Skill UI

This tool provides comprehensive on-chain analysis for DeFi protocols, allowing users to track metrics like Total Value Locked (TVL), fee revenue, DEX volumes, and yield opportunities. It is essential for DeFi researchers, protocol analysts, and yield farmers who require programmatic access to deep blockchain insights using the DeFiLlama API.

This tool provides comprehensive on-chain analysis for DeFi protocols, allowing users to track metrics like Total Value Locked (TVL), fee revenue, DEX volumes, and yield opportunities. It is essential for DeFi researchers, protocol analysts, and yield farmers who require programmatic access to deep blockchain insights using the DeFiLlama API.

This skill provides comprehensive monitoring and comparative analysis of cross-chain bridges in the DeFi ecosystem. It tracks key metrics such as Total Value Locked (TVL), transaction volume, typical fees, and real-time transfer status across major networks (e.g., Ethereum, Arbitrum, Polygon). Use this tool to research bridge safety, compare transfer routes before depositing funds, or track specific transaction hashes across multiple protocols like Wormhole, LayerZero, and Stargate.

This skill automates deep packet capture (PCAP) analysis using tshark and pyshark. It is invaluable for security assessments and incident response, enabling the extraction of protocol statistics, identification of suspicious network flows (e.g., port scanning, beaconing), and comprehensive extraction of Indicators of Compromise (IOCs), including IPs, domains, and URLs. It also specializes in detecting complex anomalies like DNS tunneling.

A comprehensive framework for transforming raw observations into rigorous, evidence-bounded, and testable research plans. It guides users through establishing research questions (PICO/PECO), formulating candidate hypotheses and rival explanations, defining causal estimands, and creating preregistration-ready analysis plans, all while maintaining strict ethical and scientific guardrails.

This skill generates detailed, structured research protocols for various academic, clinical, or market research studies. It ensures all critical components—such as objectives, study design, participant criteria, methodology, data management, statistical analysis plan, and ethical considerations—are comprehensively documented, providing a robust framework for any scientific investigation.

A comprehensive guide detailing best practices for achieving research transparency and reproducibility, especially for HCI and related scientific studies. It outlines three critical layers of documentation—Protocol, Analysis, and Data—to ensure that studies can be reliably verified and built upon by the academic community.

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 details the necessary steps for achieving and reporting reproducibility in Human-Robot Interaction (HRI) studies. Since HRI involves unique elements like human participants and physical environments, reproducibility focuses on making the study protocol, robot behavior specifications, analysis code, and de-identified data transparent. Learn how to properly assemble a comprehensive replication package and ethically disclose limitations to enhance academic credibility in top-tier HRI publications.

A comprehensive guide for conducting empirical data analysis in International Relations (IR). This skill addresses specific econometric challenges inherent to IR data, such as dyadic dependence, state selection bias, and gravity modeling. It guides users through rigorous reporting standards, advanced statistical methods, and verification-readiness protocols required by top-tier academic journals.

This skill guides the planning of psychological studies to meet the highest academic standards. It provides comprehensive methodologies for performing power analysis, justifying sample sizes, and implementing preregistration protocols. Use this when initiating a new research project or developing a pre-analysis plan to ensure the study design is robust and transparent.

This skill details the rigorous methodology for defining study eligibility criteria and coding data for comprehensive meta-analyses. It emphasizes pre-specifying inclusion/exclusion rules in a protocol, developing a detailed codebook, performing double-coding, and calculating inter-rater reliability (e.g., Kappa, ICC) to ensure the reproducibility and trustworthiness of synthesis findings.