1.1만건 · 관련도순
Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.
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Build collaborative forensic incident timelines using Timesketch to ingest, normalize, and analyze multi-source event data for attack chain reconstruction and investigation documentation.
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Detect PowerShell Empire framework artifacts in Windows event logs by identifying Base64 encoded launcher patterns, default user agents, staging URL structures, stager IOCs, and known Empire module signatures in Script Block Logging events.
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Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache investigation.
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Analyze Windows Shellbag registry artifacts to reconstruct folder browsing activity, detect access to removable media and network shares, and establish user interaction with directories even after deletion using SBECmd and ShellBags Explorer.
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Extract and catalog attack patterns from cyber threat intelligence reports into a structured STIX-based library mapped to MITRE ATT&CK for detection engineering and threat-informed defense.
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Uses the Linux Audit framework (auditd) with ausearch and aureport utilities to detect intrusion attempts, unauthorized access, privilege escalation, and suspicious system activity. Covers audit rule configuration, log querying, timeline reconstruction, and integration with SIEM platforms. Activates for requests involving auditd analysis, Linux audit log investigation, ausearch queries, aureport summaries, or host-based intrusion detection on Linux.
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Extract and analyze Cobalt Strike beacon configuration from PE files and memory dumps to identify C2 infrastructure, malleable profiles, and operator tradecraft.
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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.
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Reverse engineer Go-compiled malware using Ghidra with specialized scripts for function recovery, string extraction, and type reconstruction in stripped Go binaries.
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Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (check_syscall, lsmod, hidden_modules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.
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Create or update product-update / release-notes content for a given OpenMetadata/Collate patch version across getcollate, openmetadata-site, and docs-om. Sources getcollate's changelog from openmetadata-collate + OpenMetadata + ai-platform; openmetadata-site's from OpenMetadata only; docs-om reuses openmetadata-site's already-written content, reformatted into its Mintlify snippet structure. Filters to customer-facing changes only, runs each target's own doc-review checklist, verifies every link live, and requires explicit approval before any commit.
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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.
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Generate comprehensive documentation for content authors taking over an AEM Edge Delivery Services project. Use when onboarding content authors, training content managers, or creating author-focused handover documentation — analyzes project structure and produces a complete authoring guide with blocks, templates, configurations, and publishing workflows.
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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.
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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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Optimize AEM Edge Delivery Services commerce product pages for search engine crawling and indexing. Audits client-side rendered product content for crawlability, validates meta tags, Product schema.org structured data, canonical URLs, and image optimization. Addresses the core challenge that EDS product pages render catalog data via JavaScript, which search crawlers may not fully execute. Use when product pages are not appearing in search results or have poor search visibility.
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Rewrite AEM Edge Delivery Services page content for AI search discoverability (GEO — Generative Engine Optimization). Analyzes content structure, semantic clarity, and factual density, then produces optimized rewrites that rank in both traditional search and AI-powered search engines. Use when optimizing pages for search visibility, AI discoverability, or content clarity.
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Orchestrate the migrate-prep cascade — extract --prep → direct --prep → prototype --prep → assets prep — with confirmation gates between phases. Builds the data structure migrate consumes.
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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.
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Generate comprehensive technical documentation for developers taking over an AEM Edge Delivery Services project. Use when onboarding developers, creating technical handover documentation, or documenting a project's architecture — analyzes codebase structure, custom implementations, design tokens, and produces a complete developer guide.
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Analyzes development requirements and generates structured acceptance criteria for AEM Edge Delivery Services (EDS) tasks. Use when the user needs to define acceptance criteria, write requirements, scope work, or create a definition of done for EDS blocks, components, variants, bug fixes, or styling changes. Produces task breakdowns, identifies edge cases, and documents analysis for new blocks, variants, behavior modifications, CSS-only changes, and bug fixes.
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Create, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
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