Skill UI

This tool facilitates interactive dynamic malware analysis using the ANY.RUN cloud sandbox. It allows analysts to detonate suspicious samples, observe real-time execution behavior, and manually interact with prompts (like dialogs or CAPTCHAs). Key outputs include detailed process trees, network traffic analysis (C2 communication), and identification of all Indicators of Compromise (IOCs), mapping observed behaviors to MITRE ATT&CK. Essential for rapid, sophisticated behavioral analysis without maintaining local sandbox infrastructure.

This tool automates the discovery and comprehensive inventory of all privileged accounts across an enterprise infrastructure. It identifies domain administrators, local accounts, service accounts, cloud IAM roles, and application admins. It supports risk classification, enabling organizations to build accurate inventories, onboard accounts to PAM solutions, and scope necessary privileged access controls for compliance and security assessments.

A comprehensive guide to conducting security assessments on thick client (desktop) applications. This skill details techniques ranging from static analysis (using tools like dnSpy, IDA Pro) to dynamic analysis (process monitoring with Procmon, network interception via Burp Suite). It covers finding hardcoded credentials, identifying insecure local storage, mitigating DLL hijacking, and analyzing memory manipulation within local applications.

This comprehensive tool manages the complete lifecycle of ML experiments, providing robust deployment capabilities across diverse environments. It supports running training jobs on local GPUs, dedicated remote servers, Vast.ai, and serverless platforms like Modal. The workflow handles environment detection, code synchronization (rsync/git), dependency pinning, and integrates advanced logging tools (e.g., W&B), ensuring reliable, scalable, and reproducible ML training runs.

Guides on using SwanLab for open-source ML experiment tracking, offering local or self-hosted dashboards, rich media logging, and integrations with PyTorch, Transformers, Lightning, or Fastai for comparing runs and monitoring metrics.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

MCPB is a specialized local server bundle that packages the entire runtime environment (Node.js or Python) alongside the server code. It allows developers to distribute and run complex MCP services directly on a user's machine without requiring the user to pre-install specific runtimes or toolchains. Use this when the server must interact with the local filesystem, desktop applications, or OS-level APIs.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

A comprehensive guide detailing how to refactor large, complex SwiftUI views into smaller, more stable, and explicit components. It emphasizes adopting a lightweight state approach (favoring local state and environment variables over excessive view model layers) to improve data flow, readability, and overall code maintainability in modern iOS development.

This skill performs in-depth literature reviews across complex communications domains, including 5G/6G, Wi-Fi, satellite networking, and advanced protocols. It systematically searches user-defined knowledge bases (Zotero, Obsidian, local files) and major academic databases (IEEE, ACM, ScienceDirect) to provide comprehensive survey summaries, related work analyses, and technical landscape overviews.

This skill is designed for running comprehensive evaluations of Hugging Face Hub models on local hardware. It supports multiple evaluation frameworks, including `inspect-ai` and `lighteval`, and provides flexibility in backend selection (e.g., `vllm`, Transformers, or `accelerate`) to optimize for performance and compatibility. Use this for local GPU benchmarking, smoke testing, and backend selection, but note that it does not handle remote job orchestration or result publishing.

This skill provides a managed cloud environment for training and fine-tuning advanced vision models. Users can build and customize models for object detection (e.g., DETR, YOLOS), image classification, and segmentation (SAM/SAM2). By leveraging Hugging Face Jobs, training is executed on powerful cloud GPUs without requiring local GPU setup, and all resulting models are automatically saved to the Hugging Face Hub.