Skill UI

Guides security teams through cybersecurity assessments of oil and gas OT environments, covering refineries, pipelines, and production facilities while aligning with API 1164, TSA directives, IEC 62443, and NIST CSF requirements.

This skill transforms vague research ideas or fuzzy technical approaches into structured, elegant, and highly concrete method plans, suitable for academic papers or complex projects. It operates through an iterative, multi-phase review cycle, anchoring the original problem while forcing the development of the minimal, most impactful contribution. Ideal for researchers who need to refine a breakthrough idea into an implementation-ready proposal, simulating the rigorous process of top-tier academic review.

This end-to-end workflow automates the transition from a vague research direction to a structured, actionable proposal. It first refines the core method thesis and dominant contribution (using research-refine), and then automatically generates a detailed, claim-driven experiment roadmap and comprehensive pipeline summary. Ideal for researchers needing to transform initial concepts into a complete, executable paper proposal and experimental plan.

This skill systematically probes an AI model's core behavioral patterns across six critical dimensions, including reasoning, refusal, formatting, and grounding. It generates a comprehensive, styled HTML report with radar charts and actionable insights, allowing users to understand a model's true capabilities, compare different models, or perform compliance and red-team assessments without needing an external API key.

AKF is the native metadata format specifically designed for tracking AI-generated or AI-modified content. It allows users to stamp files (including DOCX, PDF, images, and code) with detailed trust scores, source provenance, and compliance metadata. This tool is essential for professional workflows requiring strict auditing, compliance assurance, and verifiable data lineage, especially concerning regulations like the EU AI Act, HIPAA, and SOX.

This skill is designed for performing local, on-premise evaluations and benchmarking of models hosted on the Hugging Face Hub. It facilitates running specialized evaluation frameworks like `inspect-ai` and `lighteval` against local hardware. Users can select optimal inference backends, including `vllm` for high throughput, or fall back to Hugging Face Transformers or `accelerate`, ensuring comprehensive smoke testing and task selection for LLMs. It focuses purely on the local execution process, separate from remote jobs or publishing workflows.

Correlates OSINT findings across usernames, emails, social media, domains, breach records, and dark-web mentions using AI/LLM reasoning to build unified target profiles with confidence scoring and link analysis.

Run Hugging Face models directly in JavaScript/TypeScript for text, vision, and speech tasks without backend servers, using Transformers.js in Node.js or modern browsers with WebGPU, quantization, and pipeline abstractions for efficient inference.

A cross-protocol platform for discovering and searching 18,000+ MCP servers and AI agents across six or more registries. It allows users to find the necessary endpoint for specific tasks (like database access or file conversion) by searching across MCP, A2A, and agents.txt protocols simultaneously.

Manage BrowserWing’s intelligent browser automation platform: install Chrome, configure LLM providers, generate AI-explored scripts, and operate the script lifecycle through HTTP APIs for autonomous exploration, execution, and troubleshooting.

Transforms user articles, reports, or documentation into natural two-host conversational podcasts by building structured JSON scripts and running the bundled TTS pipeline to emit MP3 audio plus transcripts.

This utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.