[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71A6WN4GZNMRAZMXS7G3M5P":3,"skill-same:01M71A6WN4GZNMRAZMXS7G3M5P":68},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":53,"updatedAt":54,"install":55,"resources":67},"01M71A6WN4GZNMRAZMXS7G3M5P","cyber-exploiting-active-directory-with-bloodhound",null,"BloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attac","nexuslinkproductions\u002Fyuri-os","nexuslinkproductions","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F273246263?v=4","01M718YX9NZRCW50B59B8ZVDKN","Marcel Spatz","01M718YXAQ7530F4B1VKN6SFQV",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002F.claude\u002Fskills-labgated\u002Fcyber-exploiting-active-directory-with-bloodhound\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"active","directory","exploiting active directory with bloodhound","bloodhound","with","cyber exploiting active directory with bloodhound","active-directory","domain","post-exploitation","red-team","graph","environments","reveal","subdomain","simulation","mitre-attack","team","cybersecurity","adversary-simulation","Graph Theory","2026-11-10 10:12:30",false,"---\nname: cyber-exploiting-active-directory-with-bloodhound\ndescription: \"BloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attac\"\nsource: mukul975\u002FAnthropic-Cybersecurity-Skills\nlicense: Apache-2.0\nauthorized_lab: true\norigin_frontmatter: \"name: exploiting-active-directory-with-bloodhound | description: BloodHound is a graph-based Active Directory reconnaissance tool that |   uses graph theory to reveal hidden and unintended relationships within AD environments. |   Red teams use BloodHound to identify attac | domain: cybersecurity | subdomain: red-teaming | tags: | - red-team | - adversary-simulation | - mitre-attack | - exploitation | - post-exploitation | - active-directory | - bloodhound | version: '1.0' | author: mahipal | li\"\nhide: true\n---\n\n> AUTHORIZED-LAB ONLY. Offensive\u002Fdual-use capability. Use exclusively against systems you own or have explicit written authorization to test. This skill is gated out of the default discovery path; activation requires an explicit authorized-engagement flag.\n\n# Exploiting Active Directory with BloodHound\n\n\n> **Legal Notice:** This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.\n\n## Overview\n\nBloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attack paths from compromised accounts to high-value targets such as Domain Admins, identifying privilege escalation chains that would be nearly impossible to find manually. SharpHound is the official data collector that gathers AD objects, relationships, ACLs, sessions, and group memberships.\n\n\n## When to Use\n\n- When performing authorized security testing that involves exploiting active directory with bloodhound\n- When analyzing malware samples or attack artifacts in a controlled environment\n- When conducting red team exercises or penetration testing engagements\n- When building detection capabilities based on offensive technique understanding\n\n## Prerequisites\n\n- Familiarity with red teaming 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## Objectives\n\n- Collect Active Directory relationship data using SharpHound or BloodHound.py\n- Visualize attack paths from compromised accounts to Domain Admin\n- Identify misconfigured ACLs, group memberships, and delegation settings\n- Discover shortest attack paths to high-value targets\n- Map Kerberos delegation configurations for abuse\n- Document all identified privilege escalation chains\n\n## MITRE ATT&CK Mapping\n\n- **T1087.002** - Account Discovery: Domain Account\n- **T1069.002** - Permission Groups Discovery: Domain Groups\n- **T1482** - Domain Trust Discovery\n- **T1615** - Group Policy Discovery\n- **T1018** - Remote System Discovery\n- **T1033** - System Owner\u002FUser Discovery\n- **T1016** - System Network Configuration Discovery\n\n## Workflow\n\n### Phase 1: Data Collection with SharpHound\n1. Transfer SharpHound collector to compromised host\n2. Execute collection with appropriate method (All, DCOnly, Session, LoggedOn)\n3. Collect from all reachable domains if multi-domain