Skill UI

Claude-style communications literature review skill that prioritizes knowledge bases (Zotero, Obsidian, local libraries) before IEEE Xplore, ScienceDirect, ACM, and the broader web, covering wireless, cellular, satellite, routing, congestion control, and related systems when the user needs papers or landscape summaries.

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.

Detects AI-generated voice phishing by extracting MFCC and spectral cues, scoring batches, and outputting forensic reports for incident response and red/blue team exercises.

Detects and prevents injection attacks targeting serverless functions by combining static analysis, event mapping, runtime behavior monitoring, and IAM auditing so SOCs can spot poisoned triggers, malicious layers, and privilege escalations across Lambda, Azure, and GCP environments.

Automatically detects typosquatting in npm and PyPI by generating candidate variants from popular dependencies, querying each registry, and scoring matches with Levenshtein distance, publication recency, and download anomalies so teams can flag suspicious packages before installation.

This skill acts as a crucial bridge, automating the transition from a conceptual experiment plan to fully deployed and executable code. It parses structured experiment roadmaps, implements necessary training/evaluation scripts, manages dependencies, and deploys the experiment setup to GPU environments. It ensures reproducibility through mandatory code review and structured result collection.

A comprehensive, library-agnostic checklist covering best practices for Flutter and Dart applications. It guides developers on architectural principles, state management patterns (BLoC, Riverpod, etc.), Dart idioms, performance optimization, and clean code principles, making it invaluable for code reviews.

Draft a comprehensive, structured grant proposal ready for submission. This skill supports major international and national funding agencies (e.g., NSF, KAKENHI, ERC, NSFC). It guides users through a full research-to-funding pipeline, ensuring the proposal addresses feasibility, novelty, and specific reviewer expectations for maximum success.

This library provides comprehensive patterns for building robust Clinical Decision Support Systems (CDSS) integrated into Electronic Medical Records (EMR). It ensures patient safety by implementing critical checks such as drug interaction screening, weight/age/renal-adjusted dose validation, and standardized clinical scoring (e.g., NEWS2). The engine uses pure functions, guaranteeing high testability and reliability for critical medical insights.

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

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.

A comprehensive tool and API wrapper for accessing, parsing, and analyzing research papers published on Hugging Face and arXiv. Users can input various URLs or IDs (e.g., arXiv ID) to retrieve structured metadata, full markdown content, and automatically link associated resources such as models, datasets, and application spaces. Ideal for researchers and developers needing structured access to cutting-edge AI knowledge.