[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFRZ26PQV9JFQSPEXJ4Q9P":3,"skill-same:01M6AFRZ26PQV9JFQSPEXJ4Q9P":122},{"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":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":63,"commitSha":64,"skillDirectory":65,"forksCount":66,"repositoryHtmlUrl":67,"repositoryHomepageUrl":68,"ownerHtmlUrl":69,"ownerBlogUrl":70,"ownerDescription":71,"updatedAt":72,"install":73,"resources":86},"01M6AFRZ26PQV9JFQSPEXJ4Q9P","performing-endpoint-forensics-investigation",null,"Performs digital forensics investigation on compromised endpoints including memory acquisition, disk imaging, artifact analysis, and timeline reconstruction. Use when investigating security incidents, collecting evidence for legal proceedings, or analyzing endpoint compromise scope. Activates for requests involving endpoint forensics, memory analysis, disk forensics, or incident investigation.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",36078,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"performing endpoint forensics investigation","investigation","forensics","endpoint","endpoint-security","analysis","t1547","timeline","t1059","t1055","memory-analysis","security","analyzing","Volatility","incident-investigation","disk-imaging","pr","cybersecurity","memory","2026-11-10 10:12:30",false,"---\nname: performing-endpoint-forensics-investigation\ndescription: 'Performs digital forensics investigation on compromised endpoints including\n  memory acquisition, disk imaging, artifact analysis, and timeline reconstruction.\n  Use when investigating security incidents, collecting evidence for legal proceedings,\n  or analyzing endpoint compromise scope. Activates for requests involving endpoint\n  forensics, memory analysis, disk forensics, or incident investigation.\n\n  '\ndomain: cybersecurity\nsubdomain: endpoint-security\ntags:\n- endpoint\n- forensics\n- memory-analysis\n- disk-imaging\n- incident-investigation\n- Volatility\nversion: 1.0.0\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- PR.PS-01\n- PR.PS-02\n- DE.CM-01\n- PR.IR-01\nmitre_attack:\n- T1055\n- T1547\n- T1059\n- T1036\n- T1005\n---\n# Performing Endpoint Forensics Investigation\n\n## When to Use\n\nUse this skill when:\n- Investigating a confirmed or suspected endpoint compromise requiring forensic analysis\n- Collecting volatile and non-volatile evidence for incident response or legal proceedings\n- Analyzing memory dumps for malware, injected code, or credential theft artifacts\n- Reconstructing attacker timelines from endpoint artifacts (prefetch, shimcache, amcache)\n\n**Do not use** this skill for live threat hunting (use EDR\u002FSIEM) or network forensics.\n\n## Prerequisites\n\n- Forensic workstation with analysis tools (Volatility 3, KAPE, Autopsy, Eric Zimmerman tools)\n- Write-blocker for disk imaging (hardware or software)\n- Secure evidence storage with chain-of-custody documentation\n- Memory acquisition tool (WinPMEM, FTK Imager, Magnet RAM Capture)\n- Administrative access to the target endpoint (or physical access)\n\n## Workflow\n\n### Step 1: Evidence Preservation (Order of Volatility)\n\nCollect evidence from most volatile to least volatile:\n```\n1. System memory (RAM) - Most volatile\n2. Network connections and routing tables\n3. Running processes and open files\n4. Disk contents (file system)\n5. Removable media\n6. Logs and backup data - Least volatile\n```\n\n**Memory Acquisition**:\n```powershell\n# WinPMEM (Windows)\nwinpmem_mini_x64.exe memdump.raw\n\n# FTK Imager - Create memory capture via GUI\n# File → Capture Memory → Destination path → Capture Memory\n\n# Linux (LiME kernel module)\nsudo insmod lime.ko \"path=\u002Fevidence\u002Fmemory.lime format=lime\"\n```\n\n**Volatile Data Collection**:\n```powershell\n# Capture running processes\nGet-Process | Export-Csv \"evidence\\processes.csv\" -NoTypeInformation\ntasklist \u002Fv > \"evidence\\tasklist.txt\"\n\n# Capture network connections\nnetstat -anob > \"evidence\\netstat.txt\"\nGet-NetTCPConnection | Export-Csv \"evidence\\tcp_connections.csv\"\n\n# Capture logged-on users\nquery user > \"evidence\\logged_users.txt\"\n\n# Capture scheduled tasks\nschtasks \u002Fquery \u002Ffo CSV \u002Fv > \"evidence\\scheduled_tasks.csv\"\n\n# Capture services\nGet-Service | Export-Csv \"evidence\\services.csv\"\n\n# Capture DNS cache\nipconfig \u002Fdisplaydns > \"evidence\\dns_cache.txt\"\n```\n\n### Step 2: Disk Imaging\n\n```powershell\n# FTK Imager - Create forensic disk image\n# File → Create Disk Image → Physical Drive → E01 format\n# Always verify image hash (MD5\u002FSHA1) matches source\n\n# dd (Linux)\nsudo dc3dd if=\u002Fdev\u002Fsda of=\u002Fevidence\u002Fdisk.dd hash=sha256 log=\u002Fevidence\u002Fimaging.log\n\n# Verify image integrity\nsha256sum \u002Fevidence\u002Fdisk.dd\n# Compare with hash generated during imaging\n```\n\n### Step 3: Memory Analysis with Volatility 3\n\n```bash\n# Identify OS profile\nvol -f memdump.raw windows.info\n\n# List running processes\nvol -f memdump.raw windows.pslist\nvol -f memdump.raw windows.pstree\n\n# Find hidden processes\nvol -f memdump.raw windows.psscan\n\n# Analyze network connections\nvol -f memdump.raw windows.netscan\n\n# Detect process injection\nvol -f memdump.raw windows.malfind\n\n# Extract command line arguments\nvol -f memdump.raw windows.cmdline\n\n# Analyze DLLs loaded by processes\nvol -f memdump.raw windows.dlllist --pid 1234\n\n# Extract files from memory\nvol -f memdump.raw windows.filescan | grep -i \"suspicious\"\nvol -f memdump.raw windows.dumpfiles --pid 1234\n\n# Detect credential theft\nvol -f memdump.raw windows.hashdump\nvol -f memdump.raw windows.lsadump\n\n# Registry analysis from memory\nvol -f memdump.raw windows.registry.printkey --key \"Software\\Microsoft\\Windows\\CurrentVersion\\Run\"\n```\n\n### Step 4: Windows Artifact Analysis\n\n```\nKey forensic artifacts and their tools:\n\nPrefetch Files (C:\\Windows\\Prefetch\\):\n  Tool: PECmd.exe (Eric Zimmerman)\n  Shows: Program execution history with timestamps and run counts\n  Command: PECmd.exe -d \"C:\\Windows\\Prefetch\" --csv output\\\n\nShimCache (AppCompatCache):\n  Tool: AppCompatCacheParser.exe\n  Shows: Programs that existed on system (even if deleted)\n  Command: AppCompatCacheParser.exe -f SYSTEM --csv output\\\n\nAmCache (C:\\Windows\\appcompat\\Programs\\Amcache.hve):\n  Tool: AmcacheParser.exe\n  Shows: Program execution with SHA1 hashes and install timestamps\n  Command: AmcacheParser.exe -f Amcache.hve --csv output\\\n\nNTFS artifacts ($MFT, $UsnJrnl, $LogFile):\n  Tool: MFTECmd.exe\n  Shows: Complete file system timeline including deleted files\n  Command: MFTECmd.exe -f \"$MFT\" --csv output\\\n\nEvent Logs:\n  Tool: EvtxECmd.exe\n  Shows: Security, System, PowerShell, Sysmon events\n  Command: EvtxECmd.exe -d \"C:\\Windows\\System32\\winevt\\Logs\" --csv output\\\n\nRegistry Hives (SAM, SYSTEM, SOFTWARE, NTUSER.DAT):\n  Tool: RECmd.exe with batch files\n  Shows: User accounts, services, installed software, USB history\n  Command: RECmd.exe -d \"C:\\Windows\\System32\\config\" --bn BatchExamples\\RECmd_Batch_MC.reb --csv output\\\n```\n\n### Step 5: Timeline Reconstruction\n\n```bash\n# Use KAPE for automated artifact collection\nkape.exe --tsource C: --tdest C:\\evidence\\kape_output \\\n  --target KapeTriage --module !EZParser\n\n# Create super timeline with plaso\u002Flog2timeline\nlog2timeline.py timeline.plaso disk_image.E01\npsort.py -o l2tcsv timeline.plaso -w timeline.csv\n\n# Filter timeline around incident timeframe\npsort.py -o l2tcsv timeline.plaso \"date > '2026-02-20' AND date \u003C '2026-02-22'\" -w filtered_timeline.csv\n```\n\n### Step 6: Document Findings\n\nStructure forensic report:\n```\n1. Executive Summary\n2. Scope and Methodology\n3. Evidence Inventory (with chain of custody)\n4. Timeline of Events\n5. Findings and Analysis\n   - Initial access vector\n   - Persistence mechanisms\n   - Lateral movement\n   - Data