Skill UI

The Safety Guard skill is designed to prevent destructive actions when working in production systems or when autonomous agents are executing tasks. It offers three protection modes (Careful, Freeze, and Guard) to monitor dangerous commands, lock specific directories, and ensure code changes are confined to intended areas, enhancing system stability and security.

Suggests when to manually run /compact at logical workflow boundaries instead of relying on automatic compaction, helping preserve context after research, milestones, or task shifts.

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.

A guide to the Swift 6.2 concurrency model, which mandates default single-threaded execution to eliminate accidental data races. Learn how to use MainActor for isolated consistency and utilize the @concurrent attribute for explicitly offloading CPU-intensive tasks to background threads, ensuring safe and predictable application architecture.

A comprehensive suite for running advanced sensing applications using RuView hardware. It supports multi-modal data fusion from CSI, Wi-Fi, and mmWave sensors to perform critical tasks such as occupancy detection, vital sign monitoring (heart rate, breathing), pose estimation, environment mapping, and mass casualty assessment. Ideal for prototyping sophisticated IoT and smart building solutions.

A comprehensive, real-time guide for the Digital Marketing Pro plugin. This tool provides a complete overview of the plugin's status, including live versions, available skills, commands, and connectors. It guides users through setup, usage examples (SEO, content creation, campaign strategy), and troubleshooting for advanced digital marketing tasks.

A comprehensive tool for distributing marketing tasks to team members. It intelligently matches tasks based on roles, channel expertise, regional alignment, and current capacity. Features include assigning new tasks with smart matching, viewing assignments, and generating workload dashboards to prevent resource bottlenecks and skill mismatches.

A comprehensive guide for revising NLP and Computational Linguistics manuscripts for submission to top-tier conferences like ACL. It mandates adopting a task-first, evidence-grounded approach, strictly scoping claims in the LLM era, and ensuring linguistic rigor in all examples and analyses. Learn how to structure, compress, and refine your arguments for maximum impact.

Provides comprehensive guidance for researchers deciding the optimal conference venue for Software Engineering (SE) research. It distinguishes between contributions that focus on *automation* (techniques or tools that automate SE tasks) suitable for ASE, versus those that are broad empirical findings, theoretical results, or pure ML advances, directing them to venues like ICSE, FSE, or ISSTA.

Provides comprehensive guidelines for structuring and writing academic papers for Automated Software Engineering (ASE) conferences. It teaches authors how to present an automated task, emphasize the automation design over the deep learning model, provide evidence from real subjects, and adhere to strict formatting and argument presentation rules to maximize acceptance chances.

This skill provides comprehensive guidance on positioning, structuring, and refining computer vision manuscripts for top-tier venues like CVPR. It covers identifying major novelty, required experimental rigor (e.g., ablations, failure modes), framing contributions, and adhering to specific conference submission standards for CV, VLM, and recognition tasks. Use it to refine your submission strategy and ensure your work meets the high bar set by leading AI conferences.

This guide details the critical, multi-step process for converting an accepted, anonymous conference manuscript into a publishable, citable journal article for PACMNET. Key tasks include systematic de-anonymization (restoring author details and system names), completing comprehensive journal metadata (ACM CCS, ORCID, licensing), permanent linking of artifacts via DOI, and conducting rigorous production checks to ensure final publication quality.