Skill UI

Moltbook AI Social Hub is a community-driven social network where registered agents post, comment, upvote, and join submolts, powered by the Moltbook API for secure authentication and heartbeat reminders.

Guides authorized practitioners through Nmap, Masscan, Nessus, OpenVAS, Burp Suite, and OWASP ZAP for network discovery, vulnerability assessment, web application testing, wireless security, and compliance reporting across diverse environments.

Guide to capturing, filtering, and inspecting packets with Wireshark for troubleshooting, performance tuning, and security investigations across live and PCAP-based workflows.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

Provides programmatic access to DrugBank drug and target data, covering downloads, queries, interaction networks, targets/pathways, and chemical similarity analyses for pharmacology research and safety reviews.

Comprehensive Python toolkit for building, analyzing, and visualizing complex networks; supports algorithms, I/O, generation, and community detection across social, biological, transportation, and citation data.

Generates minimal macOS Seatbelt sandbox profiles with allowlist-based resource rules so apps can be isolated by permissions, useful for restricting file, network, and IPC access when defending against high-risk execution or supply chain threats.

SimPy is a powerful, process-based discrete-event simulation framework written in Python. It is designed for modeling complex systems where entities interact with shared resources and time-based events occur, such as manufacturing lines, service queues, network traffic, or logistics networks. Use this skill to analyze resource contention, simulate process optimization, conduct capacity planning, and model dynamic system behaviors.

Query the STRING REST API to retrieve protein-protein interaction networks, map identifiers, visualize evidence-weighted edges, and run GO/KEGG/Pfam enrichment for systems biology analysis.

Reference for building Graph Neural Networks with PyTorch Geometric, covering data objects, datasets, transforms, convolution layers, lazy initialization, and heterogeneous support so you can quickly tackle node, link, and graph tasks.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

This powerful skill manages Git worktrees to enable safe, isolated parallel feature development. It standardizes branch isolation, automatically allocates unique network ports, and synchronizes environments, allowing multiple agents or terminal sessions to work on different branches simultaneously without conflicts. It also provides mechanisms for detecting and safely cleaning up stale worktrees.