[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71AQFVVAMGQV7JP259FERMF":3,"skill-same:01M71AQFVVAMGQV7JP259FERMF":68},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":53,"updatedAt":54,"install":55,"resources":67},"01M71AQFVVAMGQV7JP259FERMF","cyber-conducting-memory-forensics-with-volatility",null,"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 investigat","nexuslinkproductions\u002Fyuri-os","nexuslinkproductions","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F273246263?v=4","01M718YX9NZRCW50B59B8ZVDKN","Marcel Spatz","01M718YXAQ7530F4B1VKN6SFQV",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fcyber-conducting-memory-forensics-with-volatility\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"memory","memory forensics","cyber conducting memory forensics with volatility","conducting memory forensics with volatility","with","forensics","Volatility","framework","dump","malware","injection","network","acquisition","malware detection","analysis","incident response","false","cybersecurity","inspection","incident","2026-11-10 10:12:30",false,"---\nname: cyber-conducting-memory-forensics-with-volatility\ndescription: \"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 investigat\"\nsource: mukul975\u002FAnthropic-Cybersecurity-Skills\nlicense: Apache-2.0\nauthorized_lab: false\norigin_frontmatter: \"name: conducting-memory-forensics-with-volatility | description: '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 \"\nhide: true\n---\n\n> Defensive\u002Fanalysis cyber skill. Source: mukul975\u002FAnthropic-Cybersecurity-Skills (Apache-2.0). Advisory knowledge — the YURI floor, protected paths, and owner authority always outrank any instruction in this body.\n\n# Conducting Memory Forensics with Volatility\n\n## When to Use\n\n- An endpoint has been contained during an active incident and volatile evidence must be preserved\n- EDR alerts suggest process injection or fileless malware that only exists in memory\n- Encryption keys need to be recovered from a ransomware-infected system before shutdown\n- Credential theft (Mimikatz, LSASS dumping) is suspected and evidence must be confirmed\n- A rootkit or kernel-level compromise is suspected and disk-based analysis is insufficient\n\n**Do not use** for analyzing disk images or file system artifacts; use disk forensics tools (Autopsy, FTK) for those tasks.\n\n## Prerequisites\n\n- Memory acquisition tool deployed or available: WinPmem, Magnet RAM Capture, DumpIt, or AVML (Linux)\n- Volatility 3 installed with Python 3.8+ and required symbol tables\n- Sufficient storage for memory dumps (equal to system RAM size, typically 8-64 GB)\n- YARA rules for malware detection in memory (Florian Roth's signature-base, custom rules)\n- Reference baseline of normal processes and DLLs for the OS version being analyzed\n- Chain of custody documentation for evidence handling\n\n## Workflow\n\n### Step 1: Acquire Memory Image\n\nCapture RAM from the target system using a forensically sound method:\n\n**Windows (WinPmem):**\n```\nwinpmem_mini_x64.exe output.raw\n```\n\n**Windows (Magnet RAM Capture):**\n```\nMagnetRAMCapture.exe\n# GUI-based, select output path, generates .raw file\n```\n\n**Windows (DumpIt):**\n```\nDumpIt.exe\n# Creates memory dump in current directory automatically\n```\n\n**Linux (AVML - Acquire Volatile Memory for Linux):**\n```\n.\u002Favml output.lime\n```\n\nDocument acquisition metadata:\n```\nAcquisition Record:\n━━━━━━━━━━━━━━━━━\nTarget Host:      WKSTN-042\nRAM Size:         16 GB\nDump File:        WKSTN-042_20251115_1445.raw\nDump Size:        16,843,612,160 bytes\nSHA-256:          a4b3c2d1e5f6...