Skill UI

This comprehensive guide identifies and explains common anti-patterns, gotchas, and integration mistakes when using Clay's data enrichment platform. It details the root causes of credit waste, workflow failures, and data loss, providing actionable fixes for auditing configurations, onboarding new team members, or optimizing existing pipelines.

A comprehensive guide for defining, tracking, and interpreting product metrics across all stages—from discovery to maturity. This skill covers selecting metric frameworks (AARRR, HEART), defining stage-appropriate KPIs, designing multi-layered dashboards, and executing deep analyses like cohort and retention modeling. It helps teams transform raw data into actionable product strategies, moving beyond vanity metrics.

This skill provides structured procedures and Python examples for detecting internal data exfiltration. It analyzes endpoint, cloud, and email logs by establishing behavioral baselines and performing statistical anomaly detection. Use cases include investigating insider threats, monitoring DLP policy violations, and building robust User and Entity Behavior Analytics (UEBA) rules for SOC teams.

This skill guides security teams on systematically tuning SIEM detection rules in platforms like Splunk and Elastic. It focuses on reducing alert fatigue and false positives by analyzing historical alert volumes, establishing environmental baselines, creating context-aware whitelists, and measuring detection efficacy using metrics like precision and recall. This is crucial for maturing a Security Operations Center (SOC).

This skill provides a comprehensive framework for vulnerability prioritization by combining three crucial data points: CISA's Known Exploited Vulnerabilities (KEV) catalog (confirming real-world exploitation evidence), EPSS scores (predicting exploitation probability), and CVSS metrics (assessing intrinsic severity). It builds a multi-factor prioritization engine to determine the most critical CVEs for immediate remediation, assisting security teams in managing large vulnerability backlogs efficiently.

This comprehensive guide outlines a structured workflow for investigating insider threats—incidents where authorized employees or contractors misuse their access to steal data or sabotage systems. It integrates DLP alerts, User Behavior Analytics (UBA), and digital forensics, emphasizing mandatory collaboration between Legal, HR, and Security teams to build an admissible, evidence-based case.

This skill acts as a critical gatekeeper after a set of experiments are completed. It systematically evaluates raw experimental metrics and outcomes against predefined research claims. The goal is to determine whether the results adequately support a claim, contradict it, or if critical evidence is missing. It guides the research team towards the next decisive action: pivoting the hypothesis, supplementing the data, or confirming the findings. Use this before writing final reports.

Use this skill to comprehensively verify all constraints related to a candidate cricket player. By cross-referencing structured data from sources like Wikipedia or Cricinfo, you can confirm details such as birth date, debut match specifics (format, opponent, score), career records, and teams played for. It is crucial for establishing factual accuracy, especially when distinguishing between different formats like ODI, T20I, and Test matches.

A comprehensive, domain-agnostic 13-agent system designed for rigorous academic research on any topic. It guides users through the entire research lifecycle, supporting deep literature reviews, systematic meta-analysis, cross-source synthesis, bias assessment, and generating professional APA 7.0 reports. Ideal for academics, students, and analysts needing structured, deep insights.

A comprehensive pre-submission strategy guide designed to 'red-team' theoretical and econometric manuscripts. This tool helps authors anticipate and pre-empt harsh critiques from expert co-editors and referees, focusing specifically on identifying proof gaps, testing the generality of claims, ensuring finite-sample evidence, and addressing potential methodological competitors before journal submission.