access\u002Fexfiltration\n6. Indicators of Compromise (IOCs)\n7. Recommendations\n8. Appendices (tool output, hashes, raw evidence)\n```\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **Order of Volatility** | Evidence collection priority from most volatile (RAM) to least volatile (backups) |\n| **Chain of Custody** | Documented record of evidence handling from collection to presentation |\n| **Write Blocker** | Hardware or software device that prevents modification of source evidence |\n| **Super Timeline** | Consolidated chronological view of all artifact timestamps for incident reconstruction |\n| **Prefetch** | Windows artifact recording program execution history |\n| **ShimCache** | Application compatibility artifact tracking program existence on endpoint |\n\n## Tools & Systems\n\n- **Volatility 3**: Memory forensics framework for analyzing RAM dumps\n- **KAPE (Kroll Artifact Parser and Extractor)**: Automated triage collection and parsing\n- **Eric Zimmerman Tools**: Suite of Windows artifact parsers (PECmd, MFTECmd, RECmd, etc.)\n- **Autopsy\u002FSleuth Kit**: Disk forensics platform for file system analysis\n- **FTK Imager**: Forensic imaging and memory acquisition tool\n- **Plaso\u002Flog2timeline**: Super timeline creation framework\n\n## Common Pitfalls\n\n- **Modifying evidence on live system**: Always image before analysis. Running tools on a live system alters timestamps and memory state.\n- **Forgetting chain of custody**: Evidence without documented chain of custody is inadmissible in legal proceedings.\n- **Analyzing only disk, ignoring memory**: In-memory-only malware (fileless attacks) leaves no disk artifacts. Always capture memory first.\n- **Not hashing evidence**: All evidence must be cryptographically hashed at collection time to prove integrity.\n- **Tunnel vision**: Focusing on one artifact when the timeline tells a broader story. Always build a comprehensive timeline.\n","# Performing Endpoint Forensics Investigation\n\n## When to Use\n\nUse this skill when:\n- Investigating a confirmed or suspected endpoint compromise requiring forensic analysis\n- Collecting volatile and non-volatile evidence for incident response or legal proceedings\n- Analyzing memory dumps for malware, injected code, or credential theft artifacts\n- Reconstructing attacker timelines from endpoint artifacts (prefetch, shimcache, amcache)\n\n**Do not use** this skill for live threat hunting (use EDR\u002FSIEM) or network forensics.\n\n## Prerequisites\n\n- Forensic workstation with analysis tools (Volatility 3, KAPE, Autopsy, Eric Zimmerman tools)\n- Write-blocker for disk imaging (hardware or software)\n- Secure evidence storage with chain-of-custody documentation\n- Memory acquisition tool (WinPMEM, FTK Imager, Magnet RAM Capture)\n- Administrative access to the target endpoint (or physical access)\n\n## Workflow\n\n### Step 1: Evidence Preservation (Order of Volatility)\n\nCollect evidence from most volatile to least volatile:\n```\n1. System memory (RAM) - Most volatile\n2. Network connections and routing tables\n3. Running processes and open files\n4. Disk contents (file system)\n5. Removable media\n6. Logs and backup data - Least volatile\n```\n\n**Memory Acquisition**:\n```powershell\n# WinPMEM (Windows)\nwinpmem_mini_x64.exe memdump.raw\n\n# FTK Imager - Create memory capture via GUI\n# File → Capture Memory → Destination path → Capture Memory\n\n# Linux (LiME kernel module)\nsudo insmod lime.ko \"path=\u002Fevidence\u002Fmemory.lime format=lime\"\n```\n\n**Volatile Data Collection**:\n```powershell\n# Capture running processes\nGet-Process | Export-Csv \"evidence\\processes.csv\" -NoTypeInformation\ntasklist \u002Fv > \"evidence\\tasklist.txt\"\n\n# Capture network connections\nnetstat -anob > \"evidence\\netstat.txt\"\nGet-NetTCPConnection | Export-Csv \"evidence\\tcp_connections.csv\"\n\n# Capture logged-on users\nquery user > \"evidence\\logged_users.txt\"\n\n# Capture scheduled tasks\nschtasks \u002Fquery \u002Ffo CSV \u002Fv > \"evidence\\scheduled_tasks.csv\"\n\n# Capture services\nGet-Service | Export-Csv \"evidence\\services.csv\"\n\n# Capture DNS cache\nipconfig \u002Fdisplaydns > \"evidence\\dns_cache.txt\"\n```\n\n### Step 2: Disk Imaging\n\n```powershell\n# FTK Imager - Create forensic disk image\n# File → Create Disk Image → Physical Drive → E01 format\n# Always verify image hash (MD5\u002FSHA1) matches source\n\n# dd (Linux)\nsudo dc3dd if=\u002Fdev\u002Fsda of=\u002Fevidence\u002Fdisk.dd hash=sha256 log=\u002Fevidence\u002Fimaging.log\n\n# Verify image integrity\nsha256sum \u002Fevidence\u002Fdisk.dd\n# Compare with hash generated during imaging\n```\n\n### Step 3: Memory Analysis with Volatility 3\n\n```bash\n# Identify OS profile\nvol -f memdump.raw windows.info\n\n# List running processes\nvol -f memdump.raw windows.pslist\nvol -f memdump.raw windows.pstree\n\n# Find hidden processes\nvol -f memdump.raw windows.psscan\n\n# Analyze network connections\nvol -f memdump.raw windows.netscan\n\n# Detect process injection\nvol -f memdump.raw windows.malfind\n\n# Extract command line arguments\nvol -f memdump.raw windows.cmdline\n\n# Analyze DLLs loaded by processes\nvol -f memdump.raw windows.dlllist --pid 1234\n\n# Extract files from memory\nvol -f memdump.raw windows.filescan | grep -i \"suspicious\"\nvol -f memdump.raw windows.dumpfiles --pid 1234\n\n# Detect credential theft\nvol -f memdump.raw windows.hashdump\nvol -f memdump.raw windows.lsadump\n\n# Registry analysis from memory\nvol -f memdump.raw windows.registry.printkey --key \"Software\\Microsoft\\Windows\\CurrentVersion\\Run\"\n```\n\n### Step 4: Windows Artifact Analysis\n\n```\nKey forensic artifacts and their tools:\n\nPrefetch Files (C:\\Windows\\Prefetch\\):\n  Tool: PECmd.exe (Eric Zimmerman)\n  Shows: Program execution history with timestamps and run counts\n  Command: PECmd.exe -d \"C:\\Windows\\Prefetch\" --csv output\\\n\nShimCache (AppCompatCache):\n  Tool: AppCompatCacheParser.exe\n  Shows: Programs that existed on system (even if deleted)\n  Command: AppCompatCacheParser.exe -f SYSTEM --csv output\\\n\nAmCache (C:\\Windows\\appcompat\\Programs\\Amcache.hve):\n  Tool: AmcacheParser.exe\n  Shows: Program execution with SHA1 hashes and install timestamps\n  Command: AmcacheParser.exe -f Amcache.hve --csv output\\\n\nNTFS artifacts ($MFT, $UsnJrnl, $LogFile):\n  Tool: MFTECmd.exe\n  Shows: Complete file system timeline including deleted files\n  Command: MFTECmd.exe -f \"$MFT\" --csv output\\\n\nEvent Logs:\n  Tool: EvtxECmd.exe\n  Shows: Security, System, PowerShell, Sysmon events\n  Command: EvtxECmd.exe -d \"C:\\Windows\\System32\\winevt\\Logs\" --csv output\\\n\nRegistry Hives (SAM, SYSTEM, SOFTWARE, NTUSER.DAT):\n  Tool: RECmd.exe with batch files\n  Shows: User accounts, services, installed software, USB history\n  Command: RECmd.exe -d \"C:\\Windows\\System32\\config\" --bn BatchExamples\\RECmd_Batch_MC.reb --csv output\\\n```\n\n### Step 5: Timeline Reconstruction\n\n```bash\n# Use KAPE for automated artifact collection\nkape.exe --tsource C: --tdest C:\\evidence\\kape_output \\\n  --target KapeTriage --module !EZParser\n\n# Create super timeline with plaso\u002Flog2timeline\nlog2timeline.py timeline.plaso disk_image.E01\npsort.py -o l2tcsv timeline.plaso -w timeline.csv\n\n# Filter timeline around incident timeframe\npsort.py -o l2tcsv timeline.plaso \"date > '2026-02-20' AND date \u003C '2026-02-22'\" -w filtered_timeline.csv\n```\n\n### Step 6: Document Findings\n\nStructure forensic report:\n```\n1. Executive Summary\n2. Scope and Methodology\n3. Evidence Inventory (with chain of custody)\n4. Timeline of Events\n5. Findings and Analysis\n   - Initial access vector\n   - Persistence mechanisms\n   - Lateral movement\n   - Data access\u002Fexfiltration\n6. Indicators of Compromise (IOCs)\n7. Recommendations\n8. Appendices (tool output, hashes, raw evidence)\n```\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **Order of Volatility** | Evidence collection