Skill UI

This comprehensive guide details the process for managing service transitions on the TwinMind platform. It instructs users on how to audit current usage, export all key configurations (settings, integrations, webhooks), and execute plan upgrades (Pro or Enterprise) via both the dashboard and API. This ensures a seamless and controlled migration between service tiers.

A dedicated tool for forensic analysis of Windows PowerShell Script Block Logs (Event ID 4104) captured in EVTX files. It reconstructs multi-block scripts, applying advanced detection heuristics such as entropy analysis and pattern matching to identify obfuscated commands, encoded payloads (Base64), download cradles, and AMSI bypass attempts, crucial for threat hunting and incident response.

Shellbags are registry artifacts that track user folder browsing activity (view settings, access path) via Windows Explorer. This method reconstructs a user's interaction history with local drives, network shares, and removable media, providing definitive evidence of folder access even when the directories are deleted. Essential for digital forensics and incident response.

This skill parses various cyber threat intelligence (CTI) reports (Mandiant, CrowdStrike, etc.) using NLP and specialized libraries (stix2, mitreattack-python, spaCy). It systematically extracts adversary behaviors, maps them to MITRE ATT&CK IDs, and constructs a searchable STIX 2.1 Attack Pattern library. This is essential for threat-informed detection engineering, generating detection rules (Sigma, YARA), and validating security controls.

A comprehensive guide for advanced threat hunting, focusing on detecting DLL side-loading and search-order hijacking (MITRE T1574). It instructs users to analyze Sysmon Event ID 7 logs, validate file signatures and hashes, and identify path anomalies to uncover APT persistence mechanisms. Essential for incident response and proactive threat detection in enterprise environments.

This skill details the detection of Pass-the-Ticket (PtT) attacks, a critical credential theft technique. It instructs users on analyzing Windows Security Event IDs (4768, 4769, 4771) within SIEM platforms like Splunk or Elastic. Use cases include investigating security incidents, building advanced threat hunting queries, and validating SOC monitoring coverage for lateral movement attacks.

This guide details the end-to-end process for executing authorized phishing simulations. It instructs security teams and red teams on designing realistic social engineering pretexts, setting up credential harvesting infrastructure (using tools like GoPhish), and measuring key metrics such as open rates, click-through rates, and credential submission rates. It is crucial for assessing organizational human security awareness and testing email security controls (e.g., DMARC, SPF, DKIM).

This guide details a structured threat hunting methodology designed to proactively detect sophisticated spearphishing campaigns. It instructs users on correlating indicators across email logs, endpoint telemetry (EDR), and network data (SIEM) to identify targeted threats, supporting incident response and security posture improvement.

This comprehensive guide covers advanced Dart 3 features, including pattern matching, exhaustive switch expressions, and records. Learn how to refactor complex if-else chains, perform type-safe destructuring from various data sources (maps, lists, records), and utilize modern control flow constructs for writing idiomatic, robust, and highly maintainable Dart code.

A comprehensive guide detailing recommended naming conventions for various Go language elements, including packages, structs, interfaces, constants, functions, and error handling. Use this skill when writing, reviewing, or refactoring Go code to ensure adherence to idiomatic Go style.

Guides Go developers through struct/interface design best practices, covering interface segregation, composition, dependency injection, type assertions, and choosing pointer vs value receivers to keep APIs clear and testable.

This technique guides users on decomposing multi-constraint questions, especially in the domain of football trivia or historical research. Instead of searching all criteria simultaneously, it instructs users to rank constraints by distinctiveness (e.g., specific match results first, then historical milestones). By performing incremental, cascading searches, the system dramatically narrows down the result set, leading to more accurate information retrieval.