Skill UI

This skill helps discover natural groupings within a large collection of semantic embeddings using the `ruvector` tool. It employs graph-based algorithms (Spectral/Louvain) to cluster code or data, revealing underlying themes, identifying outliers, and organizing vast vector sets. Ideal for analyzing codebases or large datasets to understand relationships between entries.

A comprehensive quality gate designed to validate all marketing content (ads, blogs, emails, etc.) before publication. It checks for factual accuracy (hallucination detection, claim verification), brand voice consistency, structural adherence, and compliance (C2PA provenance). Use this skill as the final checkpoint for all client-facing copy.

This tool identifies, models, and optimizes compounding growth loops (e.g., viral, content, data, paid) within a business. By analyzing key metrics, calculating amplification factors, and determining cycle times, it pinpoints growth bottlenecks and proposes actionable strategies, ensuring sustainable and scalable business expansion.

A comprehensive guide detailing the high standards required for presenting security evidence at major conferences. It covers methodologies for proving claims—including attacks, defenses, and detection systems—across live internet measurements, controlled testbeds, and emulation. Key requirements include adaptive adversary modeling, rigorous measurement hygiene, and adherence to ethical review protocols to ensure results are reproducible and relevant to real-world networks.

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 tool benchmarks the recall of PII detection models, crucial for ensuring privacy compliance before deployment. It uses synthetic fixtures and enforces a recall floor by measuring label-aware exact-span and grapheme recall against predefined gold standards. It is essential for auditing models and preventing critical privacy leaks.

This module provides a lazy adapter registry to seamlessly integrate OpenMed's clinical PII detection with major NLP frameworks like Microsoft Presidio, spaCy, and LangChain. It allows users to use OpenMed for enhanced PII recall, merge spans, or implement on-device PHI redaction guardrails without rewriting entire pipelines.

A powerful post-deployment monitoring tool designed for continuous quality assurance in CI/CD pipelines. It captures a performance baseline before deployment and then continuously monitors live applications after deployment. It alerts on critical failures (e.g., 5xx errors), performance regressions, and unexpected changes, ensuring that new releases maintain production stability.

This skill helps developers programmatically discover, filter, and inspect the entire OpenMed model registry. It allows users to find the correct model key for specific clinical tasks (e.g., diseases, oncology, PII detection), filter by language, size, or task, and review model metadata before loading. It pairs with `loading-openmed-models`.

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 skill implements a critical CI gate to ensure the privacy and performance integrity of de-identification models. It automatically fails the build if the model's recall on held-out PHI data drops below the defined threshold, or if any critical Personally Identifiable Information (PHI) is leaked (leakage must be zero). It enforces strict privacy standards in continuous integration pipelines, blocking unsafe merges.

A comprehensive methodology for deep security testing of both Android (APK) and iOS (IPA) applications. This guide covers the entire workflow, from initial information gathering and static analysis (decompilation, code structure analysis) to advanced dynamic instrumentation, network interception, and bypassing modern security measures like SSL Pinning, Root/Jailbreak detection, and anti-debugging.