environment\n4. Exfiltrate ZIP data files to analysis workstation\n5. Import data into BloodHound CE or Legacy\n\n### Phase 2: Attack Path Analysis\n1. Mark owned principals (compromised accounts)\n2. Query shortest path to Domain Admins\n3. Identify Kerberoastable accounts with admin privileges\n4. Find AS-REP Roastable accounts\n5. Analyze ACL-based attack paths (GenericAll, GenericWrite, WriteDACL, ForceChangePassword)\n6. Review GPO abuse opportunities\n\n### Phase 3: Exploitation Planning\n1. Prioritize attack paths by complexity and stealth\n2. Identify required tools for each step in the chain\n3. Plan OPSEC considerations for each technique\n4. Execute identified attack chain\n5. Document evidence at each step\n\n## Tools and Resources\n\n| Tool | Purpose | Platform |\n|------|---------|----------|\n| BloodHound CE | Graph visualization and analysis | Web-based |\n| SharpHound | AD data collection (.NET) | Windows |\n| BloodHound.py | AD data collection (Python) | Linux\u002FWindows |\n| Cypher queries | Custom graph queries | Neo4j\u002FBloodHound |\n| PlumHound | Automated BloodHound reporting | Python |\n| Max (BloodHound) | BloodHound automation | Python |\n\n## Key BloodHound Queries\n\n| Query | Purpose |\n|-------|---------|\n| Shortest Path to Domain Admins | Find fastest route to DA |\n| Find Kerberoastable Users with Path to DA | SPN accounts leading to DA |\n| Find AS-REP Roastable Users | Accounts without pre-auth |\n| Shortest Path from Owned Principals | Paths from compromised accounts |\n| Find Computers with Unsupported OS | Legacy systems for exploitation |\n| Find Users with DCSync Rights | Accounts that can replicate AD |\n| Find GPOs that Modify Local Group Membership | GPO-based privilege escalation |\n\n## Validation Criteria\n\n- [ ] SharpHound data collected from all domains\n- [ ] Attack paths identified from owned accounts to DA\n- [ ] ACL-based attack paths documented\n- [ ] Kerberoastable and AS-REP roastable accounts identified\n- [ ] Exploitation plan created with prioritized paths\n- [ ] Evidence screenshots captured for report\n","> AUTHORIZED-LAB ONLY. Offensive\u002Fdual-use capability. Use exclusively against systems you own or have explicit written authorization to test. This skill is gated out of the default discovery path; activation requires an explicit authorized-engagement flag.\n\n# Exploiting Active Directory with BloodHound\n\n\n> **Legal Notice:** This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.\n\n## Overview\n\nBloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attack paths from compromised accounts to high-value targets such as Domain Admins, identifying privilege escalation chains that would be nearly impossible to find manually. SharpHound is the official data collector that gathers AD objects, relationships, ACLs, sessions, and group memberships.\n\n\n## When to Use\n\n- When performing authorized security testing that involves exploiting active directory with bloodhound\n- When analyzing malware samples or attack artifacts in a controlled environment\n- When conducting red team exercises or penetration testing engagements\n- When building detection capabilities based on offensive technique understanding\n\n## Prerequisites\n\n- Familiarity with red teaming 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## Objectives\n\n- Collect Active Directory relationship data using SharpHound or BloodHound.py\n- Visualize attack paths from compromised accounts to Domain Admin\n- Identify misconfigured ACLs, group memberships, and delegation settings\n- Discover shortest attack paths to high-value targets\n- Map Kerberos delegation configurations for abuse\n- Document all identified privilege escalation chains\n\n## MITRE ATT&CK Mapping\n\n- **T1087.002** - Account Discovery: Domain Account\n- **T1069.002** - Permission Groups Discovery: Domain Groups\n- **T1482** - Domain Trust Discovery\n- **T1615** - Group Policy Discovery\n- **T1018** - Remote System Discovery\n- **T1033** - System Owner\u002FUser Discovery\n- **T1016** - System Network Configuration Discovery\n\n## Workflow\n\n### Phase 1: Data Collection with SharpHound\n1. Transfer SharpHound