Skill UI

A comprehensive reference for managing Uncloud, a decentralized self-hosting platform. This guide details using the `uc` CLI to deploy services, configure Caddy ingress, manage ports (HTTP/HTTPS/TCP/UDP), scale workloads, and inspect logs across a WireGuard mesh cluster. Ideal for setting up resilient, self-owned microservices infrastructure.

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.

Builds a comprehensive content cluster plan using seed keywords. It generates pillar+spokes architecture, intent grouping, and an internal-link map, providing a structured, prioritized content brief ready for execution. Ideal for establishing topical authority, auditing for cannibalization, or scaling programmatic SEO.

This guide outlines how to structure a rigorous robustness program for economic policy manuscripts. It emphasizes defending the overall policy takeaway, rather than merely reporting coefficient significance. Users learn to address common econometric threats—such as specification changes, pre-trends, multiple testing, and selection on unobservables—using advanced techniques (e.g., Wild-cluster bootstrap, Oster bounds) to ensure the policy conclusion is stable and credible.

A comprehensive guide on positioning academic manuscripts within the broader field conversation, specifically for general education-research journals. It teaches researchers how to move beyond niche citation clusters to establish a field-wide gap, articulate the stakes, and connect the study to adjacent subfields, ensuring the contribution is broadly relevant.

This guide outlines the rigorous execution and reporting standards required for quantitative research intended for top-tier academic journals, such as APSR. It covers best practices for ensuring reproducibility, assessing robustness, reporting uncertainty honestly (using credible intervals), and correctly applying advanced statistical inference (e.g., clustering, randomization inference). It is essential for researchers conducting empirical work in political science, economics, or social sciences.

This guide provides strict guidelines for compiling and formatting scholarly exhibits (tables, figures, and appendices) for top-tier accounting journals, such as CAR. It ensures compliance with academic style guides, requiring variable definitions, clustered standard errors, and self-contained presentation so that the results are immediately interpretable by reviewers. It is crucial for transforming raw analysis output into submission-ready academic materials.

A detailed guide for scrutinizing statistical inference in high-level econometric manuscripts, designed to withstand rigorous technical peer review. It covers critical areas such as selecting appropriate clustering levels, handling weak instrumental variables (IV), correcting for multiple hypothesis testing, and ensuring accurate standard error reporting for panel data and causal inference models.

This skill provides rigorous standards and best practices for running and reporting analyses within Comparative Political Studies (CPS). It addresses common pitfalls in cross-national data, such as measurement error, few clusters, and spurious correlation, ensuring the resulting research is transparent, robust, and replication-ready for academic review.

A comprehensive guide for ensuring rigorous statistical inference in experimental economics. It covers critical areas such as power justification, determining the correct unit of observation (cluster-robust SEs), handling multiple comparisons, and applying non-parametric tests. This framework helps researchers transition from preliminary findings to statistically robust, publishable conclusions.

This guide outlines the rigorous methodological standards required for empirical finance manuscripts. It covers essential details from sample construction (attrition tables, survivorship bias) to variable definition, merge integrity, and appropriate statistical inference (clustering, panel structure). Use this to harden your data layer and ensure your methodology is robust enough to pass peer review in top finance journals.