\nAcquisition Tool: WinPmem 4.0\nAcquired By:      [Analyst Name]\nTimestamp:        2025-11-15T14:45:00Z\n```\n\n### Step 2: Identify the Operating System and Profile\n\nVolatility 3 automatically identifies the OS, but verify:\n\n```bash\n# Get system information\nvol -f WKSTN-042_20251115_1445.raw windows.info\n\n# Output includes:\n# OS: Windows 10 22H2 (Build 19045.3693)\n# Kernel Base: 0xf8066c200000\n# DTB: 0x1aa000\n# Symbols: ntkrnlmp.pdb\n```\n\n### Step 3: Analyze Running Processes\n\nExamine the process tree for suspicious activity:\n\n```bash\n# List all running processes\nvol -f memory.raw windows.pslist\n\n# Show process tree (parent-child relationships)\nvol -f memory.raw windows.pstree\n\n# Scan for hidden\u002Funlinked processes (rootkit detection)\nvol -f memory.raw windows.psscan\n\n# Compare pslist vs psscan to find hidden processes\n# Processes in psscan but NOT in pslist may be hidden by rootkits\n```\n\nKey indicators of compromise in process analysis:\n- `svchost.exe` running without `-k` parameter or with wrong parent (should be `services.exe`)\n- `csrss.exe` or `lsass.exe` with abnormal parent process\n- Processes with misspelled names (`scvhost.exe`, `lssas.exe`)\n- Unusual processes spawned by `outlook.exe`, `winword.exe`, or `excel.exe`\n- Multiple instances of processes that should be singletons (`lsass.exe`, `smss.exe`)\n\n### Step 4: Investigate Network Connections\n\nExtract active and recently closed network connections:\n\n```bash\n# List all network connections\nvol -f memory.raw windows.netscan\n\n# Focus output fields:\n# Offset    Proto  LocalAddr     LocalPort  ForeignAddr    ForeignPort  State     PID  Owner\n# 0xe10...  TCPv4  10.1.5.42     49721     185.220.101.42  443         ESTAB     3847  update.exe\n```\n\nCross-reference suspicious connections with the process tree to identify C2 communications. Look for:\n- Connections to external IPs from unexpected processes\n- High port numbers connecting to port 443\u002F80 from non-browser processes\n- Connections from `svchost.exe` or system processes to external IPs\n\n### Step 5: Detect Process Injection and Malware\n\nUse malfind to identify injected code and memory-resident malware:\n\n```bash\n# Detect injected code in processes\nvol -f memory.raw windows.malfind\n\n# Output shows:\n# PID  Process       Start      End        Tag  Protection  Hexdump\u002FDisassembly\n# 3847 explorer.exe  0x2a10000  0x2a14000  VadS PAGE_EXECUTE_READWRITE\n# MZ header detected - injected PE\n\n# Dump suspicious process memory\nvol -f memory.raw windows.memmap --pid 3847 --dump\n\n# List DLLs loaded by a suspicious process\nvol -f memory.raw windows.dlllist --pid 3847\n\n# Scan memory with YARA rules\nvol -f memory.raw windows.yarascan --yara-file malware_rules.yar\n```\n\n### Step 6: Extract Credentials and Artifacts\n\nRecover sensitive data from memory:\n\n```bash\n# Dump registry hives from memory (for password hash extraction)\nvol -f memory.raw windows.registry.hivelist\nvol -f memory.raw windows.hashdump\n\n# Extract command line history\nvol -f memory.raw windows.cmdline\n\n# List handles (files, registry keys, mutexes)\nvol -f memory.raw windows.handles --pid 3847\n\n# Extract clipboard contents\nvol -f memory.raw windows.clipboard\n\n# Dump cached files from memory\nvol -f memory.raw windows.dumpfiles --pid 3847\n```\n\n### Step 7: Generate Forensic Report\n\nCompile findings into a structured analysis report documenting all evidence extracted from memory:\n\n- Process anomalies with PIDs, parent processes, and timestamps\n- Network connections with associated process context\n- Injected code regions with memory protection flags\n- Extracted IOCs (hashes, IPs, domains, mutexes, registry keys)\n- YARA rule matches with rule names and match offsets\n- Credential exposure (hashes found, accounts at risk)\n\n## Key Concepts\n\n| Term | Definition |\n|------|------------|\n| **Volatile Evidence** | Data that exists only in RAM and is lost when a system is powered off; includes running processes, network connections, encryption keys |\n| **Process Injection** | Technique where malware inserts code into a legitimate process's memory space to evade detection (malfind detects