Skill UI

Analyze sexual health records, ED severity, STD screening, contraception effectiveness, and sexual activity trends. Useful for generating structured risk reports and linking sexual health data with medication, chronic disease, and lifestyle factors.

Amazon Macie is a fully managed AWS service utilizing advanced machine learning and pattern matching to discover, classify, and protect sensitive data (PII, financial records, credentials) stored in Amazon S3. It provides deep visibility into your cloud data, enabling comprehensive Data Loss Prevention (DLP) and compliance auditing by identifying specific sensitive data types through automated and targeted discovery jobs.

This skill utilizes AI/LLM reasoning capabilities combined with industry-standard OSINT tools (Sherlock, theHarvester, SpiderFoot). It correlates fragmented data—including usernames, emails, domain records, and breach data—from multiple sources into unified, confidence-scored intelligence profiles. It is ideal for deep target profiling, identity resolution, and mapping complex threat landscapes.

This skill de-identifies sensitive PII from clinical notes across numerous languages, including Spanish, German, French, and Arabic. It automatically selects language-specific PII models, national ID regex patterns, and generates locale-aware fake surrogates. Designed for global healthcare datasets, it runs entirely on-device, ensuring maximum privacy and linguistic accuracy for diverse international clinical records.

This skill converts free-text radiology narratives (CT, MRI, X-ray, etc.) into structured, machine-readable data. It automatically identifies and segments key report sections (Technique, Findings, Impression) and extracts critical clinical details such as anatomy, measurements, laterality, BI-RADS/Lung-RADS scores, and follow-up recommendations. It is crucial for building structured medical records and decision support systems.

This framework moves beyond simple storage solutions (like vector databases) to treat memory as a complex metabolic system. It provides tools and methodologies to audit, price, and govern an agent's memory, focusing especially on the forgetting policy. Key functions include cost profiling (construction vs. query energy), classifying stored records (fact/skill/log), and ensuring a mandatory forgetting policy to prevent state bloat and maintain performance.

This skill assists in designing tenant-scoped memory systems for AI agents, covering retrieval, retention, and storage strategies. It guides planning for RAG, conversation history, and vector stores while ensuring data isolation across sessions. Users learn to establish semantic and episodic records, manage permissions, and test retrieval quality before deployment.