Skill UI

This tool suite provides functionality to interact with a community gallery for WebAssembly (WASM) agents. Users can browse available agents, search for specific functionalities, install pre-built agents, publish their own creations, and export agents for packaging. It streamlines the process of sharing and utilizing modular WASM components.

Guides users through collaborative dialogue to refine ideas, explore design options, and produce a validated design specification before implementation. Ideal for new features, components, or systems with design decisions.

This skill performs a comprehensive, setup-wide health check for agentic developers. It audits various components—including skills, memory, tools, configuration, and workflow habits—to identify inefficiencies and potential improvements. It provides a detailed report and guides the user through applying targeted fixes, ensuring the agent environment remains lean and optimized.

Hallmark is a specialized design skill engineered for AI coding assistants. It enforces deep structural variety and aesthetic sophistication, ensuring generated UIs look professionally 'made' rather than generically templated. Users can invoke Hallmark to build new pages (default), audit existing designs for anti-patterns, redesign components while preserving intent, or extract the 'DNA' (structure, archetypes, color) from existing URLs or screenshots for reference.

This comprehensive tool generates professional, structured marketing agency proposals, pitch decks, and scopes of work. It guides the creation of all necessary components—including strategic analysis, service deliverables, KPI frameworks, pricing models, and terms & conditions—to help agencies formalize engagements, win new business, or expand existing partnerships.

This guide outlines the mandatory steps and components required to assemble a robust, reproducible data and code replication package for submissions to the American Economic Journal: Applied Economics (AEJ: Applied). It ensures compliance with the stringent AEA Data and Code Availability Policy, covering data provenance, master scripts, and version control. This package must be built iteratively, not at the acceptance stage.

This guide provides a methodology for integrating theoretical models into empirical economics papers, especially for journals emphasizing empirical findings (e.g., AEJ: Applied). It teaches researchers how to ensure that theoretical components—such as mechanisms, welfare statements, or counterfactuals—serve to interpret, sharpen, or guide the existing empirical estimates, rather than leading the paper or replacing the core identification provided by the design. Focus on sufficient statistics and minimal model assumptions is key.

A comprehensive guide for submitting high-quality replication packages for academic journals like the American Economic Journal (AEJ: Micro). This package covers two essential components: self-contained, verifiable proofs for theoretical claims (the Proof Appendix), and a fully reproducible code and data deposit (the Code/Data Repository). It ensures that all numerical results, simulations, and empirical findings can be regenerated by the journal's editor, maintaining the highest standards of scientific transparency.

A comprehensive pre-submission checklist designed for authors aiming to submit original research papers to high-level academic journals, such as Acta Electronica Sinica (AES). This guide covers essential academic components, including proper abstract structuring, keyword adherence, specialized referencing formats (GB/T 7714), blind review compliance, ethical data handling, and formatting standards. It minimizes the risk of rejection during the initial editorial screening process.

This skill guides researchers on the mandatory, rigorous evaluation of complex systems models (e.g., AgSy). It details best practices for model validation, emphasizing the comparison of observed vs. simulated data using independent, held-out datasets. Key components covered include standard fit statistics (RMSE, NSE), sensitivity analysis (identifying core drivers), uncertainty propagation, and multi-objective trade-off/scenario analysis to ensure scientific credibility.

This guide provides a rigorous framework for structuring Agricultural Systems (AgSy) research. It teaches users how to explicitly define system boundaries, core components, critical interactions, and hierarchical levels. Furthermore, it instructs on selecting, justifying, and describing complex models (e.g., process-based, ABM, bio-economic) to ensure the research is scientifically sound and fully reproducible.

Guides authors on how to structure and write a powerful response document (rebuttal) for academic journals after a major revision (R&R). The focus is on demonstrating genuine theoretical strengthening—not just defending existing claims. It outlines the necessary components, from summarizing theoretical moves to adjudicating reviewer conflicts, ensuring the argument is substantive and collegial.