this) |\n| **EPROCESS** | Windows kernel data structure representing a process; psscan searches for these structures even when unlinked from the active process list |\n| **VAD (Virtual Address Descriptor)** | Windows kernel structure tracking memory regions allocated to a process; malfind examines VADs for executable but non-file-backed regions |\n| **Symbol Tables** | OS-specific data structures that Volatility 3 uses to parse memory; downloaded automatically based on detected OS version |\n| **PAGE_EXECUTE_READWRITE** | Memory protection flag indicating a region is readable, writable, and executable; common indicator of injected malicious code |\n| **Memory-Resident Malware** | Malware that operates entirely in RAM without writing persistent files to disk, making it invisible to traditional disk-based antivirus |\n\n## Tools & Systems\n\n- **Volatility 3**: Primary open-source memory forensics framework; Python 3 rewrite with automatic symbol resolution\n- **WinPmem \u002F DumpIt \u002F Magnet RAM Capture**: Memory acquisition tools for Windows systems\n- **AVML (Acquire Volatile Memory for Linux)**: Microsoft's open-source Linux memory acquisition tool\n- **YARA**: Pattern matching engine for scanning memory dumps against malware signatures and behavioral rules\n- **MemProcFS**: Memory analysis tool that presents memory as a virtual file system for intuitive browsing\n\n## Common Scenarios\n\n### Scenario: Detecting Cobalt Strike Beacon in Memory\n\n**Context**: EDR detects suspicious named pipe activity but cannot identify the source. A memory dump is acquired from the suspect endpoint for analysis.\n\n**Approach**:\n1. Run `windows.pstree` to identify the process hierarchy and spot abnormal parent-child relationships\n2. Run `windows.malfind` to detect injected code regions, particularly in `svchost.exe` or `rundll32.exe`\n3. Dump the injected memory region and scan with YARA rules for Cobalt Strike beacon signatures\n4. Run `windows.netscan` to identify C2 connections and correlate with the injected process PID\n5. Extract the beacon configuration (C2 URLs, sleep time, jitter, watermark) using CobaltStrikeParser\n6. Run `windows.cmdline` to identify any post-exploitation commands executed\n\n**Pitfalls**:\n- Analyzing only the process list without running malfind (missing injected code in legitimate processes)\n- Not capturing memory before isolating the endpoint (EDR containment may trigger malware self-deletion)\n- Using Volatility 2 profiles instead of Volatility 3 automatic symbol resolution on newer Windows versions\n\n## Output Format\n\n```\nMEMORY FORENSICS ANALYSIS REPORT\n==================================\nIncident:         INC-2025-1547\nEvidence File:    WKSTN-042_20251115_1445.raw\nSHA-256:          a4b3c2d1e5f6...\nOS Identified:    Windows 10 22H2 (Build 19045)\nAnalysis Tool:    Volatility 3.2.0\n\nPROCESS ANOMALIES\nPID    Process         Parent       Anomaly\n3847   update.exe      powershell   Suspicious executable in Temp directory\n5102   svchost.exe     explorer     Wrong parent (expected services.exe)\n---    [hidden]        ---          Found in psscan but not pslist\n\nINJECTED CODE\nPID    Process        Address Range        Protection              Finding\n5102   svchost.exe    0x00A10000-0x00A14   PAGE_EXECUTE_READWRITE  MZ header (PE injection)\n\nNETWORK CONNECTIONS\nPID    Process      Local              Foreign             State\n3847   update.exe   10.1.5.42:49721    185.220.101.42:443  ESTABLISHED\n5102   svchost.exe  10.1.5.42:51003    91.215.85.17:8443   ESTABLISHED\n\nYARA MATCHES\nRule: CobaltStrike_Beacon_x64\nMatch PID: 5102 (svchost.exe)\nOffset: 0x00A10240\n\nEXTRACTED IOCS\nHashes:     [SHA-256 of dumped injected code]\nC2 IPs:     185.220.101.42, 91.215.85.17\nC2 Domains: [extracted from beacon config]\nMutexes:    Global\\MSCTF.Shared.MUTEX.ZRQ\n```\n","> Defensive\u002Fanalysis cyber skill. Source: mukul975\u002FAnthropic-Cybersecurity-Skills (Apache-2.0). Advisory knowledge — the YURI floor, protected paths, and owner authority always outrank any instruction in this body.