Skill UI

This tool provides comprehensive management capabilities for Cognitum Seed device fleets. Users can perform various actions such as creating new fleets, listing existing ones, adding or removing individual devices, and deleting entire fleets. It helps maintain firmware policies and manage the lifecycle of connected IoT devices.

This tool is designed to register a Cognitum Seed IoT device, establishing a secure agent bridge and fetching the device's identity. It resolves the specified or default endpoint for connectivity and handles initial trust level assignment. It is crucial for the initial setup, pairing, and status checking of IoT hardware deployments.

A comprehensive guide for engineering and optimizing latency-sensitive systems. Essential for real-time dashboards, market data feeds, streaming agents, and high-frequency infrastructure where p95 latency, data freshness, and throughput are critical metrics. Covers hot path mapping, performance optimization strategies, and robust verification guardrails.

A comprehensive guide for configuring and deploying the RuView wireless sensing system. It covers firmware build-time settings (sdkconfig), runtime parameters (NVS provisioning), multi-node mesh setup, and integrating advanced edge intelligence modules (ADR-041). Essential for tuning system behavior and capabilities without modifying core code.

This skill guides researchers to determine if their topic fits the scope of Acta Automatica Sinica (AAS). It provides detailed boundaries for control theory, pattern recognition, robotics, and smart systems, helping authors distinguish their contribution from pure algorithms or system software, and advising on appropriate article type (Review/Paper/Short) to avoid initial rejection.

A comprehensive guide for positioning research manuscripts for the ACM SIGGRAPH I3D Symposium. It covers core topics like real-time rendering, interactive 3D systems, and GPU algorithms, advising authors on how to frame their work, structure evidence, and compare it against related academic venues to maximize submission success.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

This guide helps researchers determine the suitability of their energy-related manuscripts for the Applied Energy journal. It shifts the focus from single-device performance or component novelty to generalizable, systems-level insights. Key areas include energy-systems modeling, decarbonization pathways, techno-economic analysis (TEA), life cycle assessment (LCA), and assessing integrated energy transitions across sectors.

This comprehensive audit guide helps authors prepare their research papers for the CAV submission portal. It covers crucial steps including selecting the correct paper category (Regular, Short Tool, etc.), adhering to LNCS formatting and page limits, and performing technical double-blind anonymity sweeps. Use this checklist to mitigate risks like identity leaks and filing errors before the final deadline.

A comprehensive guide for authors targeting high-impact chemical engineering venues. It helps authors re-frame their work from simple material synthesis to demonstrating an applied advance with clear mechanistic insight. Focus areas include catalysis, separation, functional materials, and performance benchmarking under realistic process conditions.

This guide outlines the process for converting accepted academic papers into optional ACM reproducibility badges. It walks authors through the rigorous evaluation cycle, covering everything from initial opt-in before submission to providing detailed one-page artifact descriptions. The goal is to prove that the research results are available, functionally sound, highly reusable, and accurately reproducible by independent evaluators.

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.