Skill UI

This tool provides comprehensive usage analytics and reporting for Kling AI video generation. It logs detailed events (submission, completion, errors) into structured JSONL files. Users can track performance metrics, analyze success rates, monitor total resource consumption (credits), and generate cost summaries over defined periods. Ideal for building dashboards, cost auditing, and understanding usage patterns.

This tool parses Kubernetes API server audit logs (JSON lines) to detect critical security events. It specifically targets unauthorized access, secret enumeration, RBAC escalation, privileged pod creation, and shell access (exec/attach). Use it for incident response, threat hunting, or building SIEM detection rules.

Timesketch is a collaborative, open-source platform for security teams to analyze and reconstruct complex forensic timelines. It ingests and normalizes multi-source security data (logs, artifacts) using methods like Plaso, CSV, and JSONL. Users can visualize event chronology, track attack chains, and utilize advanced built-in analyzers (e.g., Sigma, Chain of Events) for comprehensive incident investigation and documentation.

This skill provides a comprehensive framework for detecting Living Off the Land Binaries (LOLBAS) abuse, such as misuse of certutil, regsvr32, and mshta. It leverages process telemetry from Sysmon and Windows Event Logs, combined with advanced Sigma rule-based detection and parent-child process anomaly analysis. Ideal for SOC analysts and threat hunters investigating sophisticated adversaries aiming to evade traditional security controls.

This skill analyzes Windows Security Event Logs (EVTX) to detect RDP brute force attacks. It parses failed logon events (ID 4625) and correlates them with successful logons (ID 4624), performing source IP frequency analysis to identify attack patterns, username spraying, and potential account compromises. This is critical for SOC analysts and threat hunters investigating lateral movement or credential stuffing attempts.

A comprehensive workflow for extracting, parsing, and analyzing Windows Event Logs (EVTX). It guides security professionals in using advanced tools like Chainsaw and Hayabusa, combined with Sigma rules, to detect critical security incidents such as lateral movement, privilege escalation, and persistence mechanisms during incident response and digital forensic investigations.

This skill detects WMI-based lateral movement by analyzing key Windows Security Event ID 4688 and Sysmon Event ID 1 logs. It focuses on identifying suspicious process execution patterns, such as WmiPrvSE.exe spawning unauthorized child processes (cmd.exe, powershell.exe), suspicious command lines, and WMI event subscriptions used for persistence. Ideal for security incident response and threat detection.

This skill provides a structured methodology for detecting process injection techniques (MITRE ATT&CK T1055), which adversaries use for defense evasion and privilege escalation. It analyzes granular security logs, specifically utilizing Sysmon Event IDs 8 (CreateRemoteThread) and 10 (ProcessAccess), alongside source-target process relationship mapping to identify suspicious malicious behavior over general system activity.

This guide demonstrates how to build comprehensive forensic super-timelines using Plaso (log2timeline) to correlate events across diverse sources. It covers the entire workflow, from processing disk images and various logs (MFT, registry, browser history) to generating standardized CSV, JSONL exports, and visualizing the results in tools like Timesketch for deep investigative analysis.

A comprehensive guide and implementation template for building robust data ingestion pipelines into ClickHouse. Supports multiple sources including HTTP webhooks (with batching), Kafka streams, S3, and direct SQL inserts. Demonstrates best practices for data handling, such as deduplication using ReplacingMergeTree and monitoring query logs, ensuring reliable, scalable data analytics.

KAPE is a powerful, Windows-native triage tool used for Digital Forensics and Incident Response (DFIR). It executes in two phases: 'Targets' collect raw forensic artifacts (like registry hives, event logs, and prefetch files) from live systems, while 'Modules' parse this collected data into normalized, analyst-ready CSV/JSON formats. It enables fast, defensible, and scalable artifact collection, especially when full disk imaging is impractical.