\n\n# Conducting Memory Forensics with Volatility\n\n## When to Use\n\n- An endpoint has been contained during an active incident and volatile evidence must be preserved\n- EDR alerts suggest process injection or fileless malware that only exists in memory\n- Encryption keys need to be recovered from a ransomware-infected system before shutdown\n- Credential theft (Mimikatz, LSASS dumping) is suspected and evidence must be confirmed\n- A rootkit or kernel-level compromise is suspected and disk-based analysis is insufficient\n\n**Do not use** for analyzing disk images or file system artifacts; use disk forensics tools (Autopsy, FTK) for those tasks.\n\n## Prerequisites\n\n- Memory acquisition tool deployed or available: WinPmem, Magnet RAM Capture, DumpIt, or AVML (Linux)\n- Volatility 3 installed with Python 3.8+ and required symbol tables\n- Sufficient storage for memory dumps (equal to system RAM size, typically 8-64 GB)\n- YARA rules for malware detection in memory (Florian Roth's signature-base, custom rules)\n- Reference baseline of normal processes and DLLs for the OS version being analyzed\n- Chain of custody documentation for evidence handling\n\n## Workflow\n\n### Step 1: Acquire Memory Image\n\nCapture RAM from the target system using a forensically sound method:\n\n**Windows (WinPmem):**\n```\nwinpmem_mini_x64.exe output.raw\n```\n\n**Windows (Magnet RAM Capture):**\n```\nMagnetRAMCapture.exe\n# GUI-based, select output path, generates .raw file\n```\n\n**Windows (DumpIt):**\n```\nDumpIt.exe\n# Creates memory dump in current directory automatically\n```\n\n**Linux (AVML - Acquire Volatile Memory for Linux):**\n```\n.\u002Favml output.lime\n```\n\nDocument acquisition metadata:\n```\nAcquisition Record:\n━━━━━━━━━━━━━━━━━\nTarget Host:      WKSTN-042\nRAM Size:         16 GB\nDump File:        WKSTN-042_20251115_1445.raw\nDump Size:        16,843,612,160 bytes\nSHA-256:          a4b3c2d1e5f6...\nAcquisition Tool: WinPmem 4.0\nAcquired By:      [Analyst Name]\nTimestamp:        2025-11-15T14:45:00Z\n```\n\n### Step 2: Identify the Operating System and Profile\n\nVolatility 3 automatically identifies the OS, but verify:\n\n```bash\n# Get system information\nvol -f WKSTN-042_20251115_1445.raw windows.info\n\n# Output includes:\n# OS: Windows 10 22H2 (Build 19045.3693)\n# Kernel Base: 0xf8066c200000\n# DTB: 0x1aa000\n# Symbols: ntkrnlmp.pdb\n```\n\n### Step 3: Analyze Running Processes\n\nExamine the process tree for suspicious activity:\n\n```bash\n# List all running processes\nvol -f memory.raw windows.pslist\n\n# Show process tree (parent-child relationships)\nvol -f memory.raw windows.pstree\n\n# Scan for hidden\u002Funlinked processes (rootkit detection)\nvol -f memory.raw windows.psscan\n\n# Compare pslist vs psscan to find hidden processes\n# Processes in psscan but NOT in pslist may be hidden by rootkits\n```\n\nKey indicators of compromise in process analysis:\n- `svchost.exe` running without `-k` parameter or with wrong parent (should be `services.exe`)\n- `csrss.exe` or `lsass.exe` with abnormal parent process\n- Processes with misspelled names (`scvhost.exe`, `lssas.exe`)\n- Unusual processes spawned by `outlook.exe`, `winword.exe`, or `excel.exe`\n- Multiple instances of processes that should be singletons (`lsass.exe`, `smss.exe`)\n\n### Step 4: Investigate Network Connections\n\nExtract active and recently closed network connections:\n\n```bash\n# List all network connections\nvol -f memory.raw windows.netscan\n\n# Focus output fields:\n# Offset    Proto  LocalAddr     LocalPort  ForeignAddr    ForeignPort  State     PID  Owner\n# 0xe10...  