Skill UI

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

This powerful orchestrator coordinates the entire product development lifecycle by routing inquiries across 16 specialized sub-skills (e.g., RICE, OKRs, UX research, analytics). It acts as a central hub for both deterministic task routing and running continuous, machine-gated discovery loops. Use it when you need to systematically prioritize features, run A/B tests, or drive a goal from initial assumption to validated repository code.

Autoresearch Agent runs an autonomous optimization loop that edits a target file, invokes a provided evaluation command, keeps metric-improving commits, drops failures, and repeats indefinitely so engineers can optimize speed, bundle size, prompts, or other measurable goals overnight.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

PIP kicks in when tasks fail repeatedly or the assistant becomes passive, layering Western big-tech performance rhetoric, a systematic debugging methodology, and proactivity enforcement so every code, ops, research, deployment, or writing task is owned, verified, and driven to completion.

This powerful orchestrator guides the planning, funding, scoping, and synthesis of enterprise research across four critical domains: clinical R&D, finance, market analysis, and product usability. It acts as a central hub, routing complex inquiries to specialized sub-skills and generating structured digests with actionable insights and critical 'grill challenges' for operational rigor.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

This skill guides authors writing about programming languages, software systems, and PL applications on how to assess if their manuscript is a suitable fit for the OOPSLA conference. It provides strategies for reframing papers, defining the required evidence (empirical, formal, or tool-based), and positioning the work against established CS research standards for top-tier systems conferences.

This system acts as an orchestrator for a three-tier model team (Advisor, Workers). It is designed for complex, large-scale tasks that exceed the capability of a single model pass, requiring decomposition, parallelization, and mandatory verification loops. Use cases include researching multiple competitors, executing multi-stage plans, or managing iterative development cycles involving different specialized AI agents.

A comprehensive guide detailing the rigorous two-round, double-blind review process for submissions to the ACM SIGOPS ATC. It outlines the strategic importance of the extended abstract, the role of the shepherd, and how the process differs from other top-tier systems conferences like OSDI and SOSP. Essential reading for authors aiming to publish high-quality systems research.

A comprehensive guide detailing the end-to-end workflow for submitting research papers to top-tier academic conferences, such as the ACM SIGOPS ATC. It provides a backward planning structure, covering critical stages from initial system freezing and extended abstract writing, through multi-round peer review, rebuttal preparation, artifact evaluation, and final camera-ready submission.