Skill UI

This tool audits and validates all Claude Code hooks defined across your settings files. It checks for validity, performance safety, correct event scoping, and proper exit-code handling (blocking vs. non-blocking). Essential for initial setup, debugging hooks that fail to fire, or performing routine system hygiene passes to ensure your agent's tool flow is robust and reliable.

A comprehensive skill for AI agents to perform advanced filesystem operations. It provides features for intelligent file listing with deep filtering, powerful content and pattern searching, safe batch file copying, and detailed directory analysis (statistics, tree visualization). Ideal for code analysis, system auditing, and managing project assets.

This skill generates a comprehensive, structured handoff document from the current conversation session. It captures all critical project details, including scope, relevant files with specific line numbers, key discoveries, achieved work, current status, and actionable next steps. This is essential for knowledge transfer, ensuring that work can be seamlessly resumed by another agent or team member.

A comprehensive framework for conducting authorized security assessments on Large Language Models (LLMs) and AI agents. It covers critical vulnerabilities including advanced prompt injection (OWASP Top 10), tool abuse, RAG data poisoning, memory manipulation, and system prompt extraction. Ideal for red teaming, security auditing, and ensuring the robustness of AI systems in production environments.

This mode defines an autonomous, senior-level AI agent capable of resolving complex tasks from start to finish with minimal human supervision. It operates by strictly verifying every claim against real evidence (live web search, codebase, available tools). The agent prioritizes action over questioning, only interrupting the user for irreversible actions, genuine dead-ends, or critical ambiguities, ensuring maximum efficiency in hands-on engineering and operational workflows.

A sophisticated, multi-agent system designed to perform deep, comprehensive code reviews on pull requests. It covers core elements like code quality, functionality, security vulnerabilities, and testing coverage. Advanced features include detecting scope drift, enforcing SOLID principles, identifying DRY violations, and providing anti-hallucination safeguards for maintain high software integrity.

An interactive wizard designed to help developers identify the optimal architectural pattern (Agent, Command, Skill, Hook, or Rule) for a new automation or feature. By asking a series of guided questions regarding trigger, complexity, and scope, it diagnoses the best fit and generates a complete, ready-to-use template (scaffold) for immediate development.

This skill enables the execution of OpenSquilla agents against complex, real-world software engineering benchmarks (SWE-bench). It runs the entire process—from pulling the necessary Docker image to collecting the patch and running official evaluations—in a containerized environment. Use it to test an agent's ability to solve specific coding issues, benchmark performance across datasets (e.g., verified, multilingual), or verify if a generated patch successfully resolves an instance.

This skill guides the researcher through the comprehensive process of updating the AI agent security threat intelligence database. It involves executing targeted searches for new malicious skills, CVEs, attack vectors, and defensive tools. The process mandates critical analysis, de-duplication, risk assessment, and structured version control to maintain a cutting-edge security posture for AI agents.

This tool leverages an LLM-as-a-Judge agent to thoroughly evaluate staged Git changes before they are committed. It checks for correctness, completeness, and safety issues, providing a detailed verdict (APPROVE, NEEDS_REVIEW, REJECT) and actionable feedback. It is essential for maintaining high code quality, especially in complex or security-sensitive code sections.

This agent is designed to serve as a comprehensive and helpful assistant, capable of guiding users through a wide variety of tasks and inquiries. It acts as a general knowledge resource and helper, providing information, suggestions, and support across multiple domains whenever the user needs assistance.

This framework provides a rigorous pre-execution review mechanism for proposed actions by AI agents or systems. It functions as a critical safety boundary, auditing the entire decision path—from initial request to proposed tool execution—without authorizing or executing anything. It verifies action conformity, evidence integrity, temporal validity, and reviewer independence to ensure system compliance and security.