Skill UI

This skill assists in researching prediction markets, treating them as potential data sources or oracle signals for corporate decision intelligence, product development, and agent inputs. It provides source-grounded analysis of implied probabilities, signal quality, and integration patterns, while strictly avoiding investment advice.

A specialized tool designed to query and summarize the latest AI industry news, hot topics, curated selections, and daily reports. It monitors major developments in large models (LLMs), product releases, research papers, and updates from leading tech companies. The output is presented as an easy-to-read digest, ensuring users receive accurate, current information directly from the official AI HOT source.

A deep research framework for systematically studying products, companies, concepts, or people through longitudinal storytelling and cross-sectional competitor analysis, ending with a polished PDF report.

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 tool simulates market demand using synthetic audience panels grounded in real CRM data to estimate customers' willingness-to-pay (WTP). It calculates optimal price points, acceptable price ranges, and analyzes price sensitivity across different buyer segments. Use this command to guide pricing strategies before product launch, tier adjustments, or competitive positioning evaluation, providing actionable intelligence without expensive formal research.

This guide outlines the critical rules for structuring technical research papers submitted to top-tier conferences like ASE. It emphasizes the 'decision-criticality' rule, instructing authors on how to separate core arguments (which must fit within the 10-page body) from reproduction details, raw logs, and supplementary material (the artifact). Mastering this separation is crucial for ensuring reviewers judge the paper on its merits, not its supplementary data.

A comprehensive guide for researchers detailing how to strengthen the reproducibility of security claims for top conferences like CCS. It covers mapping every security claim (attacks, defenses, measurements) to verifiable evidence, managing artifact availability when sharing is restricted, and defining the achievable degree of reproduction (Turnkey, Scripted, Descriptive). This ensures academic rigor and transparency in published security work.

A comprehensive guide for researchers preparing academic artifacts for voluntary, post-acceptance evaluation (e.g., HPCA). It outlines best practices for structuring submissions for cold-start evaluators, defining reproducibility tiers (full reproduction, regeneration from logs, inspection), and managing computational budgets to successfully earn IEEE reproducibility badges.

A comprehensive guide for positioning database research papers for top-tier conferences like ICDE. It teaches authors how to distinguish genuine novelty (new mechanisms) from engineering delta (implementation improvements), conduct deep literature audits, and compare their work against both academic venues and real-world production systems. Essential for improving manuscript eligibility and impact.

This guide details the process of packaging artifacts for academic conferences (ICLR), ensuring that research claims are fully reproducible and reviewable. It emphasizes providing minimal reproduction paths, maintaining data provenance, rigorously anonymizing metadata to protect authors' identities, and creating robust, permanent records for the scholarly community.