Skill UI

This skill provides implementation patterns for utilizing Runway's AI video generation and creative platform. It covers setting up the SDK, generating videos from images, and handling common API errors (like insufficient credits or unauthorized access). It is designed for developers needing to integrate secure, high-quality AI video workflows into their applications.

This debug bundle provides comprehensive guidance for working with Together AI's OpenAI-compatible API. It supports critical LLM lifecycle tasks, including inference, fine-tuning, and model deployment for over 100 open-source models (e.g., Llama, Mixtral). It also includes detailed troubleshooting steps and solutions for common API errors (like 401 Unauthorized or 429 Rate limit), ensuring smooth development.

This guide provides comprehensive instructions for setting up and authenticating the Together AI SDK. It covers installation using both Python and Node.js, configuring the necessary API key, and verifying connectivity to utilize the OpenAI-compatible API for powerful open-source model inference and fine-tuning tasks. Ideal for developers starting with Together AI.

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.

This skill provides a comprehensive, structured workflow for creating, authoring, and refining new agent skills. It guides users through gathering requirements, drafting structured documentation (SKILL.md), adding utility scripts, and optimizing the skill description for optimal agent recognition. Ideal for system architects and developers building complex AI agent toolsets.

A specialized skill for optimizing content to be cited as authoritative sources by major AI language models (ChatGPT, Perplexity, Claude, Gemini). Unlike traditional SEO, AEO focuses on E-E-A-T signals, factual density, and structured data to ensure LLM integration. Features include content auditing, E-E-A-T scoring, structural rewriting, and citation tracking.

A comprehensive studio for professional voice conversion. This tool allows users to transform local audio recordings into specific, authorized target voices. It manages complex workflows, requiring rigorous checks on source rights, target voice licensing, and detailed parameters like emotion, accent, pace, and language locale, ensuring compliance and high-quality output.

This skill uses Python and boto3 to analyze AWS CloudTrail logs. It establishes statistical baselines of normal API activity and detects various anomalies, such as unusual event sources, first-time API usage, geographic IP deviations, and high-frequency calls. This is critical for identifying credential compromise, privilege escalation, and unauthorized resource access in a security incident.

This skill provides a repeatable assessment for RAG systems, testing for critical vulnerabilities in vector stores and embedding models. It specifically addresses embedding inversion, cross-tenant data leakage, and knowledge-base poisoning, aligning with OWASP LLM08:2025. Use when performing authorized security audits of retrieval layers and multi-tenant isolation.

This skill audits Model Context Protocol (MCP) servers to detect critical AI security vulnerabilities. It scans for tool poisoning, tool shadowing, rug pulls, SSRF, and unauthenticated exposure. Use it before integrating new tools or during security reviews to ensure agent reliability and prevent supply chain attacks.

The Anti-Deception Harness detects manipulative user prompts, such as manufactured urgency, pressure to validate, or undue reliance on authority. It calls an external tool to provide a structured scaffold detailing integrity procedures and counter-evidence, ensuring responses are honest and robust, preventing compromised or sycophantic answers. Ideal for high-stakes decision support.

This skill provides authors with a comprehensive guide to positioning their computer science manuscript for the AAAI/ACM Conference on AI, Ethics, and Society (AIES). It covers critical aspects such as framing the contribution, meeting evidence standards, addressing social impacts, and distinguishing the work from similar venues like FAccT. Use it to refine submissions focused on AI ethics, governance, and socio-technical methods.