「files」の検索結果

Apply this site's visual identity — colours, type, logo files and fixed brand strings. Use for any page, component, social asset or document that should look like the 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。

Review source files changed during a phase for bugs, security issues, and code quality problems

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。

Query, inspect, or refresh codebase intelligence files in .planning/intel/

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。

Identifies and exploits insecure local data storage vulnerabilities in Android and iOS mobile applications including unencrypted databases, world-readable files, insecure SharedPreferences, plaintext credential storage, and improper keychain/keystore usage. Use when performing mobile penetration testing focused on OWASP M9 (Insecure Data Storage) or assessing compliance with MASVS-STORAGE requirements. Activates for requests involving mobile data storage security, local storage exploitation, SharedPreferences analysis, or mobile data leakage 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。

Detects rootkit presence on compromised systems by identifying hidden processes, hooked system calls, modified kernel structures, hidden files, and covert network connections using memory forensics, cross-view detection, and integrity checking techniques. Activates for requests involving rootkit detection, hidden process discovery, kernel integrity checking, or system call hook 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。

Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.

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。

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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。

Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.

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。

Detects fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Use when building detections for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware. Activates for requests involving fileless malware detection, in-memory attacks, PowerShell exploitation, or living-off-the-land techniques.

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。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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。

Detecting exposed AWS credentials in source code repositories, CI/CD pipelines, and configuration files using TruffleHog, git-secrets, and AWS-native detection mechanisms to prevent credential theft and unauthorized account access.

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 comprehensive threat actor profiles using open-source intelligence (OSINT) techniques to document adversary motivations, capabilities, infrastructure, and TTPs for proactive defense.

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 an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert 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。

Parse Windows PowerShell Script Block Logs (Event ID 4104) from EVTX files to detect obfuscated commands, encoded payloads, and living-off-the-land techniques. Uses python-evtx to extract and reconstruct multi-block scripts, applies entropy analysis and pattern matching for Base64-encoded commands, Invoke-Expression abuse, download cradles, and AMSI bypass attempts.

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。

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.

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。

Analyze Microsoft Outlook PST and OST files for email forensic evidence including message content, headers, attachments, deleted items, and metadata using libpff, pst-utils, and forensic email analysis tools for legal investigations and incident response.

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 comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.

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.

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 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。

Create or edit the Universal Editor component configuration (component-definition.json, component-models.json, component-filters.json) for AEM Edge Delivery Services blocks. Use this skill whenever the user mentions component models, component definitions, component filters, block configuration for the Universal Editor, UE block setup, adding a new block to UE, configuring block properties, block authoring fields, or any task involving the three JSON config files that control how blocks appear in the Universal Editor. Also trigger when the user wants to create a new EDS/Franklin block with UE support, modify block fields, add a block to the section filter, or asks about how blocks connect to the Universal Editor.

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 professional PDF whitepapers from Markdown files. Use when the user wants to create a whitepaper, technical document, or PDF with professional formatting and typography.

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。