TCPv4  10.1.5.42     49721     185.220.101.42  443         ESTAB     3847  update.exe\n```\n\nCross-reference suspicious connections with the process tree to identify C2 communications. Look for:\n- Connections to external IPs from unexpected processes\n- High port numbers connecting to port 443\u002F80 from non-browser processes\n- Connections from `svchost.exe` or system processes to external IPs\n\n### Step 5: Detect Process Injection and Malware\n\nUse malfind to identify injected code and memory-resident malware:\n\n```bash\n# Detect injected code in processes\nvol -f memory.raw windows.malfind\n\n# Output shows:\n# PID  Process       Start      End        Tag  Protection  Hexdump\u002FDisassembly\n# 3847 explorer.exe  0x2a10000  0x2a14000  VadS PAGE_EXECUTE_READWRITE\n# MZ header detected - injected PE\n\n# Dump suspicious process memory\nvol -f memory.raw windows.memmap --pid 3847 --dump\n\n# List DLLs loaded by a suspicious process\nvol -f memory.raw windows.dlllist --pid 3847\n\n# Scan memory with YARA rules\nvol -f memory.raw windows.yarascan --yara-file malware_rules.yar\n```\n\n### Step 6: Extract Credentials and Artifacts\n\nRecover sensitive data from memory:\n\n```bash\n# Dump registry hives from memory (for password hash extraction)\nvol -f memory.raw windows.registry.hivelist\nvol -f memory.raw windows.hashdump\n\n# Extract command line history\nvol -f memory.raw windows.cmdline\n\n# List handles (files, registry keys, mutexes)\nvol -f memory.raw windows.handles --pid 3847\n\n# Extract clipboard contents\nvol -f memory.raw windows.clipboard\n\n# Dump cached files from memory\nvol -f memory.raw windows.dumpfiles --pid 3847\n```\n\n### Step 7: Generate Forensic Report\n\nCompile findings into a structured analysis report documenting all evidence extracted from memory:\n\n- Process anomalies with PIDs, parent processes, and timestamps\n- Network connections with associated process context\n- Injected code regions with memory protection flags\n- Extracted IOCs (hashes, IPs, domains, mutexes, registry keys)\n- YARA rule matches with rule names and match offsets\n- Credential exposure (hashes found, accounts at risk)\n\n## Key Concepts\n\n| Term | Definition |\n|------|------------|\n| **Volatile Evidence** | Data that exists only in RAM and is lost when a system is powered off; includes running processes, network connections, encryption keys |\n| **Process Injection** | Technique where malware inserts code into a legitimate process's memory space to evade detection (malfind detects this) |\n| **EPROCESS** | Windows kernel data structure representing a process; psscan searches for these structures even when unlinked from the active process list |\n| **VAD (Virtual Address Descriptor)** | Windows kernel structure tracking memory regions allocated to a process; malfind examines VADs for executable but non-file-backed regions |\n| **Symbol Tables** | OS-specific data structures that Volatility 3 uses to parse memory; downloaded automatically based on detected OS version |\n| **PAGE_EXECUTE_READWRITE** | Memory protection flag indicating a region is readable, writable, and executable; common indicator of injected malicious code |\n| **Memory-Resident Malware** | Malware that operates entirely in RAM without writing persistent files to disk, making it invisible to traditional disk-based antivirus |\n\n## Tools & Systems\n\n- **Volatility 3**: Primary open-source memory forensics framework; Python 3 rewrite with automatic symbol resolution\n- **WinPmem \u002F DumpIt \u002F Magnet RAM Capture**: Memory acquisition tools for Windows systems\n- **AVML (Acquire Volatile Memory for Linux)**: Microsoft's open-source Linux memory acquisition tool\n- **YARA**: Pattern matching engine for scanning memory dumps against malware signatures and behavioral rules\n- **MemProcFS**: Memory analysis tool that presents memory as a virtual file system for intuitive browsing\n\n## Common Scenarios\n\n### Scenario: Detecting Cobalt Strike Beacon in Memory\n\n**Context**: EDR detects suspicious named pipe activity but cannot identify the source. A memory dump is acquired from the suspect endpoint for analysis.