Skill UI

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.

This skill provides comprehensive framework for conducting deep audience research, enabling the creation of detailed buyer personas and sophisticated market segmentation strategies. It covers psychographic profiling, Jobs-to-Be-Done (JTBD) analysis, behavioral segmentation, and defining ideal customer profiles (ICPs). Use it for targeted marketing, product development, and refining outreach efforts across B2B and B2C models.

Run simulated focus groups by leveraging real CRM data to predict how diverse customer segments will react to new messaging, pricing structures, or product concepts. This powerful tool generates structured predictions, including sentiment analysis and key objections, providing directional feedback before committing to expensive live research or campaigns. It bridges the gap between intuition and data-driven strategy.

This advanced tool performs comprehensive keyword research, classifying search intent (informational, commercial, etc.), identifying long-tail opportunities, and conducting competitor content gap analysis. It generates a highly prioritized, actionable content strategy document complete with recommended formats and a full SEO content roadmap.

This guide details how to position academic manuscripts within a specific geographic scholarly debate, rather than simply citing domain literature. It teaches authors to identify the "closest prior account," articulate a cross-cutting geographic stake, and engage with core geographic theories to enhance the impact of their findings for an advanced scholarly audience.

A comprehensive guide for preparing scholarly manuscripts involving spatial data. It outlines best practices for documenting data provenance (sources, projections, processing chains), ensuring reproducibility, and adhering to critical ethical standards such as geoprivacy and human-subjects protection. Essential for maintaining research integrity in academic publishing.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

A comprehensive guide for preparing accepted research papers for final submission to major conferences (e.g., AAAI Press). This process covers strict template adherence, deanonymization of author details, managing copyright transfer, optimizing page counts (including purchasing extra technical pages), and ensuring all source files (LaTeX/Word, figures, bibliography) are correctly formatted for professional publication.

A comprehensive guide for positioning AI/ML manuscripts for top-tier general AI conferences like AAAI. It helps authors refine their scope, frame their contribution, provide necessary evidence (e.g., ablations, baselines), and strategically compare their work against related research and similar venues (ICML, IJCAI). Ideal for pre-submission manuscript refinement.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.

This comprehensive guide helps researchers strengthen the reproducibility of AI academic papers for top-tier conferences like AAAI. It outlines best practices for creating a claim-to-evidence ledger, documenting seeds, hyperparameters, compute costs, and dataset licensing. Use this to ensure your work meets the highest standards of academic rigor and verifiability.

This guide helps researchers strategically decide if their AI project is a strong fit for the AAAI conference. It provides detailed criteria, including strong and weak signals, and routing logic to determine whether the work should be submitted to AAAI, or routed to specialized venues like NeurIPS, ICML, or domain-specific conferences (e.g., CVPR, KDD). Use it to refine your project's framing and maximize submission success.