[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73F00Z8SP5DDBGPS3GSFHA5":3,"skill-same:01M73F00Z8SP5DDBGPS3GSFHA5":89},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":19,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":23,"frontMatter":24,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M73F00Z8SP5DDBGPS3GSFHA5","performing-ssl-tls-security-assessment",null,"Assess SSL\u002FTLS server configurations using the sslyze Python library to evaluate cipher suites, certificate chains, protocol versions, HSTS headers, and known vulnerabilities like Heartbleed and ROBOT.","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-ssl-tls-security-assessment\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-ssl-tls-security-assessment\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: performing-ssl-tls-security-assessment\ndescription: Assess SSL\u002FTLS server configurations using the sslyze Python library\n  to evaluate cipher suites, certificate chains, protocol versions, HSTS headers,\n  and known vulnerabilities like Heartbleed and ROBOT.\ndomain: cybersecurity\nsubdomain: network-security\ntags:\n- network-security\n- ssl\n- tls\n- sslyze\n- certificate\n- cipher-suites\n- vulnerability-assessment\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- PR.IR-01\n- DE.CM-01\n- ID.AM-03\n- PR.DS-02\nmitre_attack:\n- T1046\n- T1040\n- T1557\n- T1071\n- T1553\nsource: https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\nsource_commit: 04450304b12645cb2b974ab96d28c0664758a88d\n---\n# Performing SSL\u002FTLS Security Assessment\n\n## Overview\n\nAssess SSL\u002FTLS server configurations using sslyze, a fast Python-based scanning library. This skill covers evaluating supported protocol versions (SSLv2\u002F3, TLS 1.0-1.3), cipher suite strength, certificate chain validation, HSTS enforcement, OCSP stapling, and scanning for known vulnerabilities including Heartbleed, ROBOT, and session renegotiation weaknesses.\n\n\n## When to Use\n\n- When conducting security assessments that involve performing ssl tls security assessment\n- When following incident response procedures for related security events\n- When performing scheduled security testing or auditing activities\n- When validating security controls through hands-on testing\n\n## Prerequisites\n\n- Python 3.9+ with `sslyze` library (pip install sslyze)\n- Network access to target HTTPS servers on port 443\n- Understanding of TLS protocol versions and cipher suite classifications\n\n## Steps\n\n### Step 1: Configure Server Scan\nCreate ServerScanRequest with ServerNetworkLocation specifying target hostname and port.\n\n### Step 2: Execute TLS Scan\nUse sslyze Scanner to queue and execute scans for all TLS check commands concurrently.\n\n### Step 3: Analyze Results\nEvaluate accepted cipher suites, certificate validity, protocol versions, and vulnerability scan results.\n\n### Step 4: Generate Security Report\nProduce a JSON report with compliance findings and remediation recommendations.\n\n## Expected Output\n\nJSON report with supported protocols, accepted cipher suites, certificate details, vulnerability results (Heartbleed, ROBOT), and HSTS status.\n","# Performing SSL\u002FTLS Security Assessment\n\n## Overview\n\nAssess SSL\u002FTLS server configurations using sslyze, a fast Python-based scanning library. This skill covers evaluating supported protocol versions (SSLv2\u002F3, TLS 1.0-1.3), cipher suite strength, certificate chain validation, HSTS enforcement, OCSP stapling, and scanning for known vulnerabilities including Heartbleed, ROBOT, and session renegotiation weaknesses.\n\n\n## When to Use\n\n- When conducting security assessments that involve performing ssl tls security assessment\n- When following incident response procedures for related security events\n- When performing scheduled security testing or auditing activities\n- When validating security controls through hands-on testing\n\n## Prerequisites\n\n- Python 3.9+ with `sslyze` library (pip install sslyze)\n- Network access to target HTTPS servers on port 443\n- Understanding of TLS protocol versions and cipher suite classifications\n\n## Steps\n\n### Step 1: Configure Server Scan\nCreate ServerScanRequest with ServerNetworkLocation specifying target hostname and port.\n\n### Step 2: Execute TLS Scan\nUse sslyze Scanner to queue and execute scans for all TLS check commands concurrently.\n\n### Step 3: Analyze Results\nEvaluate accepted cipher suites, certificate validity, protocol versions, and vulnerability scan results.\n\n### Step 4: Generate Security Report\nProduce a JSON report with compliance findings and remediation recommendations.\n\n## Expected Output\n\nJSON report with supported protocols, accepted cipher suites, certificate details, vulnerability results (Heartbleed, ROBOT), and HSTS status.\n",{"name":5,"tags":25,"author":33,"domain":34,"source":35,"license":15,"version":36,"nist_csf":37,"subdomain":26,"description":7,"mitre_attack":42,"source_commit":48},[26,27,28,29,30,31,32],"network-security","ssl","tls","sslyze","certificate","cipher-suites","vulnerability-assessment","mahipal","cybersecurity","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","1.0",[38,39,40,41],"PR.IR-01","DE.CM-01","ID.AM-03","PR.DS-02",[43,44,45,46,47],"T1046","T1040","T1557","T1071","T1553","04450304b12645cb2b974ab96d28c0664758a88d","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fperforming-ssl-tls-security-assessment",1,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-11 23:56:26",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill performing-ssl-tls-security-assessment",{"tool":65,"command":66},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill performing-ssl-tls-security-assessment",{"tool":68,"command":69},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill performing-ssl-tls-security-assessment","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-ssl-tls-security-assessment\u002FSKILL.md",true,[73,77,83],{"path":74,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":16},"LICENSE","许可证",11255,{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fapi-reference.md","REFERENCE","参考文档",1917,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-ssl-tls-security-assessment\u002Freferences\u002Fapi-reference.md",{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"scripts\u002Fagent.py","SCRIPT","脚本",7908,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-ssl-tls-security-assessment\u002Fscripts\u002Fagent.py",[90,98,105,113,121,129],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":94,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":20},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":20},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":109,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":20},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":117,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":20},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":125,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":20},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":130,"name":131,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":20},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]