Skill UI

A comprehensive, end-to-end playbook designed for software engineers and AI agents. It provides a structured methodology to safely introduce machine learning capabilities into existing, non-ML codebases. The process covers critical stages: problem framing, data readiness, architectural decoupling (using dedicated APIs/services), baseline model implementation, and final handoff to MLOps practices (CI/CD, monitoring).

Provides comprehensive guidelines for structuring and writing academic papers for Automated Software Engineering (ASE) conferences. It teaches authors how to present an automated task, emphasize the automation design over the deep learning model, provide evidence from real subjects, and adhere to strict formatting and argument presentation rules to maximize acceptance chances.

This guide outlines the stringent standards for academic paper reproducibility in theoretical machine learning (e.g., COLT). It provides a comprehensive pre-submission audit checklist covering formal proof completeness, assumption bookkeeping, constant tracking, and ensuring numerical illustrations are fully traceable. Use this methodology to strengthen your theoretical claims and withstand rigorous peer review.

This comprehensive guide provides detailed instructions for revising academic papers, specifically in the field of learning theory, for the COLT conference. It teaches authors how to structure the argument rigorously, ensuring the model is unambiguous, mastering the informal-to-formal theorem pairing, and maintaining mathematical precision (quantifier and constant hygiene).

This comprehensive style guide is written for authors submitting research papers to the CoRL conference. It provides specific directives on structuring the narrative to satisfy two distinct audiences: machine learning theorists and robotics engineers. It emphasizes quantifying all claims with concrete evidence scales (e.g., success rates, frequency), structuring the argument within a limited page count, and properly integrating mandatory sections like Limitations.

This guide provides best practices for authors writing the Related Work section for top-tier AI conferences (e.g., ECAI). It instructs users on how to cover diverse AI lanes (symbolic, learning, multi-agent, application) for broad reviewers, employ 'Delta-first' positioning to state novelty concisely, and adhere to strict page budgets and double-blind review rules.

Provides comprehensive guidelines for packaging supplementary materials, including code, data, model weights, and benchmarks, for major machine learning conferences like ICML. It details the requirements for both the double-blind review stage (focusing on anonymization and minimal replication commands) and the final public release package (ensuring permanent public links and clear licensing).

This skill acts as a comprehensive router for the NeurIPS academic workflow. It guides users through every stage of preparing a machine learning manuscript, from initial topic and track selection to writing, mandatory paper checklist completion, OpenReview submission, handling the double-blind review process, and finalizing the camera-ready version. It helps ensure the manuscript is robust, evidence-backed, and adheres to all conference policies.

This guide details the methodology for packaging scientific artifacts (code, solvers, data, logs) for machine learning submissions. It focuses on creating double-blind, self-contained ZIP archives that allow reviewers to independently and quickly verify complex probabilistic claims, even under strict time constraints. It covers best practices for anonymity, structured README generation, and handling data/licensing.

A comprehensive guide for authors submitting to UAI (Uncertainty in Artificial Intelligence). It details how to position research by ensuring coverage across diverse academic communities, including Machine Learning, Statistics, Causal Inference, and foundational mathematics. Focuses on establishing the technical 'delta' rather than merely listing citations, helping authors avoid common review failures.

A comprehensive guide for authors submitting to SIGMETRICS, detailing how to structure the Related Work section. It emphasizes shifting from a simple citation catalog to a 'delta-first' contrast, highlighting the specific technical novelty (e.g., tighter bounds, weaker assumptions) relative to existing literature across multiple performance evaluation lanes (systems, measurement, learning, etc.).

Qiskit is a leading open-source quantum computing framework designed for building, optimizing, and executing quantum circuits. It provides comprehensive tools to run on local simulators or major cloud quantum backends, including IBM Quantum, IonQ, and Amazon Braket. Use it to develop algorithms in chemistry, machine learning, and optimization.