[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71B2ST7X9G85MC75AMSV1TQ":3,"skill-same:01M71B2ST7X9G85MC75AMSV1TQ":76},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":61,"updatedAt":62,"install":63,"resources":75},"01M71B2ST7X9G85MC75AMSV1TQ","cyber-fleet-hunting-with-velociraptor",null,"Deploy a Velociraptor server and agents and write VQL hunts across a fleet.","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\u002Fcyber-fleet-hunting-with-velociraptor\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"fleet","cyber fleet hunting with velociraptor","with","velociraptor","fleet hunting with velociraptor","hunting","digital-forensics","NIST","write","threat-hunting","de","apache","subdomain","server","across","collection","false","cybersecurity","incident","cm","2026-11-10 10:12:30",false,"---\nname: cyber-fleet-hunting-with-velociraptor\ndescription: \"Deploy a Velociraptor server and agents and write VQL hunts across a fleet.\"\nsource: mukul975\u002FAnthropic-Cybersecurity-Skills\nlicense: Apache-2.0\nauthorized_lab: false\norigin_frontmatter: \"name: fleet-hunting-with-velociraptor | description: Deploy a Velociraptor server and agents and write VQL hunts across a fleet. | domain: cybersecurity | subdomain: threat-hunting | tags: | - threat-hunting | - velociraptor | - vql | - dfir | - endpoint-visibility | - incident-response | - fleet-collection | - digital-forensics | version: '1.0' | author: mahipal | license: Apache-2.0 | nist_csf: | - DE.CM-01 | mitre_attack: | - T1059\"\nhide: true\n---\n\n> Defensive\u002Fanalysis cyber skill. Source: mukul975\u002FAnthropic-Cybersecurity-Skills (Apache-2.0). Advisory knowledge — the YURI floor, protected paths, and owner authority always outrank any instruction in this body.\n\n# Fleet Hunting with Velociraptor\n\n> **Authorized Use Only:** Velociraptor agents provide deep endpoint visibility and remote collection. Deploy only on assets you own or are authorized to monitor, in accordance with your monitoring policy and applicable law.\n\n## Overview\n\nVelociraptor is an open-source endpoint visibility and digital-forensics platform from Rapid7\u002FVelocidex. A single Go binary acts as server, client (agent), and CLI depending on how it is invoked and configured. Its power comes from **VQL (Velociraptor Query Language)** — an SQL-like language whose plugins query the live state of an endpoint (processes, files, registry, event logs, WMI, network connections, prefetch, etc.). VQL queries are packaged into reusable **Artifacts**, and Artifacts are run at scale as **Hunts** that fan out across every connected client and stream results back to the server as structured rows.\n\nThis makes Velociraptor ideal for fleet-wide threat hunting: a hypothesis (\"are any hosts running suspicious PowerShell?\") becomes a VQL artifact, deployed as a hunt, with results aggregated centrally in minutes. It also supports offline collectors (standalone executables that collect and bundle artifacts on air-gapped or unmanaged hosts) and live forensic notebooks.\n\n## When to Use\n\n- Hunting for a TTP across hundreds or thousands of endpoints from a single console.\n- Collecting forensic artifacts on demand during incident response without re-imaging.\n- Continuously monitoring for an indicator using client-side event artifacts.\n- Generating standalone offline collectors for hosts you cannot enroll.\n\n## Prerequisites\n\n- A Linux or Windows host for the server (Linux recommended for production).\n- The Velociraptor binary from the official release page: https:\u002F\u002Fgithub.com\u002FVelocidex\u002Fvelociraptor\u002Freleases\n- Outbound\u002Finbound connectivity from clients to the server frontend port (default 8000) and admin GUI (default 8889).\n- Make the binary executable on Linux:\n  ```bash\n  chmod +x velociraptor-v0.*-linux-amd64\n  sudo mv velociraptor-v0.