1.2万 件 ・ 関連度順
概要と使いどころ
Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.
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Builds a structured SOC incident response playbook for ransomware attacks covering detection, containment, eradication, and recovery phases with specific SIEM queries, isolation procedures, and decision trees. Use when SOC teams need formalized response procedures for ransomware incidents aligned to NIST SP 800-61 and MITRE ATT&CK ransomware techniques.
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This skill outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF to cloud metadata services, and reporting findings aligned to MITRE ATT&CK Cloud matrix.
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Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.
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Conducts penetration testing of iOS and Android mobile applications following the OWASP Mobile Application Security Testing Guide (MASTG) to identify vulnerabilities in data storage, network communication, authentication, cryptography, and platform-specific security controls. The tester performs static analysis of application binaries, dynamic analysis at runtime, and API security testing to evaluate the complete mobile attack surface. Activates for requests involving mobile app pentest, iOS security assessment, Android security testing, or OWASP MASTG assessment.
日本語の概要は準備中です。原文の説明を表示しています。
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
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Analyzes UEFI bootkit persistence mechanisms including firmware implants in SPI flash, EFI System Partition (ESP) modifications, Secure Boot bypass techniques, and UEFI variable manipulation. Covers detection of known bootkit families (BlackLotus, LoJax, MosaicRegressor, MoonBounce, CosmicStrand), ESP partition forensic inspection, chipsec-based firmware integrity verification, and Secure Boot configuration auditing. Activates for requests involving UEFI malware analysis, firmware persistence investigation, boot chain integrity verification, or Secure Boot bypass detection.
日本語の概要は準備中です。原文の説明を表示しています。
Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into platform-specific queries using sigmac or pySigma backends.
日本語の概要は準備中です。原文の説明を表示しています。
Designs and documents structured incident response playbooks that define step-by-step procedures for specific incident types aligned with NIST SP 800-61r3 and SANS PICERL frameworks. Covers playbook structure, decision trees, escalation criteria, RACI matrices, and integration with SOAR platforms. Activates for requests involving IR playbook creation, incident response procedure documentation, response runbook development, or SOAR playbook design.
日本語の概要は準備中です。原文の説明を表示しています。
Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat detection rules from audit event patterns. Use when investigating Kubernetes cluster compromise or building k8s-specific SIEM detection rules.
日本語の概要は準備中です。原文の説明を表示しています。
Reverse engineer Go-compiled malware using Ghidra with specialized scripts for function recovery, string extraction, and type reconstruction in stripped Go binaries.
日本語の概要は準備中です。原文の説明を表示しています。
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
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Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
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Analyzes bootkit and advanced rootkit malware that infects the Master Boot Record (MBR), Volume Boot Record (VBR), or UEFI firmware to gain persistence below the operating system. Covers boot sector analysis, UEFI module inspection, and anti-rootkit detection techniques. Activates for requests involving bootkit analysis, MBR malware investigation, UEFI persistence analysis, or pre-OS malware detection.
日本語の概要は準備中です。原文の説明を表示しています。
Performs runtime mobile security exploration of iOS applications using Objection, a Frida-powered toolkit that enables security testers to interact with app internals without jailbreaking. Use when assessing iOS app security posture, bypassing client-side protections, dumping keychain items, inspecting filesystem storage, and evaluating runtime behavior. Activates for requests involving iOS security testing, Objection runtime analysis, Frida-based iOS assessment, or mobile runtime exploration.
日本語の概要は準備中です。原文の説明を表示しています。
Check docs/ai/ spec files and ADRs for inconsistencies with the actual codebase. Report only, no modifications.
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Check docs/ai/ spec files and ADRs for inconsistencies with the codebase, then fix all discrepancies.
日本語の概要は準備中です。原文の説明を表示しています。
Search for existing content pages containing a specific block in AEM Edge Delivery Services. Reports URLs with occurrences and variants to help identify test content during development.
日本語の概要は準備中です。原文の説明を表示しています。
Set a redesign direction for an existing website. Analyzes the user's intent, picks a palette and visual direction, and writes the target spec (PRODUCT.md, DESIGN.md, DESIGN.json) plus a reasoning trace at stardust/direction.md. Use when the user asks to redesign a site, refresh the design, set a new design direction, define a redesign target, or invokes /stardust:direct.
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Audit images across AEM Edge Delivery Services pages for SEO and performance. Checks alt text quality, missing dimensions, lazy loading on LCP images, fetch priority, file naming, and decorative image handling. Generates a per-page report with specific fix instructions for alt text (in the source document) and performance attributes (in block code or scripts). Use when optimizing Core Web Vitals, improving image accessibility, or auditing image SEO.
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Analyze and improve the internal link structure of an AEM Edge Delivery Services site. Builds a link graph from the query index and page content, identifies orphan pages, hub pages, and content silos, and generates specific linking recommendations with suggested anchor text and placement. Use when improving site navigation, fixing orphan pages, strengthening topical authority, or auditing link equity distribution.
日本語の概要は準備中です。原文の説明を表示しています。
AEM Cloud Service expert skill for Sling Scheduler. Routes to path-a.md (Runnable + OSGi properties) or path-b.md (Sling Jobs via JobManager). Covers classification, CS-specific constraints (no @SlingScheduled, multi-pod runOn, Boolean type hint), review checklist, troubleshooting fingerprints, and common pitfalls.
日本語の概要は準備中です。原文の説明を表示しています。
[BETA] AEM Cloud Service expert skill — migrate javax.inject.@Inject fields in Sling Model (@Model) classes to injector-specific annotations (@ValueMapValue / @OSGiService / @SlingObject). Self-discoverable (scan for @Model classes with field-level @Inject); the replacement is chosen deterministically from each field's declared type. Use for "fix @Inject in HeroModel.java", "modernize my Sling Models", or when scanning an AEM project for @Inject misuse. Detailed decision table, companion-annotation handling, import management, and file-level skip policy are in recipe.md. This skill is in beta. Verify all outputs before applying them to production projects.
日本語の概要は準備中です。原文の説明を表示しています。
Audit and optimize headings across AEM Edge Delivery Services pages for search intent, hierarchy, and consistency. Extracts all headings (H1-H6), evaluates uniqueness, keyword alignment, specificity, and structural correctness, then generates optimized heading suggestions per page. Use when improving on-page SEO, fixing heading hierarchy issues, or standardizing heading patterns across a site.
日本語の概要は準備中です。原文の説明を表示しています。