[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE3YFNQPGXTHVGQC0Q0P99":3,"skill-same:01M6AE3YFNQPGXTHVGQC0Q0P99":115},{"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":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6AE3YFNQPGXTHVGQC0Q0P99","conducting-post-incident-lessons-learned",null,"Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.","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\u002Fconducting-post-incident-lessons-learned\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],"conducting post incident lessons learned","review","cybersecurity","incident response","produce","incident-response","lessons-learned","post-incident","after-action-review","process-improvement","2026-11-10 10:12:30",false,"---\nname: conducting-post-incident-lessons-learned\ndescription: Facilitate structured post-incident reviews to identify root causes,\n  document what worked and failed, and produce actionable recommendations to improve\n  future incident response.\ndomain: cybersecurity\nsubdomain: incident-response\ntags:\n- incident-response\n- lessons-learned\n- post-incident\n- after-action-review\n- process-improvement\nmitre_attack:\n- T1566\n- T1486\n- T1059\n- T1078\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- RS.MA-01\n- RS.MA-02\n- RS.AN-03\n- RC.RP-01\n---\n\n# Conducting Post-Incident Lessons Learned\n\n## When to Use\n- After any security incident has been fully resolved and recovery completed\n- Following tabletop exercises or IR simulations\n- After significant near-miss events\n- Quarterly review of accumulated incident trends\n- When IR playbooks need updating based on real-world experience\n\n## Prerequisites\n- Incident fully resolved (containment, eradication, recovery complete)\n- Incident timeline and documentation gathered\n- All incident responders available for review session\n- Meeting space for collaborative discussion\n- Incident ticketing system data for metrics analysis\n\n## Workflow\n\n### Step 1: Gather Incident Data\n```bash\n# Export incident timeline from ticketing system\ncurl -s \"https:\u002F\u002Fthehive.local\u002Fapi\u002Fv1\u002Fcase\u002F$CASE_ID\u002Ftimeline\" \\\n  -H \"Authorization: Bearer $THEHIVE_API_KEY\" | jq '.' > incident_timeline.json\n\n# Extract detection and response metrics from SIEM\nindex=notable incident_id=\"IR-2024-042\"\n| stats min(_time) as first_alert, max(_time) as last_alert,\n  count as total_alerts, dc(src) as unique_sources\n\n# Compile all responder actions and timestamps\ngrep -E \"timestamp|action|analyst\" \u002Fvar\u002Flog\u002Fir\u002FIR-2024-042\u002F*.json | \\\n  python3 -m json.tool > compiled_actions.json\n```\n\n### Step 2: Conduct Blameless Post-Mortem Meeting\n```\nStructured Agenda (90 minutes):\n1. Incident summary (5 min) - Factual overview\n2. Timeline walkthrough (20 min) - Chronological events\n3. What worked well (15 min) - Positive outcomes\n4. What needs improvement (15 min) - Gaps and failures\n5. Root cause analysis (15 min) - 5 Whys or fishbone\n6. Action items (10 min) - Specific improvements with owners\n7. Playbook updates (10 min) - Changes to IR procedures\n\nBlameless Principles:\n- Focus on systems and processes, not individuals\n- Assume best intentions with available information\n- Seek to understand, not to blame\n```\n\n### Step 3: Perform Root Cause Analysis\n```bash\n# 5 Whys analysis example:\n# Why 1: Why did ransomware encrypt production servers?\n#   Answer: Attacker had domain admin credentials\n# Why 2: Why did attacker have domain admin credentials?\n#   Answer: Kerberoasted a service account and cracked it\n# Why 3: Why was the service account password crackable?\n#   Answer: Used a 12-character dictionary-based password\n# Why 4: Why was the service account password weak?\n#   Answer: No enforcement of service account password policy\n# Why 5: Why was there no service account password policy?\n#   Answer: PAM was not implemented for service accounts\n# ROOT CAUSE: Lack of privileged access management\n```\n\n### Step 4: Calculate Response Metrics\n```python\nfrom datetime import datetime\nevents = {\n    'compromise': '2024-01-10 14:00:00',\n    'detection': '2024-01-15 08:30:00',\n    'triage': '2024-01-15 08:45:00',\n    'containment': '2024-01-15 09:30:00',\n    'eradication': '2024-01-16 14:00:00',\n    'recovery': '2024-01-18 16:00:00',\n    'closure': '2024-01-25 10:00:00',\n}\nfmt = '%Y-%m-%d %H:%M:%S'\ntimes = {k: datetime.strptime(v, fmt) for k, v in events.items()}\nprint(f\"Dwell Time: {times['detection'] - times['compromise']}\")\nprint(f\"MTTD: {times['triage'] - times['detection']}\")\nprint(f\"MTTC: {times['containment'] - times['detection']}\")\nprint(f\"MTTR: {times['recovery'] - times['eradication']}\")\nprint(f\"Total Duration: {times['closure'] - times['detection']}\")\n```\n\n### Step 5: Document Findings and Create Action Items\n```bash\n# Create tracked action items in project management\ncurl -X POST \"https:\u002F\u002Fjira.local\u002Frest\u002Fapi\u002F2\u002Fissue\" \\\n  -H \"Authorization: Bearer $JIRA_TOKEN\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"fields\": {\n      \"project\": {\"key\": \"SEC\"},\n      \"summary\": \"Implement PAM for service accounts (IR-2024-042)\",\n      \"issuetype\": {\"name\": \"Task\"},\n      \"priority\": {\"name\": \"High\"},\n      \"assignee\": {\"name\": \"security_engineer\"},\n      \"duedate\": \"2024-03-15\"\n    }\n  }'\n```\n\n### Step 6: Update Playbooks and Detection Rules\n```yaml\n# New Sigma detection rule based on incident learnings\ntitle: Kerberoasting Activity Detected\nstatus: stable\ndescription: Detects Kerberoasting based on IR-2024-042 lessons\nlogsource:\n  product: windows\n  service: security\ndetection:\n  selection:\n    EventID: 4769\n    TicketEncryptionType: '0x17'\n  condition: selection\nlevel: high\ntags:\n  - attack.credential_access\n  - attack.t1558.003\n```\n\n## Key Concepts\n\n| Concept | Description |\n|---------|-------------|\n| Blameless Post-Mortem | Reviewing incidents focusing on systems, not blaming individuals |\n| Root Cause Analysis | Identifying the fundamental reason the incident occurred |\n| 5 Whys | Iterative questioning technique to find root cause |\n| MTTD | Mean Time to Detect - time from compromise to detection |\n| MTTC | Mean Time to Contain - time from detection to containment |\n| MTTR | Mean Time to Recover - time from eradication to full recovery |\n| Continuous Improvement | Iterating on IR processes based on real incident data |\n\n## Tools & Systems\n\n| Tool | Purpose |\n|------|---------|\n| TheHive\u002FServiceNow | Incident timeline and documentation |\n| Jira\u002FAzure DevOps | Action item tracking |\n| Confluence\u002FSharePoint | Lessons learned documentation |\n| Splunk\u002FElastic | Incident metrics and detection improvement |\n| Sigma | Detection rule development |\n\n## Common Scenarios\n\n1. **Ransomware Post-Mortem**: Review entire kill chain from initial access to encryption. Identify detection gaps and backup failures.\n2. **Phishing Campaign Review**: Analyze why users clicked, why email filters missed it, and how to improve training.\n3. **Cloud Misconfiguration Incident**: Review IaC pipeline, CSPM coverage, and change management process.\n4. **Insider Threat Review**: Examine DLP effectiveness, access control gaps, and user monitoring capabilities.\n5. **Third-Party Breach Impact**: Review vendor risk assessment process and data sharing agreements.\n\n## Output Format\n- Post-incident review meeting minutes\n- Root cause analysis document\n- Incident metrics report (MTTD, MTTC, MTTR)\n- Action items list with owners and deadlines\n- Updated IR playbooks and detection rules\n- Executive summary for leadership\n","# Conducting Post-Incident Lessons Learned\n\n## When to Use\n- After any security incident has been fully resolved and recovery completed\n- Following tabletop exercises or IR simulations\n- After significant near-miss events\n- Quarterly review of accumulated incident trends\n- When IR playbooks need updating based on real-world experience\n\n## Prerequisites\n- Incident fully resolved (containment, eradication, recovery complete)\n- Incident timeline and documentation gathered\n- All incident responders available for review session\n- Meeting space for collaborative discussion\n- Incident ticketing system data for metrics analysis\n\n## Workflow\n\n### Step 1: Gather Incident Data\n```bash\n# Export incident timeline from ticketing system\ncurl -s \"https:\u002F\u002Fthehive.local\u002Fapi\u002Fv1\u002Fcase\u002F$CASE_ID\u002Ftimeline\" \\\n  -H \"Authorization: Bearer $THEHIVE_API_KEY\" | jq '.' > incident_timeline.json\n\n# Extract detection and response metrics from SIEM\nindex=notable incident_id=\"IR-2024-042\"\n| stats min(_time) as first_alert, max(_time) as last_alert,\n  count as total_alerts, dc(src) as unique_sources\n\n# Compile all responder actions and timestamps\ngrep -E \"timestamp|action|analyst\" \u002Fvar\u002Flog\u002Fir\u002FIR-2024-042\u002F*.json | \\\n  python3 -m json.tool > compiled_actions.json\n```\n\n### Step 2: Conduct Blameless Post-Mortem Meeting\n```\nStructured Agenda (90 minutes):\n1. Incident summary (5 min) - Factual overview\n2. Timeline walkthrough (20 min) - Chronological events\n3. What worked well (15 min) - Positive outcomes\n4. What needs improvement (15 min) - Gaps and failures\n5. Root cause analysis (15 min) - 5 Whys or fishbone\n6. Action items (10 min) - Specific improvements with owners\n7. Playbook updates (10 min) - Changes to IR procedures\n\nBlameless Principles:\n- Focus on systems and processes, not individuals\n- Assume best intentions with available information\n- Seek to understand, not to blame\n```\n\n### Step 3: Perform Root Cause Analysis\n```bash\n# 5 Whys analysis example:\n# Why 1: Why did ransomware encrypt production servers?