Skill UI

This tool synthesizes multiple, scattered research findings retrieved from memory into a single, structured, and actionable report. It automatically grades evidence quality (High/Medium/Low), resolves conflicting claims, and provides concrete, recommendation-based insights, making complex data digestible for decision-making.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

A comprehensive tool for importing structured data from various sources (CSV, JSON, Google Sheets) into connected platforms like CRMs, email marketing systems, or custom data stores. It manages the entire import pipeline, including automated field mapping, rigorous data validation, deduplication against existing records, and critical compliance verification (GDPR, CCPA). Features a mandatory execution gate for risk management.

This skill automatically converts structured data from Excel (.xlsx) workbooks into a clean, readable Markdown format. It is essential for enabling accurate analysis, summarization, and data extraction of spreadsheet contents, especially when the user asks to 'read', 'analyze', or 'compare' data without specifying a conversion method. It supports both single files and batch processing of entire folders.

This comprehensive guide outlines the rigorous standards for designing, auditing, and reporting Natural Language Processing (NLP) experiments, specifically tailored for top-tier academic conferences like EMNLP. It covers critical areas including baseline fairness checks, data contamination audits, statistical significance testing, prompt sensitivity analysis, and structured human evaluation protocols, ensuring reported claims are robust, reproducible, and methodologically sound.

This guide outlines the mandatory open data and code requirements for publishing in Global Change Biology (GCB). It details that primary and secondary data, along with supporting code/software, must be archived in public, DOI-minting repositories (e.g., Dryad, Zenodo) prior to acceptance. It emphasizes that 'available on request' is not accepted and provides a structured checklist for ensuring reproducibility and proper metadata submission.

A comprehensive framework for strengthening International Relations (IR) manuscripts, particularly for top-tier journals. It guides researchers through rigorous design requirements, including causal identification in dyadic/network data, structured case selection and process tracing, experimental planning (surveys/conjoint), and formal-empirical linkages. The focus is on ruling out rival explanations and establishing robust empirical claims.

This comprehensive guide provides a structured framework for building and auditing empirical data and estimation pipelines in finance and banking research. It covers data construction from various sources (e.g., CRSP, Call Reports), advanced econometric techniques like fixed effects and winsorization, conducting stress tests (e.g., crisis periods, large banks), and ensuring full reproducibility. It emphasizes reporting results with meaningful economic magnitudes.

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.

A comprehensive guide for constructing and auditing empirical data pipelines, specifically tailored for high-impact journals. It covers advanced causal inference techniques (DID, RDD, IV, RCT), detailed sample management, robustness testing, and creating structured comparative estimates to withstand intense academic scrutiny.

A detailed guide on establishing and reporting the necessary robustness and sensitivity analyses for manuscripts in quantitative economics and econometrics. It clarifies that the required robustness differs significantly from causal inference studies, emphasizing testing against variations in method choice, parameters, data scope, and sample periods. Provides structured methodologies for generating sensitivity matrices and discussing model stability.

This skill automates the complex process of transforming raw research papers (PDF or URL) into structured, knowledge graph-ready resources for OpenViking. It executes a rigorous workflow: reading the entire paper, compiling an ARA-style artifact directory, validating the content against established schemas, and finally ingesting the structured data into the OpenViking knowledge base using the `ov add-resource` command. It ensures all evidence, claims, and logical relationships are captured systematically.