Skill UI

This tool initializes a structured multi-agent swarm with built-in anti-drift configuration. It is designed for orchestrating complex, multi-file tasks that require coordinated efforts from three or more specialized AI agents, such as feature implementation, cross-module refactoring, or comprehensive security audits. It supports various topologies (hierarchical, mesh, etc.) to ensure robust and manageable parallel work execution across large teams.

Ruflo is a powerful, cross-agent orchestration layer designed to manage complex, multi-step AI coding workflows. It supports coordinating large swarms of AI agents, managing persistent memory through semantic search, and executing sophisticated pipelines using 30+ specialized plugins. Ideal for large-scale development tasks, system benchmarking, and deep research requiring coordinated agent effort.

A comprehensive guide for authors aiming to publish agronomy or farming systems research in journals focused on sustainability. It outlines the critical need for making sustainability contributions explicit, measurable, and evidence-based (environmental, economic, and social). Use this tool to reframe your methodology, scope, and findings to meet rigorous academic standards for sustainable development.

This guide helps researchers rigorously evaluate if their project meets the criteria for a 'systems analysis' in high-impact journals. It teaches how to shift the research focus from single-factor field trials to complex systems questions that analyze interactions, trade-offs, and emergent behaviors across multiple hierarchical levels, such as the farm to the food system. Essential for properly framing scope and article type before submission.

A comprehensive set of reusable patterns for structured incident triage in AWS environments. Includes pre-built Logs Insights queries for detecting errors, latency spikes, cold starts, and OOM events. Also provides systematic correlation methods linking CloudWatch alarms to deployment events, and a decision tree to narrow the blast radius scope from account to resource.

This comprehensive skill guides researchers through the critical ethical framework for anthropological and social science manuscripts. It emphasizes 'do no harm,' continuous informed consent, community accountability, and proper handling of sensitive data, cultural heritage, and human remains. It ensures that all data sharing and knowledge reuse plans prioritize the rights, privacy, and well-being of interlocutors above academic publication needs.

This guide provides comprehensive standards for designing, auditing, and reporting empirical evaluations in database systems research. It emphasizes the principle that evidence must proportionally match the claim, requiring the use of real-world workloads, fair and tuned baselines, thorough measurement of variance and state (warm/cold), and fully reproducible evaluation harnesses. It also outlines the higher bar for 'Experiments & Analysis' papers.

A comprehensive guide to preparing accountability artifacts for AI research papers, particularly for venues like FAccT. It covers the necessary documentation genres—including datasheets for datasets, model cards for models, and impact assessments—to ensure transparency, verifiability, and accountability regarding claims of fairness and harm mitigation in AI systems. The focus is on making research outputs rigorously traceable and ethically responsible.

This guide outlines the rigorous methodological standards for empirical work in AI fairness, accountability, and transparency (FAccT). It advises researchers on matching specific claims (e.g., disparate harm) to appropriate evidence types—including disaggregated metrics, coded qualitative data, and documented ethical procedures. It stresses the importance of ethical review, handling protected attributes, and acknowledging limitations to ensure high scientific and ethical rigor.

This skill guides authors to evaluate if their ecology or biology manuscript meets the rigorous standards of Global Change Biology. It focuses on the necessity of linking research findings to explicit global-change drivers (e.g., warming, elevated CO2) and providing robust biological evidence, from molecular mechanisms to ecosystem-scale consequences.

This guide provides comprehensive best practices for achieving scientific reproducibility in systems and programming language papers, following stringent SIGPLAN guidelines. It details the necessary rigor for documenting hardware, managing complex runtime measurements (like JIT warmup and variance), and maintaining a detailed, machine-readable provenance ledger and data-availability statement.

This guide addresses the stringent requirements for empirical evaluation in compiler and runtime systems, aligning with SIGPLAN's guidelines. It covers critical aspects such as proper warmup discipline, reporting variance and confidence intervals, selecting principled benchmarks, pinning toolchains, and ensuring cross-platform validity. It provides a protocol for generating verifiable measurement logs for academic publications.