Skill UI

Provides automated guidance for ab test analyzer workflows, covering SQL queries, visualization, statistical analysis, and BI validation so teams can run reliable experiments within data analytics contexts.

Provides automated, industry-standard guidance and code for complex SQL joins, visualizations, and BI analyses whenever the “complex join helper” skill is invoked in a data analytics context.

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.

This module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

A comprehensive workflow utilizing the Perplexity Sonar API for advanced, multi-turn research. It systematically decomposes broad topics into focused sub-queries, executes batch queries while maintaining context continuity, deduplicates citations, and synthesizes all findings into a structured, comprehensive research document. Ideal for academic research, market analysis, and generating detailed investment or industry briefs.

This skill integrates multiple digital health sources (such as Apple Health, Fitbit, Oura, and CSV/JSON files) into a unified system. It processes, standardizes, and validates diverse health data formats, and connects this data to a dedicated knowledge base (WellAlly.tech). It provides intelligent recommendations and article retrieval based on the user's current health context and needs.

This system deploys advanced AI, NLP, and behavioral analytics to detect sophisticated Business Email Compromise (BEC) attacks. Unlike traditional filters that rely on malicious links, this solution analyzes writing style, tone, grammatical patterns, and contextual anomalies to identify pure social engineering impersonation, providing a robust defense layer against advanced phishing and fraud.

Systematically optimizes Security Information and Event Management (SIEM) detection rules in platforms like Splunk and Elastic. This process involves analyzing historical alert volumes, establishing environmental baselines, creating context-aware whitelists, and statistically adjusting detection thresholds to significantly reduce false positives, improving overall security efficacy.

This comprehensive guide provides a detailed workflow for parsing Windows LNK shortcut files in a digital forensics context. It extracts critical artifacts including target paths, timestamps (creation, access, modification), volume serial numbers, and machine identifiers. It is essential for reconstructing user file access history, tracking accessed files on network shares, and building robust forensic timelines.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

Surfaces Monte Carlo data observability insights—health, lineage, alerts, and monitors-as-code—before developers edit dbt models or SQL pipelines, ensuring risk-aware code changes.