collector to compromised host\n2. Execute collection with appropriate method (All, DCOnly, Session, LoggedOn)\n3. Collect from all reachable domains if multi-domain environment\n4. Exfiltrate ZIP data files to analysis workstation\n5. Import data into BloodHound CE or Legacy\n\n### Phase 2: Attack Path Analysis\n1. Mark owned principals (compromised accounts)\n2. Query shortest path to Domain Admins\n3. Identify Kerberoastable accounts with admin privileges\n4. Find AS-REP Roastable accounts\n5. Analyze ACL-based attack paths (GenericAll, GenericWrite, WriteDACL, ForceChangePassword)\n6. Review GPO abuse opportunities\n\n### Phase 3: Exploitation Planning\n1. Prioritize attack paths by complexity and stealth\n2. Identify required tools for each step in the chain\n3. Plan OPSEC considerations for each technique\n4. Execute identified attack chain\n5. Document evidence at each step\n\n## Tools and Resources\n\n| Tool | Purpose | Platform |\n|------|---------|----------|\n| BloodHound CE | Graph visualization and analysis | Web-based |\n| SharpHound | AD data collection (.NET) | Windows |\n| BloodHound.py | AD data collection (Python) | Linux\u002FWindows |\n| Cypher queries | Custom graph queries | Neo4j\u002FBloodHound |\n| PlumHound | Automated BloodHound reporting | Python |\n| Max (BloodHound) | BloodHound automation | Python |\n\n## Key BloodHound Queries\n\n| Query | Purpose |\n|-------|---------|\n| Shortest Path to Domain Admins | Find fastest route to DA |\n| Find Kerberoastable Users with Path to DA | SPN accounts leading to DA |\n| Find AS-REP Roastable Users | Accounts without pre-auth |\n| Shortest Path from Owned Principals | Paths from compromised accounts |\n| Find Computers with Unsupported OS | Legacy systems for exploitation |\n| Find Users with DCSync Rights | Accounts that can replicate AD |\n| Find GPOs that Modify Local Group Membership | GPO-based privilege escalation |\n\n## Validation Criteria\n\n- [ ] SharpHound data collected from all domains\n- [ ] Attack paths identified from owned accounts to DA\n- [ ] ACL-based attack paths documented\n- [ ] Kerberoastable and AS-REP roastable accounts identified\n- [ ] Exploitation plan created with prioritized paths\n- [ ] Evidence screenshots captured for report\n",{"hide":44,"name":5,"source":45,"license":15,"description":7,"authorized_lab":44,"origin_frontmatter":46},true,"mukul975\u002FAnthropic-Cybersecurity-Skills","name: exploiting-active-directory-with-bloodhound | description: BloodHound is a graph-based Active Directory reconnaissance tool that |   uses graph theory to reveal hidden and unintended relationships within AD environments. |   Red teams use BloodHound to identify attac | domain: cybersecurity | subdomain: red-teaming | tags: | - red-team | - adversary-simulation | - mitre-attack | - exploitation | - post-exploitation | - active-directory | - bloodhound | version: '1.0' | author: mahipal | li","main","4c6d8d65f3d6096e2fc25b0845e481dda73c2794",".claude\u002Fskills-labgated\u002Fcyber-exploiting-active-directory-with-bloodhound",0,"https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions","Building deterministic AI governance and continuity systems. Workflow architect + visual storyteller","2026-11-11 16:42:59",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":44},[57,60,63],{"tool":58,"command":59},"npx","npx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-exploiting-active-directory-with-bloodhound",{"tool":61,"command":62},"bunx","bunx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-exploiting-active-directory-with-bloodhound",{"tool":64,"command":65},"pnpm","pnpm dlx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-exploiting-active-directory-with-bloodhound","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002F.claude\u002Fskills-labgated\u002Fcyber-exploiting-active-directory-with-bloodhound\u002FSKILL.md",[],[69,92,116,146,175,194],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":39},"01M71CG1AME5WC0A79AE65WT0X","zoom-out","Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.","MIT","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fzoom-out\u002FSKILL.md",[],[77,78,23,79,80,81,82,83,84,85,86,87,88,89,90,91],"zoom