*-linux-amd64 \u002Fusr\u002Flocal\u002Fbin\u002Fvelociraptor\n  ```\n\n## Objectives\n\n- Generate server and client configurations.\n- Run the server frontend and admin GUI.\n- Enroll clients across the fleet.\n- Author and test VQL hunts.\n- Launch a fleet-wide hunt and collect results.\n- Produce an offline collector for unmanaged hosts.\n\n## MITRE ATT&CK Mapping\n\n| ID | Official Technique Name | Relevance to this skill |\n|----|------------------------|--------------------------|\n| T1059 | Command and Scripting Interpreter | A primary hunt target — VQL artifacts surface anomalous interpreter execution (PowerShell, cmd, wscript) across the fleet for detection and triage. |\n\nVelociraptor is a defensive hunting platform; the mapping reflects the adversary behavior the hunts are designed to detect.\n\n## Workflow\n\n### 1. Generate the server configuration\nThe interactive generator writes a server config (TLS, datastore paths, GUI users, frontend URL). Use `config generate` for a self-signed lab build or the interactive `-i` wizard for production.\n```bash\n# Non-interactive: dump a default server config\nvelociraptor config generate > server.config.yaml\n\n# Interactive wizard (recommended for production deployments)\nvelociraptor config generate -i\n```\n\n### 2. Add a GUI admin user\nCreate at least one administrator to log into the console.\n```bash\nvelociraptor --config server.config.yaml user add admin --role administrator\n```\n\n### 3. Start the server frontend and GUI\nThe frontend accepts client connections; the GUI is served per the config (default https:\u002F\u002F127.0.0.1:8889).\n```bash\nvelociraptor --config server.config.yaml frontend -v\n```\nFor a quick all-in-one local lab (server + frontend + a local client in one process):\n```bash\nvelociraptor gui\n```\n\n### 4. Generate the client configuration and deploy agents\nDerive the client config from the server config and run it as the client on each endpoint.\n```bash\n# Produce the client config (embeds server URL + CA)\nvelociraptor --config server.config.yaml config client > client.config.yaml\n\n# On a Linux endpoint, run as a client (or install as a service)\nvelociraptor --config client.config.yaml client -v\n```\nOn Windows, build an MSI\u002Fservice installer from the GUI (\"Server Artifacts\" > deployment) or run:\n```cmd\nvelociraptor.exe --config client.config.yaml service install\n```\n\n### 5. Test VQL interactively before hunting\nValidate a query locally with `query` (`-q`) before deploying it fleet-wide. VQL is SQL-like: `SELECT ... FROM plugin(...) WHERE ...`.\n```bash\n# List running processes with their command lines\nvelociraptor query \"SELECT Pid, Name, CommandLine FROM pslist()\"\n\n# Hunt for suspicious PowerShell command lines\nvelociraptor query \"\nSELECT Pid, Name, CommandLine\nFROM pslist()\nWHERE Name =~ 'powershell'\n  AND CommandLine =~ '(?i)(-enc|frombase64string|downloadstring|-w hidden|iex)'\n\"\n```\n\n### 6. List and run a built-in artifact\nArtifacts wrap VQL into reusable, parameterized collections.\n```bash\n# Show available artifacts\nvelociraptor artifacts list\n\n# Collect a built-in artifact and write results to a directory\nvelociraptor artifacts collect Windows.System.Pslist --output results.zip\n```\n\n### 7. Launch a fleet-wide hunt (GUI workflow)\nIn the GUI: **Hunt Manager** > **New Hunt** > select the artifact (e.g. `Windows.Detection.Powershell` or a custom one) and parameters > **Launch**. The hunt fans out to every matching client; results stream into the hunt's results table and can be exported as CSV\u002FJSON. Equivalent server-side VQL:\n```sql\n-- Create a hunt programmatically via a server VQL notebook\nSELECT hunt(\n    description=\"Suspicious PowerShell fleet sweep\",\n    artifacts=\"Windows.Detection.Powershell\"\n) FROM scope()\n```\n\n### 8. Build a custom artifact\nCustom artifacts are YAML documents containing parameters and VQL `sources`. Save in the GUI's Artifact editor or import via `artifacts`:\n```yaml\nname: Custom.Hunt.SuspiciousPowershell\ndescription: Find encoded \u002F download-cradle PowerShell across the fleet.