[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADNMMXDFXB9X3W81YNVYXJ":3,"skill-same:01M6ADNMMXDFXB9X3W81YNVYXJ":129},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":88,"commitSha":89,"skillDirectory":90,"forksCount":91,"repositoryHtmlUrl":92,"repositoryHomepageUrl":93,"ownerHtmlUrl":94,"ownerBlogUrl":95,"ownerDescription":96,"updatedAt":97,"install":98,"resources":111},"01M6ADNMMXDFXB9X3W81YNVYXJ","analyzing-indicators-of-compromise",null,"Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block\u002Fmonitor\u002Fwhitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.","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\u002Fanalyzing-indicators-of-compromise\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"analyzing indicators of compromise","ioc","MISP","MalwareBazaar","AbuseIPDB","VirusTotal","NIST-CSF","phishing","attribution","stix","cybersecurity","threat-intelligence","email","file","artifacts","create","development","monitor","campaign attribution","security alerts","2026-11-10 10:12:30",false,"---\nname: analyzing-indicators-of-compromise\ndescription: 'Analyzes indicators of compromise (IOCs) including IP addresses, domains,\n  file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign\n  attribution, and blocking priority. Use when triaging IOCs from phishing emails,\n  security alerts, or external threat feeds; enriching raw IOCs with multi-source\n  intelligence; or making block\u002Fmonitor\u002Fwhitelist decisions. Activates for requests\n  involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.\n\n  '\ndomain: cybersecurity\nsubdomain: threat-intelligence\ntags:\n- IOC\n- VirusTotal\n- AbuseIPDB\n- MalwareBazaar\n- MISP\n- threat-intelligence\n- STIX\n- NIST-CSF\nversion: 1.0.0\nauthor: mahipal\nlicense: Apache-2.0\natlas_techniques:\n- AML.T0052\nnist_csf:\n- ID.RA-01\n- ID.RA-05\n- DE.CM-01\n- DE.AE-02\nmitre_attack:\n- T1071\n- T1105\n- T1041\n- T1567\nmitre_f3:\n  version: '1.1'\n  tactics:\n  - reconnaissance\n  - resource-development\n  - initial-access\n  techniques:\n  - id: T1598\n    name: Phishing for Information\n    tactic: reconnaissance\n    source: attack\n  - id: T1660\n    name: Phishing\n    tactic: initial-access\n    source: attack\n  - id: T1583.001\n    name: 'Acquire Infrastructure: Domains'\n    tactic: resource-development\n    source: attack\n  - id: F1020.002\n    name: 'Create Fake Materials: Fake Website'\n    tactic: resource-development\n    source: f3\n---\n# Analyzing Indicators of Compromise\n\n## When to Use\n\nUse this skill when:\n- A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage\n- Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls\n- An incident investigation requires contextual enrichment of observed network artifacts\n\n**Do not use** this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).\n\n## Prerequisites\n\n- VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup\n- AbuseIPDB API key for IP reputation checks\n- MISP instance or TIP for cross-referencing against known campaigns\n- Python with `requests` and `vt-py` libraries, or SOAR platform with pre-built connectors\n\n## Workflow\n\n### Step 1: Normalize and Classify IOC Types\n\nBefore enriching, classify each IOC:\n- **IPv4\u002FIPv6 address**: Check if RFC 1918 private (skip external enrichment), validate format\n- **Domain\u002FFQDN**: Defang for safe handling (`evil[.]com`), extract registered domain via tldextract\n- **URL**: Extract domain + path separately; check for redirectors\n- **File hash**: Identify hash type (MD5\u002FSHA-1\u002FSHA-256); prefer SHA-256 for uniqueness\n- **Email address**: Split into domain (check MX\u002FDMARC) and local part for pattern analysis\n\nDefang IOCs in documentation (replace `.` with `[.]` and `:\u002F\u002F` with `[:\u002F\u002F]`) to prevent accidental clicks.