1.6万 件 ・ 関連度順
概要と使いどころ
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。
MITRE ATT&CK is a globally-accessible knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations. This skill covers systematically mapping threat actor beh
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 structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution. Activates for requests involving ThreatConnect, Recorded Future, Mandiant Advantage, MISP, AlienVault OTX, or automated feed aggregation pipelines.
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 the NTFS Master File Table ($MFT) to recover metadata and content of deleted files by examining MFT record entries, $LogFile, $UsnJrnl, and MFT slack space using MFTECmd, analyzeMFT, and X-Ways Forensics.
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。
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
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Identifies and unpacks UPX-packed and other packed malware samples to expose the original executable code for static analysis. Covers both standard UPX unpacking and handling modified UPX headers that prevent automated decompression. Activates for requests involving malware unpacking, UPX decompression, packer removal, or preparing packed samples for 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。
Extract and analyze Cobalt Strike beacon configuration from PE files and memory dumps to identify C2 infrastructure, malleable profiles, and operator tradecraft.
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。
Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr
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。
Full review of a PR, a file, or pasted text against the OpenMetadata Writing Style Guide checklist, plus checking every checkable claim for internal consistency against the diff/PR itself. One combined report, not separate passes. Does not fetch or verify against any external source repository. Link-checking is out of scope; a separate broken-links job covers that. Only runs on explicit /content-review invocation, never self-triggered.
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。
Review and fix OpenMetadata connector documentation against JSON schema and source code. Validates availableFeatures, permissions, yaml.mdx configuration, and overall completeness. Automatically fixes gaps.
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。
Start AEM Workflows on AEM as a Cloud Service using all available triggering mechanisms. Use when starting workflows manually via the Timeline UI, programmatically via WorkflowSession.startWorkflow(), via the HTTP Workflow API, through Manage Publication, or passing initial metadata and payload to a workflow instance.
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Orchestrate complete lifecycle work for the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration in AEM as a Cloud Service, from design and implementation through validation, release readiness, and incident troubleshooting.
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。
Design and create AEM Workflow models on AEM as a Cloud Service. Use when creating workflow models via the Workflow Model Editor or content package XML, defining step types (PROCESS, PARTICIPANT, DYNAMIC_PARTICIPANT, OR_SPLIT, AND_SPLIT), configuring step properties, declaring workflow variables, and deploying models through the Cloud Manager pipeline.
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 replication / content distribution. Covers migration from CQ Replicator (com.day.cq.replication.Replicator) and Sling Replication Agent (org.apache.sling.replication.agent.api) to the Sling Distribution API (Distributor + SimpleDistributionRequest). Includes agent selection (publish vs preview), async response handling, author cluster coordination, service-user setup, review checklist, troubleshooting, 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] Implement custom AEM Workflow Java components on AEM as a Cloud Service. This skill is in beta. Verify all outputs before applying them to production projects. Use when writing WorkflowProcess steps, ParticipantStepChooser implementations, registering services via OSGi DS R6 annotations, reading step arguments from MetaDataMap, accessing JCR payload via WorkflowSession adapter, reading and writing workflow metadata and variables, and handling errors with WorkflowException for retry behavior.
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。
Generate structured HTML from authoring analysis for AEM Edge Delivery Services. Creates section structure, applies block tables, handles metadata, and manages images folder.
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 OSGi Event Admin handlers (non-resource events). Covers migration of javax.jcr.observation.EventListener (residual non-resource cases) and OSGi EventHandler with inline business logic to the lightweight EventHandler + JobConsumer split. Includes routing rules (resource events go to resource-change-listener, external notification goes to AEM Eventing), TopologyEventListener for leader-only execution, replication and workflow event patterns, service-user setup, review checklist, troubleshooting, 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。
Preview and verify imported content in local AEM Edge Delivery Services dev server. Validates rendering, compares with original page, and troubleshoots common 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。
Identify section boundaries and content sequences within a scraped webpage for AEM Edge Delivery Services import. Performs two-level analysis (sections, then sequences per section) and surveys available blocks.
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Render a proposed redesign of a page on the current website as a self-contained static HTML file, then iterate via the impeccable craft loop. Per-page, idempotent, stale-aware. Use when the user asks for a redesign prototype, a before/after comparison, a design preview, a page mockup, a visual diff of the redesign, or invokes /stardust:prototype.
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。
Convert per-page styled HTML prototypes (stardust under stardust/prototypes/**, or claude-design / Mobirise / Relume / Lovable / v0 / Figma-derived pages, or JSX prototypes pre-rendered to HTML, often under samples/) into Edge Delivery Services (EDS / AEM) blocks and content pages, then deploy via DA. Each prototype section becomes one EDS block; the prototype's per-section CSS becomes that block's CSS scoped under the block class. Use when the user wants to lift styled per-page HTML prototypes into a working EDS site under blocks/ and content/.
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。
Reconcile a converted/built web page against its source prototype with two complementary probes — a PIXEL/layout diff (stretched images, dropped wraps, blank renders, colour flips) and a STRUCTURAL content+typography diff (dropped/mis-slotted headings, eyebrows, CTAs; rendered-face font forks). Stack-agnostic via profiles (eds | generic). Use after converting a prototype to EDS/AEM (the stardust:deploy Step 10), or for any prototype↔build fidelity check; invocable as stardust:diff and from 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。