Skill UI

This skill guides authors on how to strategically position their machine learning research against complex academic landscapes. It covers comparing work to neighboring conferences (ICML, ICLR), analyzing contemporaneous preprints, and defining the exact technical delta from prior methods to minimize novelty risk for top-tier AI venues.

This skill guides researchers through the rigorous validation process of their AI/ML paper for NeurIPS. It helps determine if the core contribution is robust enough for the main track, suggests the most appropriate NeurIPS track (e.g., E&D, Position), and refines the contribution type (Theory, Use-Inspired, etc.) to maximize the chances of acceptance.

A comprehensive guide for refining machine learning research papers tailored for NeurIPS submission. It teaches how to structure introductions, calibrate claims with evidence, explicitly state limitations, and address mandatory academic checklist items (e.g., reproducibility, broader impact). Use this skill to transform technical writing into a reviewer-proof, high-impact academic manuscript.

This plugin integrates the OpenViking context database, providing powerful long-term memory, session archiving, and semantic recall for OpenClaw agents. It enables agents to query, debug, and manage persistent knowledge bases, significantly enhancing conversational depth and complex tool usage.

This skill allows agents to retrieve and inject relevant operational memories from the OpenViking experience library into the current prompt context. It is designed for complex, multi-step tasks involving tool usage, code operations, or decision-making workflows. By searching and reading past successful executions, the agent improves context awareness and decision accuracy, moving beyond simple Q&A.

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.

This guide teaches how to structure the "Related Work" section for top-tier AI journals, focusing on demonstrating the novel incremental contribution (delta) rather than simply listing existing literature. It covers positioning the work within ML/CV/NLP, defining novel problem dimensions, and structuring rigorous comparison tables to withstand external review scrutiny.

This guide provides a highly strategic protocol for drafting the single-page rebuttal memo required for top-tier academic conferences, such as RSS. It teaches authors how to tailor their response for the Area Chair (AC), focusing on maximum impact with minimal words. Key strategies include prioritizing critical objections, citing evidence using coordinates (e.g., Table 3, row 4), and making controlled scope concessions rather than re-running experiments or thanking reviewers extensively. This maximizes the chances of influencing the final review decision.

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.

A comprehensive guide detailing the highly structured, multi-stage peer review process used by top-tier robotics conferences (such as RSS). It covers the mechanics from double-blind submission and threshold gating to the role of the Area Chair. Learn how to structure your arguments, write compelling rebuttals, and summarize findings to maximize acceptance chances in highly selective academic venues.

A comprehensive strategic guide for authors submitting to the SIGDIAL Conference on Discourse and Dialogue. It helps evaluate if a dialogue system manuscript is suitable for the venue, refining the paper's narrative, strengthening empirical evidence, and understanding the specific requirements for discourse and conversational AI research to maximize submission success.

A specialized guide for authors aiming to submit to the Conference on Uncertainty in Artificial Intelligence (UAI). It provides expert advice on framing the core contribution—such as probabilistic reasoning, causal inference, and uncertainty modeling—and calibrating the manuscript for highly technical, statistical ML reviewers. Use this skill to refine your submission strategy, identify evidence gaps, and properly position your work relative to other top-tier AI venues like AISTATS and IJCAI.