\n#   Answer: Attacker had domain admin credentials\n# Why 2: Why did attacker have domain admin credentials?\n#   Answer: Kerberoasted a service account and cracked it\n# Why 3: Why was the service account password crackable?\n#   Answer: Used a 12-character dictionary-based password\n# Why 4: Why was the service account password weak?\n#   Answer: No enforcement of service account password policy\n# Why 5: Why was there no service account password policy?\n#   Answer: PAM was not implemented for service accounts\n# ROOT CAUSE: Lack of privileged access management\n```\n\n### Step 4: Calculate Response Metrics\n```python\nfrom datetime import datetime\nevents = {\n    'compromise': '2024-01-10 14:00:00',\n    'detection': '2024-01-15 08:30:00',\n    'triage': '2024-01-15 08:45:00',\n    'containment': '2024-01-15 09:30:00',\n    'eradication': '2024-01-16 14:00:00',\n    'recovery': '2024-01-18 16:00:00',\n    'closure': '2024-01-25 10:00:00',\n}\nfmt = '%Y-%m-%d %H:%M:%S'\ntimes = {k: datetime.strptime(v, fmt) for k, v in events.items()}\nprint(f\"Dwell Time: {times['detection'] - times['compromise']}\")\nprint(f\"MTTD: {times['triage'] - times['detection']}\")\nprint(f\"MTTC: {times['containment'] - times['detection']}\")\nprint(f\"MTTR: {times['recovery'] - times['eradication']}\")\nprint(f\"Total Duration: {times['closure'] - times['detection']}\")\n```\n\n### Step 5: Document Findings and Create Action Items\n```bash\n# Create tracked action items in project management\ncurl -X POST \"https:\u002F\u002Fjira.local\u002Frest\u002Fapi\u002F2\u002Fissue\" \\\n  -H \"Authorization: Bearer $JIRA_TOKEN\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"fields\": {\n      \"project\": {\"key\": \"SEC\"},\n      \"summary\": \"Implement PAM for service accounts (IR-2024-042)\",\n      \"issuetype\": {\"name\": \"Task\"},\n      \"priority\": {\"name\": \"High\"},\n      \"assignee\": {\"name\": \"security_engineer\"},\n      \"duedate\": \"2024-03-15\"\n    }\n  }'\n```\n\n### Step 6: Update Playbooks and Detection Rules\n```yaml\n# New Sigma detection rule based on incident learnings\ntitle: Kerberoasting Activity Detected\nstatus: stable\ndescription: Detects Kerberoasting based on IR-2024-042 lessons\nlogsource:\n  product: windows\n  service: security\ndetection:\n  selection:\n    EventID: 4769\n    TicketEncryptionType: '0x17'\n  condition: selection\nlevel: high\ntags:\n  - attack.credential_access\n  - attack.t1558.003\n```\n\n## Key Concepts\n\n| Concept | Description |\n|---------|-------------|\n| Blameless Post-Mortem | Reviewing incidents focusing on systems, not blaming individuals |\n| Root Cause Analysis | Identifying the fundamental reason the incident occurred |\n| 5 Whys | Iterative questioning technique to find root cause |\n| MTTD | Mean Time to Detect - time from compromise to detection |\n| MTTC | Mean Time to Contain - time from detection to containment |\n| MTTR | Mean Time to Recover - time from eradication to full recovery |\n| Continuous Improvement | Iterating on IR processes based on real incident data |\n\n## Tools & Systems\n\n| Tool | Purpose |\n|------|---------|\n| TheHive\u002FServiceNow | Incident timeline and documentation |\n| Jira\u002FAzure DevOps | Action item tracking |\n| Confluence\u002FSharePoint | Lessons learned documentation |\n| Splunk\u002FElastic | Incident metrics and detection improvement |\n| Sigma | Detection rule development |\n\n## Common Scenarios\n\n1. **Ransomware Post-Mortem**: Review entire kill chain from initial access to encryption. Identify detection gaps and backup failures.\n2. **Phishing Campaign Review**: Analyze why users clicked, why email filters missed it, and how to improve training.\n3. **Cloud Misconfiguration Incident**: Review IaC pipeline, CSPM coverage, and change management process.\n4. **Insider Threat Review**: Examine DLP effectiveness, access control gaps, and user monitoring capabilities.\n5. **Third-Party Breach Impact**: Review vendor risk assessment process and data sharing agreements.