Skill UI

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.

A comprehensive guide for authors targeting the NAACL conference. It helps strategize the fit, framing, and rigor required for NLP and Computational Linguistics manuscripts. Use this to structure your paper, understand the evidence bar, and position your work for a top-tier conference audience.

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 assists researchers in determining the topic suitability, appropriate format (research paper, review, special issue), and correct academic placement for manuscripts targeting 'Pattern Recognition and Artificial Intelligence' (PR&AI). It outlines the journal's focused scope across core AI domains like ML, CV, and NLP, helping authors avoid common submission pitfalls and ensuring their contribution has sufficient originality and academic depth.

This comprehensive audit checklist is designed for authors submitting papers to the ACM RecSys conference. It guides users through selecting the correct specialized track (Main, Industry, Reproducibility), adhering to strict page budgets, ensuring thorough double-blind anonymization, and mitigating common submission risks like identity leaks and improper data splitting.

This comprehensive guide helps authors determine if their robotics manuscript is suitable for the RoboCup Symposium. It provides specialized advice on conference positioning, required evidence (e.g., league performance, benchmarks), and framing for competitive robotics domains like robot soccer, rescue, and service robots. It emphasizes grounding the contribution in empirical, competition-based evidence rather than general theoretical work, serving as a fit and re-framing tool for high-level CI/AI submissions.

This comprehensive guide helps authors determine the suitability and optimal presentation of their robotics scientific manuscripts for the Robotics: Science and Systems (RSS) conference. It provides strategic advice on conference positioning, defining the evidence bar, re-framing algorithmic or theoretical contributions, and addressing the specific needs of top-tier embodied systems reviewers.

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.

This skill provides a comprehensive guide for manuscript authors targeting the StarSem Conference on Computational Semantics (*SEM). It helps authors determine the conference fit, refine framing, establish the necessary evidence bar, and strategize submissions for topics related to meaning representation, semantic parsing, and inference. It is a specialized venue-selection and re-framing tool for advanced NLP/CS papers.