Skill UI

This comprehensive skill provides deep integration with Jira, enabling users to manage the entire issue lifecycle. Features include retrieving ticket details, extracting testable requirements and acceptance criteria, updating issue status (transitions), adding comments, and linking code artifacts (PR/MR). Supports both recommended MCP server approach and direct REST API calls for robust development workflow automation.

This tool is designed to analyze source code by extracting core entities (classes, functions, modules, types) and mapping their complex relationships (imports, inheritance, calls, dependencies). It constructs a comprehensive Knowledge Graph (KG) in AgentDB. This is essential for detailed dependency analysis, understanding large codebase structures, and performing accurate impact assessments.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

RouterBase acts as a unified, OpenAI-compatible API gateway, allowing applications to route requests across diverse large language models (LLMs) like GPT, Claude, and Gemini. It helps developers migrate existing OpenAI SDK calls, implement sophisticated primary/fallback model selection based on cost, latency, and quality, and manage multimodal payloads (image, audio, video) from a single, standardized endpoint.

This tool assesses whether coding agents adhere to defined skills, rules, or agent definitions. It automatically generates scenarios with varying prompt strictness, executes the agent, and generates a comprehensive report detailing the compliance rate of tool calls against predefined workflow specifications using advanced LLM classification.

This skill provides a rigorous framework to evaluate the broad significance and quality of scientific research. It guides authors in determining if their work meets the standards for top-tier journals like PNAS, Science, or Nature, helping to make realistic calls on the optimal publication venue before drafting or after review.

This skill bridges theoretical econometric designs (DiD, IV, RDD, SCM, DML) to concrete, audited statistical estimates. It orchestrates the necessary tool calls to fit models, generate comprehensive diagnostics, and report reliable findings, ensuring that every academic claim is backed by executable, traceable code. Essential for advanced causal inference research.

webmcpify is a specialized pipeline designed to make existing web applications agent-ready. It extracts user-facing functionality and exposes it as WebMCP tools, allowing AI agents to interact with the application via structured, reliable tool calls rather than fragile DOM scraping. The tool guides developers through detection, inventory, integration, verification, and healing phases to ensure robust AI interoperability.

This skill acts as a comprehensive map for developing clinical and biomedical NLP pipelines using OpenMed. OpenMed is a local-first Python library that performs named-entity recognition (NER), protected health information (PHI) de-identification, and FHIR export, all running fully on-device without cloud calls. Use this guide when starting a new clinical text project, needing to select the appropriate capability (e.g., zero-shot NER, auditing, or FHIR conversion), or requiring an entry point for the core OpenMed features.

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 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.

A comprehensive, systematic workflow for reverse engineering client-side JavaScript. It guides users through five stages: Observation, Capture, Rebuilding, Patching, and Deep Dive. Applicable for locating API signatures, analyzing encryption parameters, tracking runtime function calls, and reconstructing complex web environments locally for evidence collection and reproduction. Utilizes advanced tools like jshookmcp and various js-reverse_* utilities.