priority from most volatile (RAM) to least volatile (backups) |\n| **Chain of Custody** | Documented record of evidence handling from collection to presentation |\n| **Write Blocker** | Hardware or software device that prevents modification of source evidence |\n| **Super Timeline** | Consolidated chronological view of all artifact timestamps for incident reconstruction |\n| **Prefetch** | Windows artifact recording program execution history |\n| **ShimCache** | Application compatibility artifact tracking program existence on endpoint |\n\n## Tools & Systems\n\n- **Volatility 3**: Memory forensics framework for analyzing RAM dumps\n- **KAPE (Kroll Artifact Parser and Extractor)**: Automated triage collection and parsing\n- **Eric Zimmerman Tools**: Suite of Windows artifact parsers (PECmd, MFTECmd, RECmd, etc.)\n- **Autopsy\u002FSleuth Kit**: Disk forensics platform for file system analysis\n- **FTK Imager**: Forensic imaging and memory acquisition tool\n- **Plaso\u002Flog2timeline**: Super timeline creation framework\n\n## Common Pitfalls\n\n- **Modifying evidence on live system**: Always image before analysis. Running tools on a live system alters timestamps and memory state.\n- **Forgetting chain of custody**: Evidence without documented chain of custody is inadmissible in legal proceedings.\n- **Analyzing only disk, ignoring memory**: In-memory-only malware (fileless attacks) leaves no disk artifacts. Always capture memory first.\n- **Not hashing evidence**: All evidence must be cryptographically hashed at collection time to prove integrity.\n- **Tunnel vision**: Focusing on one artifact when the timeline tells a broader story. Always build a comprehensive timeline.\n",{"name":5,"tags":48,"author":49,"domain":41,"license":15,"version":50,"nist_csf":51,"subdomain":28,"description":56,"mitre_attack":57},[27,26,34,39,38,37],"mahipal","1.0.0",[52,53,54,55],"PR.PS-01","PR.PS-02","DE.CM-01","PR.IR-01","Performs digital forensics investigation on compromised endpoints including memory acquisition, disk imaging, artifact analysis, and timeline reconstruction. Use when investigating security incidents, collecting evidence for legal proceedings, or analyzing endpoint compromise scope. Activates for requests involving endpoint forensics, memory analysis, disk forensics, or incident investigation.\n",[58,59,60,61,62],"T1055","T1547","T1059","T1036","T1005","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fperforming-endpoint-forensics-investigation",4414,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","https:\u002F\u002Fmahipal.engineer\u002FAnthropic-Cybersecurity-Skills\u002F","https:\u002F\u002Fgithub.com\u002Fmukul975","Mahipal.engineer","🔐 Cybersecurity | 💻 Dev | 📷 Street Photographer\r\n🎓 MSc | 📚 Research & AI Security","2026-08-31 12:32:44",{"source":8,"skillName":5,"commands":74,"prompt":84,"zipAvailable":85},[75,78,81],{"tool":76,"command":77},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-endpoint-forensics-investigation",{"tool":79,"command":80},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-endpoint-forensics-investigation",{"tool":82,"command":83},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-endpoint-forensics-investigation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002FSKILL.md",true,[87,93,98,104,108,112,118],{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"assets\u002Ftemplate.md","ASSET","资产文件",1094,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002Fassets\u002Ftemplate.md",{"path":94,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":96,"sourceHtmlUrl":97},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002FLICENSE",{"path":99,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fapi-reference.md","REFERENCE","参考文档",6752,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002Freferences\u002Fapi-reference.md",{"path":105,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":106,"sourceHtmlUrl":107},"references\u002Fstandards.md",1384,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