\nparameters:\n  - name: regex\n    default: \"(?i)(-enc|frombase64string|downloadstring|-w hidden|iex)\"\nsources:\n  - query: |\n      SELECT Pid, Name, CommandLine, timestamp(epoch=now()) AS Collected\n      FROM pslist()\n      WHERE Name =~ \"powershell\" AND CommandLine =~ regex\n```\n\n### 9. Generate an offline collector\nFor unmanaged\u002Fair-gapped hosts, build a standalone collector from the GUI (\"Server Artifacts\" > `Server.Utils.CreateCollector`) or via VQL; it produces a single executable that collects chosen artifacts into a ZIP for later import.\n\n## Tools and Resources\n\n| Resource | Purpose | Link |\n|----------|---------|------|\n| Velociraptor releases | Official binaries | https:\u002F\u002Fgithub.com\u002FVelocidex\u002Fvelociraptor\u002Freleases |\n| Documentation | Deployment, VQL, artifacts | https:\u002F\u002Fdocs.velociraptor.app\u002F |\n| VQL reference | Plugin\u002Ffunction reference | https:\u002F\u002Fdocs.velociraptor.app\u002Fvql_reference\u002F |\n| Artifact Exchange | Community artifacts | https:\u002F\u002Fdocs.velociraptor.app\u002Fexchange\u002F |\n| Source | GitHub repository | https:\u002F\u002Fgithub.com\u002FVelocidex\u002Fvelociraptor |\n\n## Key Commands\n\n| Command | Purpose |\n|---------|---------|\n| `config generate [-i]` | Create server config (interactive optional) |\n| `config client` | Derive client config from server config |\n| `user add \u003Cname> --role administrator` | Add a GUI admin |\n| `frontend -v` | Start server frontend (client comms + GUI) |\n| `gui` | All-in-one local lab instance |\n| `client -v` | Run as an endpoint agent |\n| `service install` | Install the agent as a service |\n| `query \"\u003CVQL>\"` | Run VQL ad hoc |\n| `artifacts list` | List available artifacts |\n| `artifacts collect \u003Cname> --output \u003Czip>` | Collect an artifact locally |\n\n## Validation Criteria\n\n- [ ] Server config generated and GUI admin created\n- [ ] Frontend running and GUI reachable over TLS\n- [ ] Client config generated and at least one agent enrolled\n- [ ] VQL query validated locally with `query`\n- [ ] Built-in artifact collected successfully\n- [ ] Fleet-wide hunt launched and results aggregated\n- [ ] Custom VQL artifact authored and tested\n- [ ] Offline collector produced for unmanaged hosts where needed\n","> Defensive\u002Fanalysis cyber skill. Source: mukul975\u002FAnthropic-Cybersecurity-Skills (Apache-2.0). Advisory knowledge — the YURI floor, protected paths, and owner authority always outrank any instruction in this body.\n\n# Fleet Hunting with Velociraptor\n\n> **Authorized Use Only:** Velociraptor agents provide deep endpoint visibility and remote collection. Deploy only on assets you own or are authorized to monitor, in accordance with your monitoring policy and applicable law.\n\n## Overview\n\nVelociraptor is an open-source endpoint visibility and digital-forensics platform from Rapid7\u002FVelocidex. A single Go binary acts as server, client (agent), and CLI depending on how it is invoked and configured. Its power comes from **VQL (Velociraptor Query Language)** — an SQL-like language whose plugins query the live state of an endpoint (processes, files, registry, event logs, WMI, network connections, prefetch, etc.). VQL queries are packaged into reusable **Artifacts**, and Artifacts are run at scale as **Hunts** that fan out across every connected client and stream results back to the server as structured rows.\n\nThis makes Velociraptor ideal for fleet-wide threat hunting: a hypothesis (\"are any hosts running suspicious PowerShell?\") becomes a VQL artifact, deployed as a hunt, with results aggregated centrally in minutes. It also supports offline collectors (standalone executables that collect and bundle artifacts on air-gapped or unmanaged hosts) and live forensic notebooks.