Skill UI

This skill provides comprehensive expertise for senior database administrators to optimize and manage PostgreSQL databases at an advanced level. Use it for deep query analysis via EXPLAIN ANALYZE, implementing advanced features like JSONB indexing, configuring streaming or logical replication, and maintaining database health through VACUUM tuning and monitoring pg_stat views. Ideal for building highly available, high-performance, and resilient system architectures.

Master systematic methodologies for using Shodan to perform deep reconnaissance during penetration testing. This skill covers querying exposed services, mapping assets, identifying vulnerable systems (CVEs), and utilizing advanced filters for IP ranges, geographical locations, and organizational data. Ideal for security researchers and ethical hackers.

This skill guides SOC analysts and detection engineers on how to correlate complex, multi-stage attacks within the IBM QRadar SIEM platform. It covers advanced usage of AQL (Ariel Query Language) for deep event investigation, building custom correlation rules (Building Blocks and Offenses), and cross-source data correlation (e.g., auth failures with network flows). It is essential for reducing false positives and improving threat detection accuracy.

A comprehensive guide to identifying covert data exfiltration channels using Zeek logs. This technique analyzes DNS traffic for high-entropy subdomains, unusually long query lengths, abnormal record types (TXT/NULL), and excessive query volume, which are key indicators of malicious DNS tunneling or command-and-control (C2) activity.

This skill details the deployment of Privileged Access Management (PAM) for critical database systems, including Oracle, SQL Server, PostgreSQL, and MySQL. It covers essential security controls such as session proxy configuration, credential vaulting, query auditing, and enforcing least-privilege access, helping organizations meet stringent compliance and security standards.

Utilize Velociraptor, an advanced open-source platform, to collect, query, and monitor forensic artifacts across diverse endpoints (Windows, Linux, macOS). This skill enables incident response teams to perform large-scale, rapid digital forensic investigations by collecting critical data such as event logs, registry entries, prefetch files, and network metadata using VQL queries.

A comprehensive guide to investigating AWS account compromises by querying CloudTrail logs. Use boto3 or AWS Athena to filter suspicious activities—such as unauthorized API calls, S3 data exfiltration, or IAM privilege escalation—to reconstruct a detailed attacker timeline and generate a professional forensic report.

A comprehensive guide for diagnosing production performance issues in Qdrant vector databases. Learn how to systematically check the optimizer status, analyze high memory usage (OOM crashes), and troubleshoot slow query latencies using key metrics and dedicated API endpoints. Essential for maintaining optimal operational health.

This guide provides a comprehensive framework for investigating production issues on the Nexu platform using the Datadog Logs API. It details how to query crash events, analyze OpenClaw stderr output, check gateway startup status, and review API request logs (including 5xx errors). It includes best practices for authentication, filtering by pod/time, and parsing raw JSON logs using Python for actionable insights.

This guide provides comprehensive guidance on scaling Qdrant, a vector database. It helps users diagnose performance bottlenecks and determine the optimal horizontal or vertical scaling strategy. Strategies are categorized based on specific needs: increasing data volume, improving query throughput (QPS), minimizing query latency, or managing high query result volumes.

This comprehensive guide provides advanced strategies for optimizing and scaling Qdrant vector database throughput (QPS). It covers performance tuning techniques, such as optimizing segment size, enabling quantization, and using batch search. Furthermore, it details horizontal scaling methods using read replicas and addressing disk I/O bottlenecks, ensuring high performance for large-scale, high-concurrency retrieval applications.

This component is an essential extension for KubeSphere Observability Platform, designed for comprehensive collection, processing, and storage of Kubernetes and KubeSphere audit events. It supports various sinks, including OpenSearch and Doris, and provides an API for querying historical audit data, ensuring robust auditing capabilities for platform operations.