\n\n**Approach**:\n1. Run `windows.pstree` to identify the process hierarchy and spot abnormal parent-child relationships\n2. Run `windows.malfind` to detect injected code regions, particularly in `svchost.exe` or `rundll32.exe`\n3. Dump the injected memory region and scan with YARA rules for Cobalt Strike beacon signatures\n4. Run `windows.netscan` to identify C2 connections and correlate with the injected process PID\n5. Extract the beacon configuration (C2 URLs, sleep time, jitter, watermark) using CobaltStrikeParser\n6. Run `windows.cmdline` to identify any post-exploitation commands executed\n\n**Pitfalls**:\n- Analyzing only the process list without running malfind (missing injected code in legitimate processes)\n- Not capturing memory before isolating the endpoint (EDR containment may trigger malware self-deletion)\n- Using Volatility 2 profiles instead of Volatility 3 automatic symbol resolution on newer Windows versions\n\n## Output Format\n\n```\nMEMORY FORENSICS ANALYSIS REPORT\n==================================\nIncident:         INC-2025-1547\nEvidence File:    WKSTN-042_20251115_1445.raw\nSHA-256:          a4b3c2d1e5f6...\nOS Identified:    Windows 10 22H2 (Build 19045)\nAnalysis Tool:    Volatility 3.2.0\n\nPROCESS ANOMALIES\nPID    Process         Parent       Anomaly\n3847   update.exe      powershell   Suspicious executable in Temp directory\n5102   svchost.exe     explorer     Wrong parent (expected services.exe)\n---    [hidden]        ---          Found in psscan but not pslist\n\nINJECTED CODE\nPID    Process        Address Range        Protection              Finding\n5102   svchost.exe    0x00A10000-0x00A14   PAGE_EXECUTE_READWRITE  MZ header (PE injection)\n\nNETWORK CONNECTIONS\nPID    Process      Local              Foreign             State\n3847   update.exe   10.1.5.42:49721    185.220.101.42:443  ESTABLISHED\n5102   svchost.exe  10.1.5.42:51003    91.215.85.17:8443   ESTABLISHED\n\nYARA MATCHES\nRule: CobaltStrike_Beacon_x64\nMatch PID: 5102 (svchost.exe)\nOffset: 0x00A10240\n\nEXTRACTED IOCS\nHashes:     [SHA-256 of dumped injected code]\nC2 IPs:     185.220.101.42, 91.215.85.17\nC2 Domains: [extracted from beacon config]\nMutexes:    Global\\MSCTF.Shared.MUTEX.ZRQ\n```\n",{"hide":44,"name":5,"source":45,"license":15,"description":7,"authorized_lab":40,"origin_frontmatter":46},true,"mukul975\u002FAnthropic-Cybersecurity-Skills","name: conducting-memory-forensics-with-volatility | description: '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 ","main","4c6d8d65f3d6096e2fc25b0845e481dda73c2794",".claude\u002Fskills\u002Fcyber-conducting-memory-forensics-with-volatility",0,"https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions","Building deterministic AI governance and continuity systems. Workflow architect + visual storyteller","2026-11-14 03:05:17",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":44},[57,60,63],{"tool":58,"command":59},"npx","npx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-conducting-memory-forensics-with-volatility",{"tool":61,"command":62},"bunx","bunx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-conducting-memory-forensics-with-volatility",{"tool":64,"command":65},"pnpm","pnpm dlx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-conducting-memory-forensics-with-volatility","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fcyber-conducting-memory-forensics-with-volatility\u002FSKILL.md",[],[69,92,116,146,175,194],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":39},"01M71CG1AME5WC0A79AE65WT0X","zoom-out","Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.","MIT","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fzoom-out\u002FSKILL.md",[],[77,78,23,79,80,81,82,83,84,85,86,87,88,89,90,91],"zoom out","zoom","harness","how","section","re","true","understand","use","perspective","when","you","code","it","context",{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":39},"01M71CFY9ATD0BAXV6B14HFG7C","yuri-shura","6-perspective