Skill UI

Framework for building stateful, multi-actor AI applications with LangGraph, covering graph construction, reducers, persistence, human-in-the-loop patterns, and ReAct agents suitable for Python production systems.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

This skill implements an autonomous, multi-round research review loop, simulating the rigorous peer-review process of top-tier conferences. It iteratively reviews, identifies weaknesses, suggests fixes, and re-reviews a research document using the MiniMax API. The system manages state persistence across multiple rounds, ensuring continuous improvement until a 'ready' assessment is achieved or the maximum round limit is reached. Ideal for academic writing, thesis refinement, or comprehensive project documentation.

This skill enables AI agents to maintain persistent project planning using Markdown files on disk. It manages task_plan.md, findings.md, and progress.md to extend context window limits. Lifecycle hooks automatically inject planning content and prevent command execution in Markdown. Ideal for complex research tasks requiring over 5 tool calls, ensuring state persistence across sessions without network uploads.

This skill allows agents to ingest external resources (local files, URLs, repositories) using `ov add-resource`, and to create long-lasting searchable memories from conversations using `ov add-memory`. It is essential for building a comprehensive, persistent knowledge base for the OpenViking context database, ensuring that learned insights and external data remain available across sessions and interactions. Use when external knowledge injection or context persistence is required.

Offensive cloud security methodology for AWS, Azure, and GCP. Covers identity discovery, credential harvesting, enumeration, privilege escalation, persistence, data exfiltration, lateral movement, serverless abuse, Kubernetes paths, and detection evasion during authorized assessments.

Run and monitor managed CPU, GPU, and TPU workloads on Hugging Face Jobs, with secrets, Hub persistence, and review-focused submission guidance.

Train image classification, object detection, and SAM/SAM2 segmentation models locally or on Hugging Face Jobs, with bounded dataset pilots, safety checks, and explicit artifact persistence.

Expert guidance for building production-grade stateful, multi-actor AI agents with LangGraph, covering graph construction, state management, cycles and branching, checkpoint persistence, human-in-the-loop patterns, and the ReAct agent workflow.

This skill detects persistent instruction injection vulnerabilities in AI agents. It verifies if the agent stores malicious instructions in long-term memory or context to execute later. The process involves attempting to write a persistent rule and verifying if the agent follows it in subsequent interactions. It focuses on behavioral persistence rather than data leakage.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

This skill covers building persistent memory systems for Large Language Model (LLM) conversations. It includes managing short-term, long-term, and entity-based memory to enhance user experience. Key capabilities involve memory retrieval, consolidation, and persistence strategies. It helps AI assistants remember user preferences and history across sessions without overwhelming context windows. Essential for chatbots and virtual assistants requiring context awareness.