Skill UI

This tool enables the creation and management of Dynamic Agentic Architecture (DAA) agents. Unlike static agents, DAA agents possess the ability to learn, adapt their internal behavior based on real-time performance metrics, and continuously evolve through interactions. It facilitates knowledge sharing among multiple agents, ensuring systemic, continuous improvement in complex AI systems. Ideal for scenarios requiring high adaptability and evolving intelligence.

This skill guides the operation, consolidation, and maintenance of complex working systems within Google Workspace (Docs, Sheets, Slides). It emphasizes treating files as integrated systems—such as trackers or planning hubs—requiring careful inspection, precise edits, and structural cleanup rather than simple content rewriting. Use it for consolidating scattered data, cleaning shared spreadsheets, or ensuring the correct canonical version of a document is utilized.

Collects distributed trace spans for a specific task ID, building a comprehensive, visual trace tree. Users can analyze the timing of individual spans, identify performance bottlenecks, understand agent coordination, and synthesize a narrative summary of the task's execution flow. Ideal for debugging complex multi-agent systems and improving system observability.

Repo-scan is a powerful tool designed for comprehensive auditing of large and complex codebases, especially monorepos. It performs cross-stack analysis across C++, Android, iOS, and Web technologies. Key features include classifying files (core code, third-party, build artifacts), identifying known libraries, and assessing code modules into four stages: Core, Extract, Refactor, or Deprecate. Essential for planning major refactoring efforts or inheriting legacy systems.

A comprehensive skill for ingesting, analyzing, and manipulating multi-modal video and audio content. Supports real-time desktop capture, live stream monitoring (RTSP), and file uploads (URL/local). Capabilities include advanced indexing (visual, semantic, temporal), precise moment searching, professional timeline editing (subtitles, overlays, dubbing), transcoding, and generating actionable alerts. Ideal for content creators, monitoring systems, and complex media workflows.

A comprehensive guide for bringing RuView CSI sensing nodes (ESP32-S3/C6) online. This skill covers the full lifecycle: building the embedded firmware, flashing the device, provisioning WiFi credentials, and confirming the Continuous Sensing Image (CSI) data stream. Essential for physical hardware deployment and debugging of complex sensing systems.

This guide outlines best practices for ensuring rigorous reproducibility of complex interaction claims, especially in game theory and multi-agent systems (MAS). It details required evidence for proofs, experimental setups (agents, opponents, seeds, compute), and consistency checks between the paper's claims and the provided artifacts. Essential for high-stakes academic submissions like AAMAS.

A comprehensive project management guide for submitting academic papers to the AAMAS conference. It details all critical milestones, including venue fit, evidence locking, abstract submission, double-blind review, rebuttal, and final camera-ready preparation. Essential for managing complex research involving game theory and multi-agent systems.

This skill helps authors determine if their research, which focuses on the complex interface between agricultural management and environmental outcomes, is appropriate for the journal 'Agriculture, Ecosystems & Environment.' It details the required scope, from greenhouse gas fluxes and biodiversity to soil and water quality at the field-to-landscape scale, emphasizing the necessity of environmental outcome quantification and sound ecological methodology.

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 rigorous guidelines for creating high-quality figures and tables for complex systems manuscripts, especially in agricultural science. It emphasizes shifting focus from single-factor bar charts to systemic visualizations that reveal interactions, trade-offs (e.g., Pareto fronts), model dynamics, resource flows (Sankey diagrams), and model performance (observed-vs-simulated fit). Exhibits must be self-contained, reproducible, and convey uncertainty.

This skill guides researchers on how to transform complex agricultural systems model results into actionable, honest, and scoped implications for decision-makers. It teaches authors to move beyond merely restating results by identifying the specific actors, articulating the necessary trade-offs, and clearly defining the system boundaries and uncertainty limits, ensuring the paper informs a decision rather than just demonstrating methods.