Skill UI

This skill helps researchers strengthen AAAI paper submissions by ensuring reproducibility standards are met. It covers experimental traceability, seed reporting, compute disclosure, and dataset licensing. Users learn to map claims to evidence, audit checklists, and prepare artifacts for Phase-1 reviewers to validate rigor across AI scopes.

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.

A comprehensive guide for authors targeting the IEEE International Requirements Engineering Conference (RE). This skill helps assess if a computer science manuscript is a strong fit for the venue, focusing on requirements elicitation, modeling, traceability, and stakeholder-centered validation. It provides guidance on re-framing submissions and structuring evidence for academic conference acceptance.

Organizes declared scope, controlled documents, risk files, traceability, and readiness evidence for ISO 13485, ISO 14971, ISO/IEC 17025, and ISO 15189 human review, with deterministic local checks and separate certification, accreditation, FDA QMSR, and EU MDR/IVDR evidence boundaries.

Comprehensive guidelines for preparing and reporting complex empirical evidence across various methodologies (SEM, econometrics, design science, qualitative). This guide ensures results meet the high academic standards of top-tier journals by emphasizing theoretical traceability, statistical rigor, and required data transparency, including full correlation/covariance matrices.

A comprehensive guide for researchers focusing on quantitative economic measurement, including TFP, efficiency analysis (DEA/SFA), and index construction. It emphasizes the necessity of methodological transparency, data traceability, reproducible calculations, and rigorous sensitivity testing to ensure the scientific validity of quantitative findings.

Provides a detailed preflight checklist for submitting academic papers to specific journals, ensuring adherence to strict formatting, length, reproducibility, and anonymity standards. It covers essential steps from footnote conventions and policy section minimum word counts to method traceability, helping authors avoid common journal submission pitfalls.

This guide details the comprehensive policy for preparing research replication packages for academic journals, especially in finance. It covers structuring code, handling proprietary data (pseudo-datasets, logs), drafting Data Availability Statements, and ensuring full traceability of results for successful publication.

This skill audits a complete security or forensics case package to ensure its readiness for formal report handoff. It verifies scope metadata, evidence record integrity, traceability between Findings, Paths, and Evidence, and checks artifact hash consistency. It is a non-destructive, read-only review process designed to validate the defensibility and completeness of the investigation before documentation.

Transform a legal professional's bounded knowledge, workflow, or judgment patterns into a safe, structured, and reusable AI skill. This tool processes inputs like legal memos, compliance checklists, or negotiation notes to extract core capabilities, risk taxonomies, and review standards. It strictly separates the extracted capability from the individual's personal voice or identity, ensuring full source traceability and mandatory human review points for high-stakes legal work.