[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AEHVGAE2F8M31MHDC1Y0Z6":3,"skill-same:01M6AEHVGAE2F8M31MHDC1Y0Z6":103},{"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":62,"commitSha":63,"skillDirectory":64,"forksCount":65,"repositoryHtmlUrl":66,"repositoryHomepageUrl":67,"ownerHtmlUrl":68,"ownerBlogUrl":69,"ownerDescription":70,"updatedAt":71,"install":72,"resources":85},"01M6AEHVGAE2F8M31MHDC1Y0Z6","detecting-shadow-it-cloud-usage",null,"Detect unauthorized SaaS and cloud service usage (shadow IT) by parsing proxy access logs, DNS query logs, and firewall\u002Fnetflow data with Python pandas to aggregate traffic by domain, classify domains against known SaaS categories, and score risk by data volume and user count. Use when auditing an organization for unsanctioned cloud\u002FSaaS usage or generating a shadow IT discovery report with remediation recommendations.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",35990,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-shadow-it-cloud-usage\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","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],"detecting shadow it cloud usage","cloud","data","DNS-analysis","proxy-logs","SaaS-discovery","shadow-IT","t1078","firewall","remediation","netflow","python","dns","cloud-security","logs","proxy","pandas","report","pr","cybersecurity","2026-11-10 10:12:30",false,"---\nname: detecting-shadow-it-cloud-usage\ndescription: Detect unauthorized SaaS and cloud service usage (shadow IT) by parsing\n  proxy access logs, DNS query logs, and firewall\u002Fnetflow data with Python pandas\n  to aggregate traffic by domain, classify domains against known SaaS categories,\n  and score risk by data volume and user count. Use when auditing an organization\n  for unsanctioned cloud\u002FSaaS usage or generating a shadow IT discovery report\n  with remediation recommendations.\ndomain: cybersecurity\nsubdomain: cloud-security\ntags:\n- shadow-IT\n- SaaS-discovery\n- proxy-logs\n- DNS-analysis\n- netflow\n- cloud-security\n- pandas\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- PR.IR-01\n- ID.AM-08\n- GV.SC-06\n- DE.CM-01\nmitre_attack:\n- T1567.002\n- T1526\n- T1078.004\n- T1213\n---\n\n# Detecting Shadow IT Cloud Usage\n\n## Overview\n\nShadow IT refers to unauthorized SaaS applications and cloud services used without IT approval. This skill analyzes proxy logs, DNS query logs, and firewall\u002Fnetflow data to identify unauthorized cloud service usage, classify discovered domains against known SaaS categories, measure data transfer volumes, and flag high-risk services based on security posture and compliance requirements.\n\n\n## When to Use\n\n- When investigating security incidents that require detecting shadow it cloud usage\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Python 3.9+ with `pandas`, `tldextract`\n- Proxy logs (Squid, Zscaler, or Palo Alto format) or DNS query logs\n- SaaS application catalog\u002Fblocklist for classification\n- Network firewall logs with FQDN resolution (optional)\n\n## Steps\n\n1. Parse proxy access logs and extract destination domains with traffic volumes\n2. Parse DNS query logs to identify resolved cloud service domains\n3. Aggregate traffic by domain using pandas — total bytes, request counts, unique users\n4. Classify domains against known SaaS categories (storage, email, dev tools, AI)\n5. Flag unauthorized services not on the approved application list\n6. Calculate risk scores based on data volume, user count, and service category\n7. Generate shadow IT discovery report with remediation recommendations\n\n## Expected Output\n\n- JSON report listing discovered cloud services with traffic volumes, user counts, risk scores, and approval status\n- Top unauthorized services ranked by data exfiltration risk\n","# Detecting Shadow IT Cloud Usage\n\n## Overview\n\nShadow IT refers to unauthorized SaaS applications and cloud services used without IT approval. This skill analyzes proxy logs, DNS query logs, and firewall\u002Fnetflow data to identify unauthorized cloud service usage, classify discovered domains against known SaaS categories, measure data transfer volumes, and flag high-risk services based on security posture and compliance requirements.\n\n\n## When to Use\n\n- When investigating security incidents that require detecting shadow it cloud usage\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Python 3.9+ with `pandas`, `tldextract`\n- Proxy logs (Squid, Zscaler, or Palo Alto format) or DNS query logs\n- SaaS application catalog\u002Fblocklist for classification\n- Network firewall logs with FQDN resolution (optional)\n\n## Steps\n\n1. Parse proxy access logs and extract destination domains with traffic volumes\n2. Parse DNS query logs to identify resolved cloud service domains\n3. Aggregate traffic by domain using pandas — total bytes, request counts, unique users\n4. Classify domains against known SaaS categories (storage, email, dev tools, AI)\n5. Flag unauthorized services not on the approved application list\n6. Calculate risk scores based on data volume, user count, and service category\n7. Generate shadow IT discovery report with remediation recommendations\n\n## Expected Output\n\n- JSON report listing discovered cloud services with traffic volumes, user counts, risk scores, and approval status\n- Top unauthorized services ranked by data exfiltration risk\n",{"name":5,"tags":49,"author":50,"domain":43,"license":15,"version":51,"nist_csf":52,"subdomain":37,"description":7,"mitre_attack":57},[30,29,28,27,34,37,40],"mahipal","1.0",[53,54,55,56],"PR.IR-01","ID.AM-08","GV.SC-06","DE.CM-01",[58,59,60,61],"T1567.002","T1526","T1078.004","T1213","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fdetecting-shadow-it-cloud-usage",4395,"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":73,"prompt":83,"zipAvailable":84},[74,77,80],{"tool":75,"command":76},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill detecting-shadow-it-cloud-usage",{"tool":78,"command":79},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill detecting-shadow-it-cloud-usage",{"tool":81,"command":82},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill detecting-shadow-it-cloud-usage","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-shadow-it-cloud-usage\u002FSKILL.md",true,[86,91,97],{"path":87,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-shadow-it-cloud-usage\u002FLICENSE",{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fapi-reference.md","REFERENCE","参考文档",2399,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-shadow-it-cloud-usage\u002Freferences\u002Fapi-reference.md",{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"scripts\u002Fagent.py","SCRIPT","脚本",11060,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-shadow-it-cloud-usage\u002Fscripts\u002Fagent.py",[104,132,156,183,207,233],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":113,"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",[110],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[114,115,116,117,118,43,119,120,42,121,122,123,124,125,126,127,128,129,130,131],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","identity","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":139,"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",[138],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},[140,128,141,142,118,143,43,42,144,145,146,147,148,149,150,151,152,153,154,155],"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":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":166,"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",[162,165],{"categoryNo":163,"categoryName":164,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},[150,167,168,169,170,43,171,172,144,173,174,175,176,177,178,31,179,180,181,182],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":187,"categories":188,"keywords":190,"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",[189],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},[191,192,193,43,194,195,38,196,197,198,199,26,200,201,202,203,147,204,205,206],"windows","kape","triaging windows with kape","registry","browser","windows-forensics","dfir","mft","mftecmd","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":214,"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",[213],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},[215,216,217,43,218,219,220,221,222,223,224,225,226,33,227,228,229,230,231,232],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":234,"name":235,"nameZh":6,"description":236,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":237,"categories":238,"keywords":240,"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",[239],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},[118,241,43,242,243,244,245,246,247,248,249,31,250,179,251,252,253,254,255,256],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]