Skill UI

Use Elasticsearch-powered UEBA to model baselines from authentication, file, and network logs, score deviations, and flag insider threat indicators for SOC investigation workflows.

This advanced skill orchestrates a systematic, multi-stage workflow for generating novel and feasible research ideas in robotics and embodied AI. It moves users from a broad concept (e.g., manipulation, locomotion) to specific, benchmark-grounded, simulation-first projects. The pipeline surveys literature, checks novelty against major conferences (CoRL, ICRA, RSS), and reviews technical constraints to ensure ideas are falsifiable and practically achievable with current infrastructure.

Implements Open Policy Agent and Gatekeeper to enforce policy-as-code across Kubernetes clusters and CI/CD pipelines, covering Rego authoring, admission controller deployment, constraint templates, and local testing workflows for secure automation.

Uses the Shodan and InternetDB APIs in Python to enrich IP addresses, detail open ports/services, pull CVEs, and compute reputation scores to support threat triage and SOC workflows.

This autonomous skill orchestrates the entire scientific research lifecycle, guiding users from a vague research direction to a polished, review-ready document. The pipeline systematically executes Idea Discovery (Workflow 1), full-scale Implementation, Experiment Deployment (Workflow 2), automated Review Loops, and optional Paper Writing (Workflow 3). It handles everything from preliminary literature review and novelty checks to generating high-quality, reproducible code and final academic drafts.

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.

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.

Use Clarvia AEO (Agent Experience Optimization) to score any MCP server, API, or CLI tool for agent-readiness. It evaluates 15,400+ AI tools across four dimensions—API accessibility, data structuring, agent compatibility, and trust signals—ensuring the components are robust enough for advanced AI agent workflows and dynamic selection processes.

Combines regex signatures, heuristic scoring, and a DeBERTa classifier to detect prompt injection attempts against LLM-based applications, flagging or blocking malicious inputs before they reach the model and fitting into validation layers, audits, or incident response workflows.

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.

Generate publication-quality, complex diagrams and method illustrations for academic papers. This advanced tool uses a multi-stage, supervised workflow involving Claude (for planning/review) and Gemini (for layout/style optimization). It ensures structural correctness, adherence to academic standards (like CVPR/NeurIPS style), and iteratively refines the output until a high quality score is achieved. Ideal for researchers needing professional visual aids.

This workflow manages the complex process of responding to external academic reviews (e.g., ICML, OpenReview). It parses raw reviews, atomizes concerns into an issue board, and drafts safe, text-only rebuttals. The system enforces strict grounding (Provenance Gate) and commitment checks to ensure all claims are supported by the paper, reviews, or user-confirmed evidence, making it ideal for post-submission refinement.