3,916 件 ・ 関連度順
概要と使いどころ
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。
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration 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。
Analyzes malicious Linux ELF (Executable and Linkable Format) binaries including botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure. Covers static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM ELF samples. Activates for requests involving Linux malware analysis, ELF binary investigation, Linux server compromise assessment, or container malware analysis.
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。
Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment triage.
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.
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。
Create full documentation for a new OpenMetadata connector from scratch — generates main page, yaml.mdx, troubleshooting page, and registers in docs.json navigation. Derives features, permissions, and YAML config from JSON schema and source code.
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。
Investigate and triage runtime incidents involving the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration in AEM as a Cloud Service environments only, using runtime-backed MCP evidence.
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。
Validate an AEM Edge Delivery Services sitemap.xml against actual site content. Cross-references the sitemap with the query index, checks URL reachability, validates lastmod dates, and identifies missing or orphaned pages. Use when auditing SEO health, preparing for launch, or investigating indexing issues.
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。
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。
Creates complete AEM components with dialog, HTL template, Sling Model, unit tests, and clientlibs. Supports extending Core Components and project components. When a Figma design URL is provided, fetches the design via Figma MCP (get_design_context) and translates it into pixel-perfect HTL, CSS, and JS. Follows Adobe Experience League best practices for AEM Cloud Service and 6.5. Use this skill whenever the user mentions creating, building, generating, or scaffolding an AEM component, or mentions component types like teaser, card, hero, banner, accordion, tabs, carousel, list, navigation, breadcrumb, or any custom AEM component. Also trigger when the user wants to extend a Core Component, create a component dialog, add a Sling Model, or convert a Figma design into an AEM component.
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。
Build Flutter/Dart cross-platform apps with widgets, state management, navigation, tests, and performance patterns.
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。
Build React Native/Expo mobile apps with navigation, native UI, state, networking, tests, and release workflows.
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。
Creates comprehensive threat models using STRIDE methodology with asset identification, threat enumeration, mitigation strategies, and residual risk assessment. Use for "threat modeling", "security analysis", "STRIDE", or "risk 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。
Builds phased data and system migrations using feature flags, dual writes, backfills, and validation. Includes rollback plans and risk mitigation. Use for "data migration", "system migration", "database migration", or "platform migration".
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。
Generates complete page layouts and shells for common patterns (dashboard, authentication, settings, CRUD pages) with consistent navigation, layout components, routing structure, and state management placeholders. Use when building "new page", "dashboard layout", "auth pages", or "admin panel structure".
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。
Scans repository structure and generates comprehensive architecture documentation including system overview, entry points, module relationships, data flow diagrams, and "edit here for X" guides. Creates ARCHITECTURE.md for onboarding and navigation. Use when users request "document the codebase", "explain the architecture", "create onboarding docs", or "map the system".
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。
KRACK (CVE-2017-13077..082) and FragAttacks (CVE-2020-24586..588 + 26139-26147) — key reinstallation, fragmentation, and aggregation attacks against WPA2 supplicants. Covers Vanhoef's test scripts, viability against modern patched stacks (mostly mitigated post-2021), residual unpatched embedded devices and IoT vendors, and the practical limitations of these attacks in modern engagements. Use when assessing legacy supplicants, embedded clients, or vendors with poor patch cadence.
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。
Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 5-stage recon pipeline (seed discovery, asset expansion, enrichment, exposure analysis, reporting), asset-graph discipline with 29 asset types, severity rubric (CRITICAL/HIGH/MEDIUM/LOW/INFO), confidence upgrade workflows, time budgeting, asset-level triage rules, scale-based tactics, identity-fabric mapping (Entra/Okta/ADFS/Google/SAML/M365 Teams+SharePoint+OAuth), API and auth-map methodology, JavaScript deep analysis, mobile attack surface, cloud attack surface, breach×identity correlation, detectability tagging, detection-aware probing (back-off, persona rotation), read-only validator discipline, WAF/CDN bypass + origin discovery, vulnerability prioritization (CVE/EPSS/KEV), phishing infrastructure planning + pretext development, bug bounty submission templates, client deliverable templates with risk translation, threat-actor investigation (incl. RU/CN pivots), cryptocurrency tracing,...
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。
External SSL VPN / remote-access appliance attack matrix — Cisco ASA/AnyConnect, Fortinet FortiGate/FortiOS, Citrix NetScaler/ADC, Palo Alto GlobalProtect, Pulse Secure / Ivanti Connect Secure, SonicWall, F5 Big-IP. Covers version fingerprinting, CVE matrix (2018-2026), AAA backend identification, default credentials, configuration-disclosure paths, pre-auth RCE/SSRF/path-traversal exploits where applicable. Built from authorized-engagement Cisco ASA testing plus 2024-2026 enterprise VPN CVE landscape. Use whenever the target's perimeter exposes any SSL VPN appliance or remote-access gateway — these are the most common initial-access points in 2024-2026 actor TTPs.
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。
Perform complex, long-running research tasks using Gemini Deep Research Agent. Use when asked to research topics requiring multi-source synthesis, competitive analysis, market research, or comprehensive technical investigations that benefit from systematic web search and analysis.
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。
Clawdbot documentation expert with decision tree navigation, search scripts, doc fetching, version tracking, and config snippets for all Clawdbot features
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。
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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。
Navigate and understand codebases using agentlens hierarchical documentation. Use when exploring new projects, finding modules, locating symbols in large files, finding TODOs/warnings, or understanding code structure.
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。
A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
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。