\n\n### Step 2: Multi-Source Enrichment\n\n**VirusTotal (file hash, URL, IP, domain)**:\n```python\nimport vt\n\nclient = vt.Client(\"YOUR_VT_API_KEY\")\n\n# File hash lookup\nfile_obj = client.get_object(f\"\u002Ffiles\u002F{sha256_hash}\")\ndetections = file_obj.last_analysis_stats\nprint(f\"Malicious: {detections['malicious']}\u002F{sum(detections.values())}\")\n\n# Domain analysis\ndomain_obj = client.get_object(f\"\u002Fdomains\u002F{domain}\")\nprint(domain_obj.last_analysis_stats)\nprint(domain_obj.reputation)\nclient.close()\n```\n\n**AbuseIPDB (IP addresses)**:\n```python\nimport requests\n\nresponse = requests.get(\n    \"https:\u002F\u002Fapi.abuseipdb.com\u002Fapi\u002Fv2\u002Fcheck\",\n    headers={\"Key\": \"YOUR_KEY\", \"Accept\": \"application\u002Fjson\"},\n    params={\"ipAddress\": \"1.2.3.4\", \"maxAgeInDays\": 90}\n)\ndata = response.json()[\"data\"]\nprint(f\"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}\")\n```\n\n**MalwareBazaar (file hashes)**:\n```python\nresponse = requests.post(\n    \"https:\u002F\u002Fmb-api.abuse.ch\u002Fapi\u002Fv1\u002F\",\n    data={\"query\": \"get_info\", \"hash\": sha256_hash}\n)\nresult = response.json()\nif result[\"query_status\"] == \"ok\":\n    print(result[\"data\"][0][\"tags\"], result[\"data\"][0][\"signature\"])\n```\n\n### Step 3: Contextualize with Campaign Attribution\n\nQuery MISP for existing events matching the IOC:\n```python\nfrom pymisp import PyMISP\n\nmisp = PyMISP(\"https:\u002F\u002Fmisp.example.com\", \"API_KEY\")\nresults = misp.search(value=\"evil-domain.com\", type_attribute=\"domain\")\nfor event in results:\n    print(event[\"Event\"][\"info\"], event[\"Event\"][\"threat_level_id\"])\n```\n\nCheck Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).\n\n### Step 4: Assign Confidence Score and Disposition\n\nApply a tiered decision framework:\n- **Block (High Confidence ≥ 70%)**: ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign\n- **Monitor\u002FAlert (Medium 40–69%)**: 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution\n- **Whitelist\u002FInvestigate (Low \u003C40%)**: ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)\n- **False Positive**: Legitimate business service incorrectly flagged; document and exclude from future alerts\n\n### Step 5: Document and Distribute\n\nRecord findings in TIP\u002FMISP with:\n- All enrichment data collected (timestamps, source, score)\n- Disposition decision and rationale\n- Blocking actions taken (firewall, proxy, DNS sinkhole)\n- Related incident ticket number\n\nExport to STIX indicator object with confidence field set appropriately.\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **IOC** | Indicator of Compromise — observable network or host artifact indicating potential compromise |\n| **Enrichment** | Process of adding contextual data to a raw IOC from multiple intelligence sources |\n| **Defanging** | Modifying IOCs (replacing `.` with `[.]`) to prevent accidental activation in documentation |\n| **False Positive Rate** | Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds |\n| **Sinkhole** | DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely |\n| **TTL** | Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days |\n\n## Tools & Systems\n\n- **VirusTotal**: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments\n- **AbuseIPDB**: Community-maintained IP reputation database with 90-day abuse report history\n- **MalwareBazaar (abuse.ch)**: Free malware hash repository with YARA rule associations and malware family tagging\n- **URLScan.io**: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage\n- **Shodan**: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment\n\n## Common Pitfalls\n\n- **Blocking shared infrastructure**: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.\n- **VT score obsession**: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.\n- **Missing defanging**: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.\n- **No expiration policy**: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.\n- **Over-relying on single source**: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.\n","# Analyzing Indicators of Compromise\n\n## When to Use\n\nUse this skill when:\n- A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage\n- Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls\n- An incident investigation requires contextual enrichment of observed network artifacts\n\n**Do not use** this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).\n\n## Prerequisites\n\n- VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup\n- AbuseIPDB API key for IP reputation checks\n- MISP instance or TIP for cross-referencing against known campaigns\n- Python with `requests` and `vt-py` libraries, or SOAR platform with pre-built connectors\n\n## Workflow\n\n### Step 1: Normalize and Classify IOC Types\n\nBefore enriching, classify each IOC:\n- **IPv4\u002FIPv6 address**: Check if RFC 1918 private (skip external enrichment), validate format\n- **Domain\u002FFQDN**: Defang for safe handling (`evil[.]com`), extract registered domain via tldextract\n- **URL**: Extract domain + path separately; check for redirectors\n- **File hash**: Identify hash type (MD5\u002FSHA-1\u002FSHA-256); prefer SHA-256 for uniqueness\n- **Email address**: Split into domain (check MX\u002FDMARC) and local part for pattern analysis\n\nDefang IOCs in documentation (replace `.