Skill UI

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 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).

This skill loads and parses a structured handoff document, enabling the user to resume work from a previous session. It systematically extracts the project's scope, files involved, completed tasks, key discoveries, and next steps. Crucially, it summarizes its understanding and mandates user confirmation before taking any action, ensuring accurate context and continuity.

A comprehensive guide for installing, configuring, and validating the local long-term memory plugin (`memory-tencentdb`) within the OpenClaw environment. This guide covers everything from environment pre-checks to advanced configuration for data capture, information extraction, context recall, and vector embedding, ensuring robust, self-contained memory capabilities for RAG applications.

This skill connects extracted drug entities with authoritative regulatory facts from the public OpenFDA API. It retrieves Structured Product Labeling (SPL) data, NDC package codes, approved indications, boxed warnings, and current recall/enforcement status. It is essential for enriching drug profiles, providing regulatory context, and supporting unexpectedness checks. Note: This skill provides information, not clinical decision support.

A robust template designed for creating complex, multi-stage commands or 'recipes.' It enforces a highly structured process by mandating 'Context Validation Checkpoints' before any step executes. This pattern ensures that preconditions are explicitly verified, significantly increasing the reliability and predictability of complex automated workflows.

This skill enhances raw clinical entity recognition by assigning three critical axes: Negation, Temporality, and Uncertainty (ConText). It determines the clinical status of a mention (e.g., affirmed, negated, historical, or uncertain), ensuring that basic NER results are transformed into clinically faithful assertions before grounding to standards like FHIR. Essential for accurate problem list generation and differential diagnosis support.

This skill automatically wraps high-verbosity shell commands (such as git log, cargo test, pytest, or find) using an RTK wrapper. It detects excessive CLI output and executes optimized commands, dramatically reducing the number of tokens consumed in the context window. Use this when standard command output is overly verbose and wasting valuable context tokens.

This technique segments unstructured clinical documents (such as HPI, PMH, and A&P sections) into contextually rich, canonical chunks. This is crucial for improving the precision of downstream NLP tasks like Named Entity Recognition (NER) and de-identification. By identifying and normalizing headers and mapping them to LOINC codes, the system ensures that the source context (e.g., historical vs. active) is maintained, leading to significantly more accurate results.

This tool captures the complete context of an ongoing session into a highly structured handoff file. It meticulously records key decisions, files modified, current operational status, and a clear, ordered list of next required steps. Use this feature when pausing work, switching tasks, or handing off the project to another instance to ensure zero context loss and immediate resumption.

This skill processes verbose, stream-of-consciousness voice dictations (from tools like macOS Dictation or Wispr Flow) and transforms them into highly structured, token-efficient prompts. It removes filler words, repetitions, and tangents, organizing the core intent into standard sections (Context, Objective, Constraints) suitable for advanced LLMs like Claude Code.

This skill is designed to refine and improve the clarity, professionalism, and structure of any user-provided text. It acts as a highly cooperative writing partner, helping users polish drafts for various professional contexts, such as reports, emails, or academic writing, ensuring the final output is clear and impactful.