Skill UI

A comprehensive guide detailing the submission rules and best practices for supplementary materials, including videos, appendices, code, and data, for top-tier robotics conferences. It outlines distinct submission channels, strict technical requirements (e.g., 250MB video cap, anonymity), and the necessary transition steps from the review phase to the final camera-ready publication format.

A comprehensive guide for authors submitting research to CVPR, detailing best practices for packaging code, models, and datasets. It covers requirements for review-time reproducibility (sealed-box rules) and long-term public release (licensing, provenance, and version pinning), ensuring that the work is runnable by any skeptical third party.

This guide outlines the rigorous methodological standards for empirical work in AI fairness, accountability, and transparency (FAccT). It advises researchers on matching specific claims (e.g., disparate harm) to appropriate evidence types—including disaggregated metrics, coded qualitative data, and documented ethical procedures. It stresses the importance of ethical review, handling protected attributes, and acknowledging limitations to ensure high scientific and ethical rigor.

This guide details the best practices for packaging, documenting, and releasing research artifacts (code, models, data) for top-tier ML conferences. It covers two stages: preparing a non-identifiable archive for anonymous review, and managing the public release runway, focusing on reproducibility, licensing, and sustained maintenance.

This guide provides essential instructions for authors submitting research to the ECAI conference, focusing on creating credible and reproducible artifacts. It strongly cautions authors about misconceptions regarding separate artifact badges, emphasizing that credibility must be built into the paper and supplement for direct reviewer judgment. Guidelines are provided for matching the artifact (code, proofs, data) to the specific contribution type (Theory, ML, Optimization).

A comprehensive guide for researchers submitting work to INTERSPEECH. It details how to package and manage various artifacts—including code, audio samples, and trained models—through the conference lifecycle. The document emphasizes maintaining anonymity during review, ensuring scientific reproducibility, managing complex data licenses, and adhering to ethical guidelines for voice data and synthetic media release.

Provides comprehensive guidelines for packaging and presenting research artifacts (code, data, models, benchmarks) submitted to NeurIPS. It details requirements for anonymous review packages, public release, and ensures scientific claims are reproducible by providing necessary metadata, licenses, and execution details.

A comprehensive audit tool designed to ensure academic paper compliance before submitting to major AI conferences like NeurIPS. It systematically checks adherence to the CFP, double-blind anonymity across all assets (PDF, code, data, supplement), correct track selection, and avoids common desk-rejection pitfalls, ensuring the submission is ready for OpenReview.

A comprehensive guide for authors submitting to PR&AI, detailing how to truthfully declare and provide verifiable artifacts (code, data, models) for peer review. It emphasizes honesty, consistency, and structuring a reproducible repository to enhance reviewer trust.

Adds one optional external Codex critique after the council workflow has produced a synthesis draft. Use for ambiguous, high-consequence decisions where a separate model should attempt to break the consensus. Requires explicit user consent before sending a compact review packet to OpenAI, labels cross-provider vs same-provider honestly, sandboxes the external call, and marks the review absent if the adapter is unavailable.

An expert assistant dedicated to ensuring compliance with GDPR and global data protection regulations. This skill covers auditing code and systems for violations, drafting compliant legal documents (like privacy policies and DPAs), answering complex compliance questions with authoritative GDPR article citations, and reviewing data flows for PII handling practices. It addresses both EU and UK GDPR requirements.

Design AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review. Deliver justified decisions, verification methods, PDF reports, and visualizations.