Skill UI

Guides teams through building an AI cold outreach stack—from ICP signals and enrichment to personalization, sequencing, sending, and AI follow-ups—so they can scale precise campaigns without burning deliverability.

Blueprint for B2B lead enrichment that sequences waterfalls, ICP scoring layers, and verification pipelines to maximize coverage, boost intent alignment, and keep cost per verified contact low.

This skill guides the creation of automated workflows for enriching raw Indicators of Compromise (IOCs) with multi-source threat intelligence context. It is designed for integration into SOAR platforms (e.g., Cortex XSOAR, Splunk SOAR) or Python pipelines. By automatically querying multiple sources like VirusTotal, AbuseIPDB, and MISP, it significantly reduces analyst mean time to triage (MTTT) and standardizes the resulting threat context and confidence scoring.

A comprehensive guide to building an automated Indicator of Compromise (IOC) enrichment pipeline using OpenCTI. This skill teaches how to leverage OpenCTI's native STIX 2.1 model and internal connectors to pull deep context from major external threat intelligence sources like VirusTotal, Shodan, AbuseIPDB, and GreyNoise. The pipeline automatically enriches newly ingested indicators, correlates them with known threat actors and campaigns, and generates confidence scores for rapid analyst prioritization.

A comprehensive guide to building automated Indicator of Compromise (IOC) enrichment pipelines within Splunk Enterprise Security. This process integrates multiple external threat feeds (STIX/TAXII, CSV, APIs) into normalized KV Store collections. By correlating these enriched threat indicators against live security events using advanced lookup searches, SOC teams can significantly reduce manual triage time and automatically identify potential security incidents.

This skill guides the implementation of Security Orchestration, Automation, and Response (SOAR) workflows using platforms like Splunk Phantom. It automates the entire incident lifecycle, including alert triage, IOC enrichment (via VirusTotal, CrowdStrike), containment actions, and ticket creation (ServiceNow). Ideal for SOC teams needing to reduce Mean Time To Respond (MTTR) and standardize complex security procedures across multiple tools.

This skill guides the systematic management of Indicators of Compromise (IOCs). It covers the entire lifecycle, from initial discovery and validation through enrichment (e.g., VirusTotal, Shodan) and deployment to SIEM/IDS. Key features include confidence decay over time, false positive tracking, and automated retirement policies, ensuring the threat intelligence database remains high-quality and actionable.

This skill automates the enrichment of Indicators of Compromise (IOCs) by orchestrating lookups across multiple industry-leading threat intelligence sources (e.g., VirusTotal, AbuseIPDB, Shodan). It provides contextual scoring and disposition recommendations for IPs, domains, URLs, and hashes. Ideal for SOC analysts during alert triage or incident investigation to quickly assess risk and scope.

This skill utilizes the Shodan API to perform comprehensive IP reputation analysis. It enriches IP addresses by identifying open ports, running services, known vulnerabilities (CVEs), and hosting context. It is crucial for threat intelligence enrichment, incident triage, and security assessments, helping security professionals assess the risk level and potential compromise of exposed infrastructure.

This skill leverages the VirusTotal API v3 to enrich file hashes (MD5, SHA-256). It retrieves comprehensive security data, including multi-engine detection rates, sandbox behavioral reports, YARA rule matches, and related Indicators of Compromise (IOCs). It is essential for SOC triage, incident response, and threat intelligence workflows.

Unified CLI and Python interface to 20+ bioinformatics databases for gene info, BLAST/BLAT, viral sequences, AlphaFold structures, enrichment, disease and expression queries.

This skill provides advanced sentiment analysis tailored for financial news and market texts. It supports two modes: high-speed, localized analysis using FinBERT, and advanced, reasoning-based analysis via LLM. Users can determine the sentiment polarity (positive, negative, or neutral), score, and specific reason for financial content, making it ideal for market intelligence and data enrichment.