\n\n## Output Format\n- Post-incident review meeting minutes\n- Root cause analysis document\n- Incident metrics report (MTTD, MTTC, MTTR)\n- Action items list with owners and deadlines\n- Updated IR playbooks and detection rules\n- Executive summary for leadership\n",{"name":5,"tags":39,"author":40,"domain":26,"license":15,"version":41,"nist_csf":42,"subdomain":29,"description":7,"mitre_attack":47},[29,30,31,32,33],"mahipal","1.0",[43,44,45,46],"RS.MA-01","RS.MA-02","RS.AN-03","RC.RP-01",[48,49,50,51],"T1566","T1486","T1059","T1078","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fconducting-post-incident-lessons-learned",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":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned",{"tool":68,"command":69},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned",{"tool":71,"command":72},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-post-incident-lessons-learned\u002FSKILL.md",true,[76,82,88,93,97,101,105,111],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"agents\u002Fopenai.yaml","REFERENCE","参考文档",100,"https:\u002F\u002Fgithub.com\u002FVortex375\u002Fseraph\u002Fblob\u002Fmain\u002Fapp\u002Fseraph_app\u002F.agents\u002Fskills\u002Fcode-review\u002Fagents\u002Fopenai.yaml",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"assets\u002Ftemplate.md","ASSET","资产文件",2199,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-post-incident-lessons-learned\u002Fassets\u002Ftemplate.md",{"path":89,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-post-incident-lessons-learned\u002FLICENSE",{"path":94,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fapi-reference.md",1535,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-post-incident-lessons-learned\u002Freferences\u002Fapi-reference.md",{"path":98,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fstandards.md",1208,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-post-incident-lessons-learned\u002Freferences\u002Fstandards.md",{"path":102,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fworkflows.md",1993,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-post-incident-lessons-learned\u002Freferences\u002Fworkflows.md",{"path":106,"resourceType":107,"resourceTypeDesc":108,"sizeBytes":109,"sourceHtmlUrl":110},"scripts\u002Fagent.py","SCRIPT","脚本",5815,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-post-incident-lessons-learned\u002Fscripts\u002Fagent.py",{"path":112,"resourceType":107,"resourceTypeDesc":108,"sizeBytes":113,"sourceHtmlUrl":114},"scripts\u002Fprocess.py",9431,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-post-incident-lessons-learned\u002Fscripts\u002Fprocess.py",[116,142,166,193,219,246],{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":123,"publishedAt":34},"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",[122],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[124,125,126,127,128,26,27,129,130,131,132,133,134,135,136,137,138,139,140,141],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","identity","pr","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":149,"publishedAt":34},"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",[148],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[150,138,151,152,128,153,26,130,154,155,156,157,158,159,160,161,162,163,164,165],"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":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":176,"publishedAt":34},"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",[172,175],{"categoryNo":173,"categoryName":174,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[160,177,178,179,180,26,181,182,154,183,184,29,185,186,187,188,189,190,191,192],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":200,"publishedAt":34},"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",[199],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[201,202,203,26,204,205,206,207,208,209,210,211,212,213,214,215,157,216,217,218],"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":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":223,"categories":224,"keywords":226,"publishedAt":34},"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",[225],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[227,228,229,26,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245],"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":247,"name":248,"nameZh":6,"description":249,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":250,"categories":251,"keywords":253,"publishedAt":34},"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",[252],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[128,254,26,255,256,257,258,259,260,261,262,188,263,189,264,265,266,267,268,269],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]