` with `[.]` and `:\u002F\u002F` with `[:\u002F\u002F]`) to prevent accidental clicks.\n\n### Step 2: Multi-Source Enrichment\n\n**VirusTotal (file hash, URL, IP, domain)**:\n```python\nimport vt\n\nclient = vt.Client(\"YOUR_VT_API_KEY\")\n\n# File hash lookup\nfile_obj = client.get_object(f\"\u002Ffiles\u002F{sha256_hash}\")\ndetections = file_obj.last_analysis_stats\nprint(f\"Malicious: {detections['malicious']}\u002F{sum(detections.values())}\")\n\n# Domain analysis\ndomain_obj = client.get_object(f\"\u002Fdomains\u002F{domain}\")\nprint(domain_obj.last_analysis_stats)\nprint(domain_obj.reputation)\nclient.close()\n```\n\n**AbuseIPDB (IP addresses)**:\n```python\nimport requests\n\nresponse = requests.get(\n    \"https:\u002F\u002Fapi.abuseipdb.com\u002Fapi\u002Fv2\u002Fcheck\",\n    headers={\"Key\": \"YOUR_KEY\", \"Accept\": \"application\u002Fjson\"},\n    params={\"ipAddress\": \"1.2.3.4\", \"maxAgeInDays\": 90}\n)\ndata = response.json()[\"data\"]\nprint(f\"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}\")\n```\n\n**MalwareBazaar (file hashes)**:\n```python\nresponse = requests.post(\n    \"https:\u002F\u002Fmb-api.abuse.ch\u002Fapi\u002Fv1\u002F\",\n    data={\"query\": \"get_info\", \"hash\": sha256_hash}\n)\nresult = response.json()\nif result[\"query_status\"] == \"ok\":\n    print(result[\"data\"][0][\"tags\"], result[\"data\"][0][\"signature\"])\n```\n\n### Step 3: Contextualize with Campaign Attribution\n\nQuery MISP for existing events matching the IOC:\n```python\nfrom pymisp import PyMISP\n\nmisp = PyMISP(\"https:\u002F\u002Fmisp.example.com\", \"API_KEY\")\nresults = misp.search(value=\"evil-domain.com\", type_attribute=\"domain\")\nfor event in results:\n    print(event[\"Event\"][\"info\"], event[\"Event\"][\"threat_level_id\"])\n```\n\nCheck Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).\n\n### Step 4: Assign Confidence Score and Disposition\n\nApply a tiered decision framework:\n- **Block (High Confidence ≥ 70%)**: ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign\n- **Monitor\u002FAlert (Medium 40–69%)**: 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution\n- **Whitelist\u002FInvestigate (Low \u003C40%)**: ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)\n- **False Positive**: Legitimate business service incorrectly flagged; document and exclude from future alerts\n\n### Step 5: Document and Distribute\n\nRecord findings in TIP\u002FMISP with:\n- All enrichment data collected (timestamps, source, score)\n- Disposition decision and rationale\n- Blocking actions taken (firewall, proxy, DNS sinkhole)\n- Related incident ticket number\n\nExport to STIX indicator object with confidence field set appropriately.\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **IOC** | Indicator of Compromise — observable network or host artifact indicating potential compromise |\n| **Enrichment** | Process of adding contextual data to a raw IOC from multiple intelligence sources |\n| **Defanging** | Modifying IOCs (replacing `.` with `[.]`) to prevent accidental activation in documentation |\n| **False Positive Rate** | Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds |\n| **Sinkhole** | DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely |\n| **TTL** | Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days |\n\n## Tools & Systems\n\n- **VirusTotal**: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments\n- **AbuseIPDB**: Community-maintained IP reputation database with 90-day abuse report history\n- **MalwareBazaar (abuse.ch)**: Free malware hash repository with YARA rule associations and malware family tagging\n- **URLScan.io**: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage\n- **Shodan**: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment\n\n## Common Pitfalls\n\n- **Blocking shared infrastructure**: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.\n- **VT score obsession**: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.\n- **Missing defanging**: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.\n- **No expiration policy**: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.