002Freferences\u002Fstandards.md",{"path":109,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":110,"sourceHtmlUrl":111},"references\u002Fworkflows.md",1576,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002Freferences\u002Fworkflows.md",{"path":113,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":116,"sourceHtmlUrl":117},"scripts\u002Fagent.py","SCRIPT","脚本",5741,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002Fscripts\u002Fagent.py",{"path":119,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":120,"sourceHtmlUrl":121},"scripts\u002Fprocess.py",7161,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-endpoint-forensics-investigation\u002Fscripts\u002Fprocess.py",[123,148,172,200,226,253],{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":130,"publishedAt":43},"01M6AGDM3HJXTKXX15ZZ0ZVCJ3","verifying-build-provenance-with-slsa-sigstore","Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI\u002FCD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fverifying-build-provenance-with-slsa-sigstore\u002FSKILL.md",[129],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[131,132,133,134,35,41,135,136,40,137,138,139,140,141,142,143,144,145,146,147],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","incident response","identity","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":43},"01M6AGDGBPWYVZ2F11GFKJ4HZ9","validating-tpm-measured-boot-attestation","Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote\u002Ftpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware\u002Fkernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-tpm-measured-boot-attestation\u002FSKILL.md",[154],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[156,144,157,158,35,159,41,40,160,161,162,163,164,165,166,167,168,169,170,171],"boot","TPM","validating tpm measured boot attestation","policy","verification","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":182,"publishedAt":43},"01M6AGDCW0Y4FXZ3362GT1941M","validating-backup-integrity-for-recovery","Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable\u002Fair-gapped backups for silent corruption or tampering.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-backup-integrity-for-recovery\u002FSKILL.md",[178,181],{"categoryNo":179,"categoryName":180,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[166,183,184,185,186,41,187,188,160,189,190,191,192,193,194,195,196,197,198,199],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":207,"publishedAt":43},"01M6AGD9GBG2KZ336AE763J3WH","triaging-windows-with-kape","Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment\u002Ftriage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-windows-with-kape\u002FSKILL.md",[206],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[208,209,210,41,211,212,213,214,215,216,217,218,219,220,221,222,163,223,224,225],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","data","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":230,"categories":231,"keywords":233,"publishedAt":43},"01M6AGD5PW5J4EVZM958WD74ZQ","triaging-vulnerabilities-with-ssvc-framework","Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track\u002FTrack*\u002FAttend\u002FAct decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-vulnerabilities-with-ssvc-framework\u002FSKILL.md",[232],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[234,235,236,41,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":254,"name":255,"nameZh":6,"description":256,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":257,"categories":258,"keywords":260,"publishedAt":43},"01M6AGD24TTQQM4CXG69E3B9TJ","triaging-security-incident","Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM\u002FEDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-security-incident\u002FSKILL.md",[259],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[35,261,41,262,263,264,265,266,267,268,269,195,270,196,29,271,272,273,274,275],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]