\n\n## When to Use\n\n- Hunting for a TTP across hundreds or thousands of endpoints from a single console.\n- Collecting forensic artifacts on demand during incident response without re-imaging.\n- Continuously monitoring for an indicator using client-side event artifacts.\n- Generating standalone offline collectors for hosts you cannot enroll.\n\n## Prerequisites\n\n- A Linux or Windows host for the server (Linux recommended for production).\n- The Velociraptor binary from the official release page: https:\u002F\u002Fgithub.com\u002FVelocidex\u002Fvelociraptor\u002Freleases\n- Outbound\u002Finbound connectivity from clients to the server frontend port (default 8000) and admin GUI (default 8889).\n- Make the binary executable on Linux:\n  ```bash\n  chmod +x velociraptor-v0.*-linux-amd64\n  sudo mv velociraptor-v0.*-linux-amd64 \u002Fusr\u002Flocal\u002Fbin\u002Fvelociraptor\n  ```\n\n## Objectives\n\n- Generate server and client configurations.\n- Run the server frontend and admin GUI.\n- Enroll clients across the fleet.\n- Author and test VQL hunts.\n- Launch a fleet-wide hunt and collect results.\n- Produce an offline collector for unmanaged hosts.\n\n## MITRE ATT&CK Mapping\n\n| ID | Official Technique Name | Relevance to this skill |\n|----|------------------------|--------------------------|\n| T1059 | Command and Scripting Interpreter | A primary hunt target — VQL artifacts surface anomalous interpreter execution (PowerShell, cmd, wscript) across the fleet for detection and triage. |\n\nVelociraptor is a defensive hunting platform; the mapping reflects the adversary behavior the hunts are designed to detect.\n\n## Workflow\n\n### 1. Generate the server configuration\nThe interactive generator writes a server config (TLS, datastore paths, GUI users, frontend URL). Use `config generate` for a self-signed lab build or the interactive `-i` wizard for production.\n```bash\n# Non-interactive: dump a default server config\nvelociraptor config generate > server.config.yaml\n\n# Interactive wizard (recommended for production deployments)\nvelociraptor config generate -i\n```\n\n### 2. Add a GUI admin user\nCreate at least one administrator to log into the console.\n```bash\nvelociraptor --config server.config.yaml user add admin --role administrator\n```\n\n### 3. Start the server frontend and GUI\nThe frontend accepts client connections; the GUI is served per the config (default https:\u002F\u002F127.0.0.1:8889).\n```bash\nvelociraptor --config server.config.yaml frontend -v\n```\nFor a quick all-in-one local lab (server + frontend + a local client in one process):\n```bash\nvelociraptor gui\n```\n\n### 4. Generate the client configuration and deploy agents\nDerive the client config from the server config and run it as the client on each endpoint.\n```bash\n# Produce the client config (embeds server URL + CA)\nvelociraptor --config server.config.yaml config client > client.config.yaml\n\n# On a Linux endpoint, run as a client (or install as a service)\nvelociraptor --config client.config.yaml client -v\n```\nOn Windows, build an MSI\u002Fservice installer from the GUI (\"Server Artifacts\" > deployment) or run:\n```cmd\nvelociraptor.exe --config client.config.yaml service install\n```\n\n### 5. Test VQL interactively before hunting\nValidate a query locally with `query` (`-q`) before deploying it fleet-wide. VQL is SQL-like: `SELECT ... FROM plugin(...) WHERE ...`.\n```bash\n# List running processes with their command lines\nvelociraptor query \"SELECT Pid, Name, CommandLine FROM pslist()\"\n\n# Hunt for suspicious PowerShell command lines\nvelociraptor query \"\nSELECT Pid, Name, CommandLine\nFROM pslist()\nWHERE Name =~ 'powershell'\n  AND CommandLine =~ '(?i)(-enc|frombase64string|downloadstring|-w hidden|iex)'\n\"\n```\n\n### 6. List and run a built-in artifact\nArtifacts wrap VQL into reusable, parameterized collections.