Skill UI

This skill serves as a compatibility test to ensure that skills compiled from MDA (Metadata Definition Architecture) sources are correctly recognized and installed by the OpenClaw platform. It validates the integrity of the skill loading process, confirming successful parsing and integration.

This comprehensive canvas guides teams through developing robust AI-powered features. It ensures product decisions are rigorous by covering problem definition, model selection (LLM, RAG, etc.), data governance, evaluation metrics, user experience design (including fallbacks), and responsible AI compliance. Ideal for assessing AI readiness and preventing common product failures.

This skill generates comprehensive, production-ready database schema documents. It covers Entity-Relationship (ER) diagrams, detailed SQL DDL definitions, constraint analysis, index strategies, and access pattern optimization. Use it when designing a new database, documenting an existing one, or modeling complex data relationships for engineering review.

This comprehensive checklist guides authors submitting theory-only manuscripts to the Academy of Management Review (AMR). It ensures compliance with AOM formatting, rigorous anonymity standards, correct scope definition (must build new theory, no data), and ethical guidelines, minimizing desk rejection risk. It is designed for conceptual, non-empirical work.

A comprehensive guide for finalizing academic manuscripts in finance and economics. It enforces strict formatting rules for regression tables (three-line style, standard error presentation), requires detailed variable definition tables with calculation formulas, mandates specific data source citation, and ensures high-resolution, professionally designed figures, moving beyond simple copy-pasting of results.

A comprehensive guide for standardizing the presentation of findings in academic manuscripts, particularly for social science and public administration journals. It covers strict formatting rules for quantitative regression tables (three-line style, max 6 columns), variable definition sheets, qualitative data coding tables, and process/mechanism diagrams, ensuring methodological rigor and compliance with high publication standards.

A comprehensive guide detailing the rigorous formatting standards for tables and figures required for submission to the China Rural Economy journal. It covers three-line table style, strict column limits, variable definition protocols, mandatory data source citation, and specific figure aesthetic requirements (e.g., DPI, color usage), ensuring research output meets high academic standards.

Provides stringent guidelines for formatting tables and figures in high-level economics and finance manuscripts. Covers mandatory three-line table styles, column count limits, rigorous variable definition requirements (including naming specific databases), data source transparency, and professional figure generation techniques (e.g., 300 dpi, proper event study plots).

This skill provides a comprehensive framework for authors aiming to submit to the prestigious journal 'E-Government.' It guides users on assessing manuscript fit, aligning research with the journal's focus on digital governance, data element, public value, and administrative reform. It covers scope definition, required methodology, structural adjustments, and mandatory formatting checks.

A comprehensive guide for academic researchers on structuring the 'Data and Sample' section of manuscripts for high-impact journals. It details requirements for database naming, variable definition using calculation formulas, descriptive statistics reporting (including winzorization and quantiles), and establishing full, reproducible sample selection trails. Essential for ensuring methodological rigor and passing peer review.

This comprehensive guide provides rigorous standards for formatting tables and figures for high-impact academic journals (e.g., Economic Research). It covers best practices including three-line table styles, variable definition structures, data source documentation, and figure aesthetics (DPI, color). Essential for ensuring submitted manuscripts meet professional publishing quality.

This skill provides a strategic guide for authors evaluating if their AI/data mining manuscript is a suitable fit for the ECML PKDD conference. It covers scope definition, evidence requirements, academic framing, and strategic positioning relative to major global venues (e.g., ICML, NeurIPS). Use this to refine your submission narrative and maximize your chances of acceptance.