\n- **Over-relying on single source**: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.\n",{"name":5,"tags":49,"author":52,"domain":34,"license":15,"version":53,"mitre_f3":54,"nist_csf":75,"subdomain":35,"description":80,"mitre_attack":81,"atlas_techniques":86},[50,29,28,27,26,35,51,30],"IOC","STIX","mahipal","1.0.0",{"tactics":55,"version":59,"techniques":60},[56,57,58],"reconnaissance","resource-development","initial-access","1.1",[61,65,68,71],{"id":62,"name":63,"source":64,"tactic":56},"T1598","Phishing for Information","attack",{"id":66,"name":67,"source":64,"tactic":58},"T1660","Phishing",{"id":69,"name":70,"source":64,"tactic":57},"T1583.001","Acquire Infrastructure: Domains",{"id":72,"name":73,"source":74,"tactic":57},"F1020.002","Create Fake Materials: Fake Website","f3",[76,77,78,79],"ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02","Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block\u002Fmonitor\u002Fwhitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.\n",[82,83,84,85],"T1071","T1105","T1041","T1567",[87],"AML.T0052","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fanalyzing-indicators-of-compromise",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":99,"prompt":109,"zipAvailable":110},[100,103,106],{"tool":101,"command":102},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise",{"tool":104,"command":105},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise",{"tool":107,"command":108},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002FSKILL.md",true,[112,117,123],{"path":113,"resourceType":113,"resourceTypeDesc":114,"sizeBytes":115,"sourceHtmlUrl":116},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002FLICENSE",{"path":118,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":121,"sourceHtmlUrl":122},"references\u002Fapi-reference.md","REFERENCE","参考文档",2926,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002Freferences\u002Fapi-reference.md",{"path":124,"resourceType":125,"resourceTypeDesc":126,"sizeBytes":127,"sourceHtmlUrl":128},"scripts\u002Fagent.py","SCRIPT","脚本",8985,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-indicators-of-compromise\u002Fscripts\u002Fagent.py",[130,156,180,208,234,261],{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":137,"publishedAt":44},"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",[136],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[138,139,140,141,142,34,143,144,145,146,38,147,148,149,150,151,152,153,154,155],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","identity","pr","code","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":163,"publishedAt":44},"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",[162],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[164,152,165,166,142,167,34,145,168,169,170,171,172,173,174,175,176,177,178,179],"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":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":190,"publishedAt":44},"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",[186,189],{"categoryNo":187,"categoryName":188,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[174,191,192,193,194,34,195,196,168,197,198,199,200,201,202,203,204,205,206,207],"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":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":212,"categories":213,"keywords":215,"publishedAt":44},"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",[214],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[216,217,218,34,219,220,221,222,223,224,225,226,227,228,229,230,171,231,232,233],"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":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":238,"categories":239,"keywords":241,"publishedAt":44},"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",[240],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[242,243,244,34,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260],"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":262,"name":263,"nameZh":6,"description":264,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":265,"categories":266,"keywords":268,"publishedAt":44},"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",[267],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[142,269,34,270,37,25,271,272,273,274,275,203,276,204,277,278,279,280,281,282],"triaging security incident","siem","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]