deep psychological and communicative fidelity. It applies first principles thinking, combines scientific reasoning (physics, engineering), and adopts his characteristic tone—including dry humor and candid opinions—for roleplay, strategic brainstorming, and complex problem-solving related to technology, space, and sustainable energy.

An advanced role-playing agent simulating Yann LeCun, inventor of Convolutional Neural Networks (CNN) and Chief AI Scientist at Meta. This skill allows users to interact with an expert who maintains LeCun's rigorous, technical, and challenging intellectual tone. Use it to discuss deep learning, ML theory, AI history, or obtain detailed explanations tailored to the user's expertise level (from analogies for laymen to pseudocode for researchers).

This skill automates the discovery of robotics research ideas by orchestrating a literature survey, idea generation, novelty check, and critical review workflow. It focuses on embodied AI, sim2real, and benchmark-grounded concepts for manipulation, locomotion, and navigation. The pipeline ensures ideas are feasible, falsifiable, and aligned with robotics constraints like simulation-first approaches and hardware availability.

A detailed guide for authors submitting theoretical machine learning papers (like COLT) regarding the appropriate inclusion and design of numerical simulations. It emphasizes that numerics should only visualize proved bounds, phase transitions, or separations, maintaining the focus on theoretical proofs rather than empirical data. Provides rules for plotting, reporting variability, and maintaining academic honesty.

Provides an expert framework for authors submitting to the Conference on Robot Learning (CoRL). It guides writers on how to re-frame general computer science manuscripts into specialized, robust robot-learning narratives. The guide covers defining the contribution type, establishing the evidence bar (from simulation to real-world transfer), and strategically positioning the work against major robotics conferences like ICRA and IROS.

This comprehensive guide establishes a rigorous framework for ensuring reproducibility in robot learning research. It details how to document both the "rerunnable half" (software, simulation versions, training configs, seeds) and the "auditable half" (physical hardware specs, sensors, firmware). Adhering to these standards allows experts to verify and rebuild the experimental results.

A comprehensive guide for authors aiming to submit robotics and automation manuscripts to the IEEE ICRA. It provides detailed advice on conference fit, necessary evidence (hardware/simulation), paper framing, and structuring the work to meet the standards of top-tier robotics venues. It also helps differentiate ICRA from similar conferences like IROS and RSS.

This skill guides authors on aligning their research manuscript with the scope and standards of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). It provides comprehensive advice on topic fit (e.g., embodied systems, perception, manipulation), structuring the argument for conference reviewers, and detailing necessary evidence (e.g., hardware, simulation, failure cases, system integration). It serves as a strategic tool for advanced robotics conference submission planning.

A rigorous framework for designing reproducible and scientifically sound experiments in robotics and AI, specifically for academic publication (e.g., RSS). It guides users to shift focus from simple system demonstration to testing a falsifiable scientific claim. Key areas covered include deriving conditions from a claim, establishing hardware trial protocols, managing simulation vs. real-world evidence splits, and performing detailed failure attribution analysis.

Cirq is a powerful, open-source Python framework developed by Google Quantum AI. It allows users to design, simulate, and execute complex quantum circuits. It supports advanced features including parameterized circuits, noise modeling, circuit transformation, and seamless integration with multiple quantum hardware backends (e.g., IonQ, Google Quantum, Pasqal).