Skill UI

This skill helps discover natural groupings within a large collection of semantic embeddings using the `ruvector` tool. It employs graph-based algorithms (Spectral/Louvain) to cluster code or data, revealing underlying themes, identifying outliers, and organizing vast vector sets. Ideal for analyzing codebases or large datasets to understand relationships between entries.

A utility designed to generate high-dimensional vector embeddings (384-dim) from various sources including text, code, or documents. This process converts raw data into numerical representations optimized for semantic search, similarity comparison, and clustering, utilizing ONNX and HNSW indexing for efficient retrieval.

This skill utilizes hyperbolic space embedding (Poincare ball model) to represent complex data structures with inherent hierarchy, such as dependency trees, ontologies, or module taxonomies. Unlike standard Euclidean embeddings, hyperbolic space preserves hierarchical distances efficiently, allowing for accurate distance calculations (geodesic distance) that grow logarithmically with tree depth. It is ideal for advanced knowledge graph analysis and code architecture mapping.

This plugin provides advanced vector search capabilities using two distinct paths. The large-scale path supports millions of vectors via HNSW, ideal for general corpus search. The hot-path router handles small, critical pattern routing (≤11 patterns) with sub-ms latency. Key features include RaBitQ 1-bit quantization for memory efficiency (32× reduction), hyperbolic space search for hierarchical data, and unified cross-namespace search.

A comprehensive guide detailing the entire model lifecycle for RuView. It supports multiple advanced tracking methods including camera-free pose estimation (WiFlow), camera-supervised methods using MediaPipe, robust contrastive embeddings (RuVector), and domain generalization (MERIDIAN). Covers training, evaluation, and deployment across local GPU and cloud platforms.

A structured tool for sending critical, warning, or informational alerts to internal teams via Slack or Intercom. Ideal for pushing campaign status updates, performance metric alerts, or formal approval requests to stakeholders. It supports advanced features like tiered urgency flagging, context embedding (IDs, metrics), and mandatory execution approval, ensuring all internal communication is documented and actionable.

This comprehensive guide outlines the meticulous workflow for preparing a research paper for final publication (Camera-Ready) after acceptance at major NLP conferences like ACL. It covers critical steps including de-anonymization (restoring author details and acknowledgements), integrating mandates from meta-reviews, adding required disclosures (e.g., funding, AI assistance), and ensuring all technical metadata, font embedding, and licensing comply with the permanent open-access standards of the ACL Anthology.

This comprehensive guide addresses the unique challenges of submitting research to top-tier conferences that do not include formal author-response or major revision rounds. It teaches authors a proactive strategy: how to pre-empt potential reviewer objections (e.g., novelty, scope limitations) by embedding necessary arguments, proofs, and context directly into the initial submission. Furthermore, it provides actionable plans for navigating both acceptance (camera-ready fixes) and rejection (diagnosis and strategic rerouting) without relying on traditional appeal processes.

This comprehensive guide assists researchers in refining their academic topics for submission to top-tier Chinese journals. It emphasizes three mandatory constraints: embedding the Chinese institutional context, articulating discrete marginal contributions, and defining actionable policy implications, ensuring manuscripts meet high academic and policy relevance standards.

This guide details best practices for creating high-impact tables and figures for psychological science manuscripts, adhering to APA 7th edition guidelines. It emphasizes showing effect sizes, uncertainty (like confidence intervals), and embedding visuals directly within the main text near their first discussion, moving beyond traditional end-of-document placement. It covers data visualization principles and manuscript triage.

A comprehensive guide for authors submitting to World Development (WD) and similar cross-disciplinary journals. It helps authors anticipate and proactively address the diverse and often conflicting objections from double-anonymized, multidisciplinary reviewers (e.g., methodologists vs. contextualists). The strategy covers strengthening methodology, embedding clear development contributions, and structuring the argument to minimize rejection risk.