Skill UI

This skill provides a comprehensive guide to designing and developing autonomous AI agents for code plugins. It covers the required file structure (YAML frontmatter), best practices for defining agent roles, designing robust system prompts, and setting up clear triggering conditions. Use this when you need to conceptualize, structure, or refine an agent's capabilities to handle complex, multi-step tasks autonomously.

A comprehensive, low-level Software Development Kit (SDK) for building and managing stateful AI Agents within the Azure AI ecosystem using .NET. This SDK provides core clients for managing agent lifecycles, persistent conversation threads, message history, complex run execution, tool/function calling, and integrating file search through vector stores. It is essential for developing enterprise-grade, multi-turn conversational AI applications.

A comprehensive, low-level Software Development Kit (SDK) for building and managing stateful AI Agents within the Azure AI ecosystem using .NET. This SDK provides core clients for managing agent lifecycles, persistent conversation threads, message history, complex run execution, tool/function calling, and integrating file search through vector stores. It is essential for developing enterprise-grade, multi-turn conversational AI applications.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

This pattern addresses the fundamental limitation of context window size by treating the filesystem as a persistent, external memory. Agents can write large outputs, plans, or findings to files, offloading them from the active context. Instead of carrying all information, agents use targeted search and retrieval tools to load only the necessary context on demand, drastically reducing token bloat and improving performance over long, complex tasks.

Generates and validates llms.txt files, the emerging standard that tells AI systems how to prioritize and understand a website; crawls content, ranks pages, and enforces formatting so your brand narrative stays consistent.

This tool provides a comprehensive framework for analyzing web server logs (Apache/Nginx) to detect various security threats. It uses advanced regex matching against OWASP attack signatures to identify SQL injection, Local File Inclusion, and Directory Traversal attempts, alongside behavioral analysis for brute-force attacks and web scanner fingerprints. The system also enriches data using GeoIP for source attribution, generating a prioritized report for SOC analysts.

A comprehensive guide and workflow demonstrating how to use YARA rules for proactive threat hunting. This method helps detect unknown malware, suspicious patterns, and Indicators of Compromise (IOCs) across filesystems, memory dumps, and network artifacts, making it essential for incident response and security analysis.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

This skill facilitates the crucial process of sharing learned intelligent patterns across isolated projects or machines using the InterPlanetary File System (IPFS). It allows users to publish a project's accumulated knowledge base to a decentralized network and later fetch, apply, and merge these patterns into new or existing projects. This is invaluable for bootstrapping new applications with prior knowledge or consolidating learnings across a large monorepo.

This skill provides access to the Anthropic Claude Managed Agent runtime, which is a cloud-native, persistent environment for running complex, long-running, or asynchronous AI tasks. Unlike local WASM agents, the managed runtime provides a full container, persistent filesystem, and robust state management across multiple turns. It supports the entire agent lifecycle: creation, prompting, status inspection, event logging, and mandatory termination.

Orchestrates a robust, multi-stage cyber defense pipeline using specialized AI agents to analyze raw log files (e.g., nginx, auth, syslog). The system ingests logs, detects anomalies, classifies overall risk, and generates a comprehensive, actionable incident report, making it ideal for security investigations and breach analysis.