Skill UI

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

Generates practical and robust CI/CD pipelines by programmatically detecting the project's entire technology stack (language, runtime, tooling). It provides fast baseline setup for GitHub Actions or GitLab CI, ensuring repeatability, environment awareness, and alignment with project dependencies. Ideal for onboarding new repositories, standardizing DevOps workflows, or migrating existing deployment processes.

Creates isolated git worktrees with prioritized directory discovery, ignore verification, dependency bootstrapping, and clean baseline tests so feature branches launch safely alongside ongoing work.

This skill enforces a rigorous, opinionated baseline for UI development, ensuring consistency across spacing, hierarchy, typography, and layout. It helps maintain high code quality by fixing structural and stylistic inconsistencies, particularly useful for polishing AI-generated or quickly developed interfaces to meet professional standards and accessibility requirements.

Analyzes a code repository to generate a recommended, minimal set of read-only shell command permissions for Claude Code's settings.json. It automatically detects the project's tech stack (e.g., Python, Node.js, Go), build tools, and monorepo structure, ensuring a safe and evidence-based security baseline for new projects or auditing existing permissions.

This command executes the entire AgentHub lifecycle in a single, chained invocation. It automates complex development tasks by sequentially handling initialization, capturing performance baselines, spawning multiple specialized agents, evaluating the results (via metrics or LLM judging), and finally presenting a winner for merging. Use this when running an end-to-end competition or complex refactoring task.

Detects abnormal access patterns across major cloud storage platforms, including AWS S3, GCS, and Azure Blob Storage. By analyzing cloud audit logs (CloudTrail, etc.), this tool identifies critical security threats such as after-hours bulk data downloads, sudden spikes in API calls (GetObject spikes), access from new geographic IPs, and potential data exfiltration. It utilizes statistical baselines and time-series anomaly detection for proactive threat hunting and security incident investigation.

This tool parses NetFlow v9 and IPFIX records to conduct deep network security analysis. It is crucial for investigating security incidents by building traffic baselines and applying statistical methods. Key detection capabilities include identifying port scanning, data exfiltration attempts, command and control (C2) beaconing patterns, and general volumetric anomalies in network traffic.

Guides security architects through designing and deploying a continuous CSPM program across AWS, Azure, and GCP, covering tool selection, policy baselines, automation, and SOC integration to monitor misconfigurations and prioritize remediation.

This skill deploys advanced anomaly detection systems for Operational Technology (OT) and Industrial Control Systems (ICS). It builds multi-dimensional baselines by analyzing SCADA communications, network traffic (Modbus, DNP3, OPC UA), and timing patterns. It detects deviations, rogue devices, and potential intrusions by profiling normal behavior, complementing signature-based IDS.

Utilizes User and Entity Behavior Analytics (UEBA), statistical baselines, and machine learning models to monitor authentication logs. This skill identifies sophisticated security threats such as impossible travel, credential stuffing, brute force attacks, and compromised accounts in real-time. Essential for security operations and identity access management.

This skill provides methods and tools for detecting sophisticated cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS). It focuses on detecting anomalies like unauthorized command injection, Man-in-the-Middle attacks on industrial protocols (Modbus, DNP3), and data manipulation, which traditional IT security tools often miss. Usage involves establishing deterministic communication baselines and monitoring network traffic for deviations.