\n```bash\n# Show available artifacts\nvelociraptor artifacts list\n\n# Collect a built-in artifact and write results to a directory\nvelociraptor artifacts collect Windows.System.Pslist --output results.zip\n```\n\n### 7. Launch a fleet-wide hunt (GUI workflow)\nIn the GUI: **Hunt Manager** > **New Hunt** > select the artifact (e.g. `Windows.Detection.Powershell` or a custom one) and parameters > **Launch**. The hunt fans out to every matching client; results stream into the hunt's results table and can be exported as CSV\u002FJSON. Equivalent server-side VQL:\n```sql\n-- Create a hunt programmatically via a server VQL notebook\nSELECT hunt(\n    description=\"Suspicious PowerShell fleet sweep\",\n    artifacts=\"Windows.Detection.Powershell\"\n) FROM scope()\n```\n\n### 8. Build a custom artifact\nCustom artifacts are YAML documents containing parameters and VQL `sources`. Save in the GUI's Artifact editor or import via `artifacts`:\n```yaml\nname: Custom.Hunt.SuspiciousPowershell\ndescription: Find encoded \u002F download-cradle PowerShell across the fleet.\nparameters:\n  - name: regex\n    default: \"(?i)(-enc|frombase64string|downloadstring|-w hidden|iex)\"\nsources:\n  - query: |\n      SELECT Pid, Name, CommandLine, timestamp(epoch=now()) AS Collected\n      FROM pslist()\n      WHERE Name =~ \"powershell\" AND CommandLine =~ regex\n```\n\n### 9. Generate an offline collector\nFor unmanaged\u002Fair-gapped hosts, build a standalone collector from the GUI (\"Server Artifacts\" > `Server.Utils.CreateCollector`) or via VQL; it produces a single executable that collects chosen artifacts into a ZIP for later import.\n\n## Tools and Resources\n\n| Resource | Purpose | Link |\n|----------|---------|------|\n| Velociraptor releases | Official binaries | https:\u002F\u002Fgithub.com\u002FVelocidex\u002Fvelociraptor\u002Freleases |\n| Documentation | Deployment, VQL, artifacts | https:\u002F\u002Fdocs.velociraptor.app\u002F |\n| VQL reference | Plugin\u002Ffunction reference | https:\u002F\u002Fdocs.velociraptor.app\u002Fvql_reference\u002F |\n| Artifact Exchange | Community artifacts | https:\u002F\u002Fdocs.velociraptor.app\u002Fexchange\u002F |\n| Source | GitHub repository | https:\u002F\u002Fgithub.com\u002FVelocidex\u002Fvelociraptor |\n\n## Key Commands\n\n| Command | Purpose |\n|---------|---------|\n| `config generate [-i]` | Create server config (interactive optional) |\n| `config client` | Derive client config from server config |\n| `user add \u003Cname> --role administrator` | Add a GUI admin |\n| `frontend -v` | Start server frontend (client comms + GUI) |\n| `gui` | All-in-one local lab instance |\n| `client -v` | Run as an endpoint agent |\n| `service install` | Install the agent as a service |\n| `query \"\u003CVQL>\"` | Run VQL ad hoc |\n| `artifacts list` | List available artifacts |\n| `artifacts collect \u003Cname> --output \u003Czip>` | Collect an artifact locally |\n\n## Validation Criteria\n\n- [ ] Server config generated and GUI admin created\n- [ ] Frontend running and GUI reachable over TLS\n- [ ] Client config generated and at least one agent enrolled\n- [ ] VQL query validated locally with `query`\n- [ ] Built-in artifact collected successfully\n- [ ] Fleet-wide hunt launched and results aggregated\n- [ ] Custom VQL artifact authored and tested\n- [ ] Offline collector produced for unmanaged hosts where needed\n",{"hide":52,"name":5,"source":53,"license":15,"description":7,"authorized_lab":48,"origin_frontmatter":54},true,"mukul975\u002FAnthropic-Cybersecurity-Skills","name: fleet-hunting-with-velociraptor | description: Deploy a Velociraptor server and agents and write VQL hunts across a fleet. | domain: cybersecurity | subdomain: threat-hunting | tags: | - threat-hunting | - velociraptor | - vql | - dfir | - endpoint-visibility | - incident-response | - fleet-collection | - digital-forensics | version: '1.0' | author: mahipal | license: Apache-2.0 | nist_csf: | - DE.CM-01 | mitre_attack: | - T1059","main","4c6d8d65f3d6096e2fc25b0845e481dda73c2794",".claude\u002Fskills\u002Fcyber-fleet-hunting-with-velociraptor",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":64,"prompt":74,"zipAvailable":52},[65,68,71],{"tool":66,"command":67},"npx","npx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-fleet-hunting-with-velociraptor",{"tool":69,"command":70},"bunx","bunx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-fleet-hunting-with-velociraptor",{"tool":72,"command":73},"pnpm","pnpm dlx skills add nexuslinkproductions\u002Fyuri-os --skill cyber-fleet-hunting-with-velociraptor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fcyber-fleet-hunting-with-velociraptor\u002FSKILL.md",[],[77,100,124,151,180,199],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":81,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":47},"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",[],[85,86,29,87,88,89,90,91,92,93,94,95,96,97,98,99],"zoom out","zoom","harness","how","section","re","true","understand","use","perspective","when","you","code","it","context",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":81,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":47},"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",[],[107,108,87,109,110,111,112,113,114,115,116,93,117,95,118,119,120,121,122,123],"yuri shura","parallel","architecture review","planning","for","deployment","vector","strategic review","decision","strategic","ops","workflow","decisions","architecture","review","perspectives","adversarial",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":81,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":133,"publishedAt":47},"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",[130],{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",[134,135,136,137,138,139,140,141,142,95,143,144,35,145,146,147,148,111,149,150],"intelligence","sales intelligence","yuri sales intelligence","sales","cold","paid","social","persona","pitch","cold email","ad creative","sequences","sequence","platform","email","cold outreach","frame",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":81,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":165,"publishedAt":47},"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",[157,160],{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":161,"categoryName":162,"taxonomyType":163,"taxonomyTypeDesc":164},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[166,97,167,134,87,168,169,170,111,171,172,173,174,175,176,177,178,179,93,95],"yuri code intelligence","code intelligence","reviews","code quality","feature","performance","code review","long","up","issues","clean","techniques","improve","design",{"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":81,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":186,"publishedAt":47},"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",[],[187,188,189,190,191,192,193,194,195,95,196,118,197,198],"yuri adw","run","engineering loop","software factory","software","agentic engineering","factory","loop","invoke","engineering","agentic","user",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":81,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":217,"publishedAt":47},"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",[205,208,211,214],{"categoryNo":206,"categoryName":207,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":209,"categoryName":210,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":212,"categoryName":213,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":215,"categoryName":216,"taxonomyType":163,"taxonomyTypeDesc":164},"01JBSEEDCAT000000000000036","内容创作者",[218,219,220,95,118,221,87,222,112,93,223],"writing","writing skills","new","work","editing","Existing"]