[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73C4MYGFAENTQM52JB0CS3X":3,"skill-same:01M73C4MYGFAENTQM52JB0CS3X":90},{"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":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M73C4MYGFAENTQM52JB0CS3X","hunting-credential-stuffing-attacks",null,"Detects credential stuffing attacks by analyzing authentication logs for login velocity anomalies, ASN diversity, password spray patterns, and geographic distribution of failed logins. Uses statistical analysis on Splunk or raw log data. Use when investigating account takeover campaigns or building detection rules for auth abuse.","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\u002Fhunting-credential-stuffing-attacks\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fhunting-credential-stuffing-attacks\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: hunting-credential-stuffing-attacks\ndescription: 'Detects credential stuffing attacks by analyzing authentication logs\n  for login velocity anomalies, ASN diversity, password spray patterns, and geographic\n  distribution of failed logins. Uses statistical analysis on Splunk or raw log data.\n  Use when investigating account takeover campaigns or building detection rules for\n  auth abuse.\n\n  '\ndomain: cybersecurity\nsubdomain: security-operations\ntags:\n- credential-stuffing\n- authentication-logs\n- login-anomaly\n- asn-analysis\n- threat-hunting\n- account-takeover\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- DE.CM-01\n- RS.MA-01\n- GV.OV-01\n- DE.AE-02\nmitre_attack:\n- T1078\n- T1190\n- T1059\n- T1003\n- T1110\nsource: https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\nsource_commit: 04450304b12645cb2b974ab96d28c0664758a88d\n---\n\n# Hunting Credential Stuffing Attacks\n\n\n## When to Use\n\n- When investigating security incidents that require hunting credential stuffing attacks\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- Familiarity with security operations concepts and tools\n- Access to a test or lab environment for safe execution\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization for any testing activities\n\n## Instructions\n\nAnalyze authentication logs to detect credential stuffing by identifying patterns\nof distributed login failures, high IP diversity, and suspicious ASN distribution.\n\n```python\nimport pandas as pd\nfrom collections import Counter\n\n# Load auth logs\ndf = pd.read_csv(\"auth_logs.csv\", parse_dates=[\"timestamp\"])\n\n# Credential stuffing indicator: many IPs trying few accounts\nip_per_account = df[df[\"status\"] == \"failed\"].groupby(\"username\")[\"source_ip\"].nunique()\naccounts_under_attack = ip_per_account[ip_per_account > 50]\n```\n\nKey detection indicators:\n1. High unique source IPs per failed username\n2. Low success rate across many accounts (\u003C 1%)\n3. ASN concentration from cloud\u002Fproxy providers\n4. Geographic impossibility (same account, distant locations)\n5. User-agent uniformity across distributed IPs\n\n## Examples\n\n```python\n# Password spray: one password tried across many accounts\nspray = df[df[\"status\"] == \"failed\"].groupby([\"source_ip\", \"password_hash\"]).agg(\n    accounts=(\"username\", \"nunique\")).reset_index()\nsprays = spray[spray[\"accounts\"] > 10]\n```\n","# Hunting Credential Stuffing Attacks\n\n\n## When to Use\n\n- When investigating security incidents that require hunting credential stuffing attacks\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- Familiarity with security operations concepts and tools\n- Access to a test or lab environment for safe execution\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization for any testing activities\n\n## Instructions\n\nAnalyze authentication logs to detect credential stuffing by identifying patterns\nof distributed login failures, high IP diversity, and suspicious ASN distribution.\n\n```python\nimport pandas as pd\nfrom collections import Counter\n\n# Load auth logs\ndf = pd.read_csv(\"auth_logs.csv\", parse_dates=[\"timestamp\"])\n\n# Credential stuffing indicator: many IPs trying few accounts\nip_per_account = df[df[\"status\"] == \"failed\"].groupby(\"username\")[\"source_ip\"].nunique()\naccounts_under_attack = ip_per_account[ip_per_account > 50]\n```\n\nKey detection indicators:\n1. High unique source IPs per failed username\n2. Low success rate across many accounts (\u003C 1%)\n3. ASN concentration from cloud\u002Fproxy providers\n4. Geographic impossibility (same account, distant locations)\n5. User-agent uniformity across distributed IPs\n\n## Examples\n\n```python\n# Password spray: one password tried across many accounts\nspray = df[df[\"status\"] == \"failed\"].groupby([\"source_ip\", \"password_hash\"]).agg(\n    accounts=(\"username\", \"nunique\")).reset_index()\nsprays = spray[spray[\"accounts\"] > 10]\n```\n",{"name":5,"tags":25,"author":32,"domain":33,"source":34,"license":15,"version":35,"nist_csf":36,"subdomain":41,"description":42,"mitre_attack":43,"source_commit":49},[26,27,28,29,30,31],"credential-stuffing","authentication-logs","login-anomaly","asn-analysis","threat-hunting","account-takeover","mahipal","cybersecurity","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","1.0",[37,38,39,40],"DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02","security-operations","Detects credential stuffing attacks by analyzing authentication logs for login velocity anomalies, ASN diversity, password spray patterns, and geographic distribution of failed logins. Uses statistical analysis on Splunk or raw log data. Use when investigating account takeover campaigns or building detection rules for auth abuse.\n",[44,45,46,47,48],"T1078","T1190","T1059","T1003","T1110","04450304b12645cb2b974ab96d28c0664758a88d","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fhunting-credential-stuffing-attacks",2,"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.","2026-11-14 13:24:09",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill hunting-credential-stuffing-attacks",{"tool":66,"command":67},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill hunting-credential-stuffing-attacks",{"tool":69,"command":70},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill hunting-credential-stuffing-attacks","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fhunting-credential-stuffing-attacks\u002FSKILL.md",true,[74,78,84],{"path":75,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":16},"LICENSE","许可证",11255,{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fapi-reference.md","REFERENCE","参考文档",1479,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fhunting-credential-stuffing-attacks\u002Freferences\u002Fapi-reference.md",{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"scripts\u002Fagent.py","SCRIPT","脚本",7571,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fhunting-credential-stuffing-attacks\u002Fscripts\u002Fagent.py",[91,99,106,114,122,130],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"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":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"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":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":110,"sourceHtmlUrl":111,"categories":112,"keywords":113,"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":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":118,"sourceHtmlUrl":119,"categories":120,"keywords":121,"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":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":126,"sourceHtmlUrl":127,"categories":128,"keywords":129,"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":131,"name":132,"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":133,"categories":134,"keywords":135,"publishedAt":20},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]