Skill UI

This local developer tool inspects dependency manifests (requirements.txt, pyproject.toml, package.json) for surface-level issues like standard-library shadowing, unpinned dependencies, and conflicts. It provides plain-language explanations and suggested fixes, helping developers debug dependency rot before deeper installation attempts.

This skill deploys the OpenMed Model Context Protocol (MCP) server, exposing critical clinical NLP tools such as NER, PII extraction, and de-identification. It enables AI coding agents and chat clients to interact with OpenMed's capabilities via simple tool calls, running locally on-device to ensure user data privacy and security. Supports both stdio and Streamable HTTP deployment modes.

A specialized tool designed to detect, suggest, and evaluate Gang of Four (GoF) design patterns within TypeScript and JavaScript codebases. It analyzes code structure, identifies code smells, and provides stack-aware refactoring recommendations, supporting frameworks like React, Angular, NestJS, and Vue.

This expert, interactive assistant provides comprehensive troubleshooting for complex issues encountered with Claude Code. It systematically scans the user's environment, checks global and project configurations, and cross-references symptoms against a deep knowledge base to pinpoint root causes and provide actionable, step-by-step solutions. Ideal for resolving integration errors, permission issues, or unexpected behavior.

A comprehensive methodology for advanced red teaming and adversary emulation. This skill details how to bypass modern Endpoint Detection and Response (EDR) solutions (e.g., Defender, CrowdStrike, SentinelOne). Techniques include unhooking ntdll, utilizing indirect syscalls (Hell's Gate), patching ETW providers, and AMSI context blinding. Essential for authorized penetration testing and self-developed implant development, aligning with MITRE ATT&CK T1562.

This tool systematically audits and scores defined AI agents to ensure they are correctly scoped, highly specific, and safe for programmatic orchestration. It checks for description ambiguity, over-permissive tool grants, and anti-patterns that break headless workflows. Use it when onboarding new agents, integrating agent fleets, or debugging inconsistent multi-agent selection.

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.

An advanced tool designed to audit, validate, and maintain rule files (e.g., .claude/rules/). It checks structural correctness, validates glob patterns against actual project files, and runs an interactive session to confirm or improve the relevance of each rule. Ideal for initial setup, debugging, or periodic rule hygiene.

This tool audits all existing skills within the current project. It performs a comprehensive quality check, validating frontmatter completeness, tool scoping, and content quality against established standards. It produces a detailed, scored report, including effort-level recommendations for each skill. Ideal for new project onboarding, pre-release quality assurance, or diagnosing skill triggering issues.

This skill evaluates de-identification and clinical NER models against rigorous leakage-first release gates (G1a-G8). Instead of relying on aggregate F1 scores, it focuses on detecting residual Protected Health Information (PHI) leakage and enforcing per-label recall floors, which is crucial for HIPAA compliance. Determines if a model is RELEASABLE or QUARANTINED.

A comprehensive guide for designing and building large-scale, resilient platforms using the evolutionary modular monolith approach. It enforces strong logical boundaries using DDD principles (bounded contexts, flat-by-aggregate) and implements necessary patterns like Anti-Corruption Layers and transactional outboxes. Use this when defining a system's structure, choosing between monolith and microservices, or ensuring robust decoupling from external vendors (e.g., ERP, AI).

Utilizes OpenMed's analyze_text to perform sophisticated Named-Entity Recognition (NER) on clinical and biomedical text. This tool extracts structured entities such as diseases, drugs, anatomy, genes, and proteins. It provides precise character spans and model confidence scores, making it essential for building robust, privacy-first clinical Natural Language Processing (NLP) pipelines.