Skill UI

This tool is designed to analyze complex Machine Learning experiment results from various sources (JSON/CSV). It computes detailed statistics, builds comprehensive comparison tables across hyperparameter variations, and generates structured, actionable insights. It helps researchers interpret findings, identify trends, and propose next steps for research.

This skill outlines the comprehensive process of memory forensics on compromised Linux systems. It guides users through acquiring volatile memory using the LiME kernel module and analyzing the resulting image with the Volatility 3 framework. Key artifacts extracted include process lists, network connections, bash history, and loaded kernel modules, making it an essential technique for incident response, threat hunting, and security investigations.

Analyze email headers to uncover sender origins, routing chains, and authentication results, enabling defenders to validate legitimacy and spot phishing indicators using manual inspection and automation scripts.

This guide provides a comprehensive methodology for conducting cloud security audits across major platforms (AWS, Azure, GCP) using industry-standard CIS Benchmarks. It covers selecting appropriate benchmark versions, running automated assessments using tools like Prowler and ScoutSuite, interpreting complex compliance results, and prioritizing systematic remediation steps to achieve and maintain continuous compliance (e.g., SOC 2, ISO 27001).

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.

This skill automates the process of parsing Cyber Threat Intelligence (CTI) reports to extract adversary behaviors. It maps these behaviors to MITRE ATT&CK techniques and structures them as STIX 2.1 Attack Pattern objects. The resulting library supports detection engineering by generating rule templates (Sigma, YARA) for proactive defense and threat-informed security control implementation.

DefectDojo is an open-source platform designed to centralize, aggregate, and manage security vulnerabilities. It integrates findings from over 200 security scanners (e.g., Nessus, ZAP, Trivy), automatically deduplicates results, tracks remediation progress, and provides executive-level dashboards. It supports robust workflows, including CI/CD pipeline integration and Jira ticketing, making it essential for DevSecOps practices and compliance adherence.

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.

This skill automates the entire iterative process of Design Space Exploration (DSE), primarily for computer architecture and EDA problems. It executes complex programs, analyzes resulting objective metrics, and intelligently tunes input parameters (e.g., cache size, pipeline width, optimization passes). It continues iterating until a predefined objective is met or a timeout is reached, making it ideal for optimizing complex system and hardware configurations.

This utility integrates status updates and notifications with Feishu/Lark. It is designed as an internal, zero-impact tool that other complex skills (e.g., experiment runners, review loops) call to report key events like test completion, scoring results, or reaching a checkpoint. It supports both 'push' (fire-and-forget status updates) and 'interactive' (bidirectional approval/rejection) modes, ensuring seamless workflow visibility and robust operation even if configuration is missing.

This comprehensive workflow guides the user through a complete, automated pipeline to transition from a broad research direction to validated, pilot-tested, and refined research ideas. It systematically executes literature review, idea generation, novelty checks, critical feedback, and proposes detailed experimental plans, resulting in a comprehensive idea report and actionable proposal documents.

Automate the deployment of osquery scheduled queries to continuously monitor endpoints, covering process inventory, network listeners, file integrity, and persistence mechanisms; generate osquery.conf, configure differential logging, analyze suspicious results, and produce a security report.