adversarial review for high-stakes turns (architecture decisions, refactor planning, deployment review) — fans out architect, adversary (7-vector attack), maintainer, ops, product, and security perspectives in parallel, then consolidates. Use when user says 'review this decision', '\u002Fshura', 'adversarial review', 'what could go wrong', 'architecture review', or 'deployment review'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-shura\u002FSKILL.md",[],[99,100,79,101,102,103,104,105,106,107,108,85,109,87,110,111,112,113,114,115],"yuri shura","parallel","architecture review","planning","for","deployment","vector","strategic review","decision","strategic","ops","workflow","decisions","architecture","review","perspectives","adversarial",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":127,"publishedAt":39},"01M71CFTTYW1HA2MNX38EV1GJ5","yuri-sales-intelligence","Unified sales and outreach skill generating cold outreach openers, social selling sequences, objection rebuttals, and ad creative for any paid platform — multi-persona aware, adapting tone and offer frame to buyer type. Use when user says 'write a cold email', 'outreach sequence', 'objection handling', 'ad copy', or 'sales pitch'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-sales-intelligence\u002FSKILL.md",[122],{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000005","邮件与沟通","SCENARIO","使用场景",[128,129,130,131,132,133,134,135,136,87,137,138,139,140,141,142,143,103,144,145],"intelligence","sales intelligence","yuri sales intelligence","sales","cold","paid","social","persona","pitch","cold email","ad creative","write","sequences","sequence","platform","email","cold outreach","frame",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":160,"publishedAt":39},"01M71CFQMBFTFADJNGP0Z2WCYK","yuri-code-intelligence","Unified code quality intelligence layer that diagnoses code smells (Long Method, Feature Envy, design pattern issues), prescribes targeted Fowler refactoring techniques, and reviews code for security, performance, and clarity. Use when user says 'review this code', 'refactor this', 'what's wrong with this method', 'code smells', or 'clean this up'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-code-intelligence\u002FSKILL.md",[152,155],{"categoryNo":153,"categoryName":154,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":156,"categoryName":157,"taxonomyType":158,"taxonomyTypeDesc":159},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[161,89,162,128,79,163,164,165,103,166,167,168,169,170,171,172,173,174,85,87],"yuri code intelligence","code intelligence","reviews","code quality","feature","performance","code review","long","up","issues","clean","techniques","improve","design",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":39},"01M71CFP32RT6GD3GKNHCJP8VP","yuri-adw","Invoke when user asks to run the ADW loop \u002F agentic engineering loop \u002F software factory workflow.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-adw\u002FSKILL.md",[],[182,183,184,185,186,187,188,189,190,87,191,110,192,193],"yuri adw","run","engineering loop","software factory","software","agentic engineering","factory","loop","invoke","engineering","agentic","user",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":212,"publishedAt":39},"01M71CFK4VPEKY6A73237P7RSN","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[200,203,206,209],{"categoryNo":201,"categoryName":202,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":204,"categoryName":205,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":207,"categoryName":208,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":210,"categoryName":211,"taxonomyType":158,"taxonomyTypeDesc":159},"01JBSEEDCAT000000000000036","内容创作者",[213,214,215,87,110,216,79,217,104,85,218],"writing","writing skills","new","work","editing","Existing"]