Skill UI

This skill is designed to facilitate communication and collaboration between different AI agents. Use it when you need to consult an expert agent, request a plan, seek a review, or continue a context-aware conversation with a specialized agent. It handles both real-time foreground chat and asynchronous background task submission, ensuring robust multi-agent workflows.

A comprehensive guide for configuring and troubleshooting Jenkins when embedded within KubeSphere DevOps. Learn how to manage Jenkins via Configuration as Code (CasC), integrate advanced authentication methods like LDAP and OIDC, customize agent images, and connect to external sources such as GitLab. Covers everything from initial access to backup and recovery.

This skill is designed for scenarios requiring complex, multi-step task decomposition. Instead of asking an Agent to perform the task directly, it guides the process of requesting a clear, actionable, sequential execution plan from a stronger Agent. The core function emphasizes that the user must execute the generated plan themselves, ensuring the output is a roadmap, not a final answer.

Evolver is a sophisticated self-evolution engine designed for advanced AI agents. It analyzes runtime logs and operational history to autonomously identify performance bottlenecks, failures, and opportunities for improvement. It applies protocol-constrained evolution, communicating exclusively via a local Proxy Mailbox to interact with the EvoMap Hub for asset submission and task claiming.

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

Use terminal commands to discover, list, create, edit, move, copy, enable/disable, and delete AI agent skills across 11 tools, handling format conversions and scope overrides between global and project configurations.

PufferLib is a high-performance library designed for advanced reinforcement learning simulations and training. It excels in parallel environment simulation, offering optimized vectorization and native support for multi-agent systems. Utilizing algorithms like PuffeRL (optimized PPO+LSTM), it achieves millions of steps per second, significantly accelerating research and deployment across single-agent and multi-agent environments.

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.

This skill guides AI agents to handle complex technical tasks with maximum precision and minimal computational overhead. It implements adaptive verbosity rules, sophisticated input filtering (for logs, source files, VCS, and payloads), and surgical output generation. It ensures context-window awareness, structured reasoning, and adherence to best practices for coding, debugging, and architecture analysis.

The AI Dev Jobs MCP provides AI agents with real-time access to a massive index of AI and Machine Learning job listings across hundreds of companies. Users can search by role, location, or company, retrieve full job details, analyze salary ranges, list hiring companies, and get aggregate market statistics. It is essential for advanced job searching, talent matching, and labor market analysis workflows.

This guide details securing the CAST AI integration across multiple layers. It covers best practices for API key management (including rotation and secrets storage), implementing least-privilege RBAC using 'kubectl', deploying the Kvisor security agent for runtime scanning, and setting up Kubernetes Network Policies to restrict egress traffic. Use this when hardening production clusters or performing security audits.

A comprehensive guide detailing the systematic process for upgrading and migrating CAST AI components. This includes updating Helm charts (agent, controller, evictor) and Terraform providers. It outlines prerequisites, step-by-step upgrade procedures, validation checks, and necessary rollback procedures to ensure a safe transition between versions.