out","zoom","harness","how","section","re","true","understand","use","perspective","when","you","code","it","context",{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":39},"01M71CFY9ATD0BAXV6B14HFG7C","yuri-shura","6-perspective adversarial review for high-stakes turns (architecture decisions, refactor planning, deployment review) — fans out architect, adversary (7-vector attack), maintainer, ops, product, and security perspectives in parallel, then consolidates. Use when user says 'review this decision', '\u002Fshura', 'adversarial review', 'what could go wrong', 'architecture review', or 'deployment review'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-shura\u002FSKILL.md",[],[99,100,79,101,102,103,104,105,106,107,108,85,109,87,110,111,112,113,114,115],"yuri shura","parallel","architecture review","planning","for","deployment","vector","strategic review","decision","strategic","ops","workflow","decisions","architecture","review","perspectives","adversarial",{"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":73,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":127,"publishedAt":39},"01M71CFTTYW1HA2MNX38EV1GJ5","yuri-sales-intelligence","Unified sales and outreach skill generating cold outreach openers, social selling sequences, objection rebuttals, and ad creative for any paid platform — multi-persona aware, adapting tone and offer frame to buyer type. Use when user says 'write a cold email', 'outreach sequence', 'objection handling', 'ad copy', or 'sales pitch'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-sales-intelligence\u002FSKILL.md",[122],{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000005","邮件与沟通","SCENARIO","使用场景",[128,129,130,131,132,133,134,135,136,87,137,138,139,140,141,142,143,103,144,145],"intelligence","sales intelligence","yuri sales intelligence","sales","cold","paid","social","persona","pitch","cold email","ad creative","write","sequences","sequence","platform","email","cold outreach","frame",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":160,"publishedAt":39},"01M71CFQMBFTFADJNGP0Z2WCYK","yuri-code-intelligence","Unified code quality intelligence layer that diagnoses code smells (Long Method, Feature Envy, design pattern issues), prescribes targeted Fowler refactoring techniques, and reviews code for security, performance, and clarity. Use when user says 'review this code', 'refactor this', 'what's wrong with this method', 'code smells', or 'clean this up'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-code-intelligence\u002FSKILL.md",[152,155],{"categoryNo":153,"categoryName":154,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":156,"categoryName":157,"taxonomyType":158,"taxonomyTypeDesc":159},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[161,89,162,128,79,163,164,165,103,166,167,168,169,170,171,172,173,174,85,87],"yuri code intelligence","code intelligence","reviews","code quality","feature","performance","code review","long","up","issues","clean","techniques","improve","design",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":39},"01M71CFP32RT6GD3GKNHCJP8VP","yuri-adw","Invoke when user asks to run the ADW loop \u002F agentic engineering loop \u002F software factory workflow.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-adw\u002FSKILL.md",[],[182,183,184,185,186,187,188,189,190,87,191,110,192,193],"yuri adw","run","engineering loop","software factory","software","agentic engineering","factory","loop","invoke","engineering","agentic","user",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":212,"publishedAt":39},"01M71CFK4VPEKY6A73237P7RSN","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[200,203,206,209],{"categoryNo":201,"categoryName":202,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":204,"categoryName":205,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":207,"categoryName":208,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":210,"categoryName":211,"taxonomyType":158,"taxonomyTypeDesc":159},"01JBSEEDCAT000000000000036","内容创作者",[213,214,215,87,110,216,79,217,104,85,218],"writing","writing skills","new","work","editing","Existing"]