[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73BWJ64VT0HDRS3ZMXDACE0":3,"skill-same:01M73BWJ64VT0HDRS3ZMXDACE0":70},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":8,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":14,"categories":15,"keywords":16,"publishedAt":17,"linkOnly":18,"markdownContent":19,"markdownBody":20,"frontMatter":21,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":12,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":35,"install":36,"resources":49},"01M73BWJ64VT0HDRS3ZMXDACE0","neural-training",null,"ngdletrung\u002FJKAI-Zenith","ngdletrung","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F88302940?v=4","01M73BJVV5QJ87R3MC4BXJMWDN","01M73BJVWAAM03TM18QHKRDAMH",0,"MIT","https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FCORE\u002Fneural-training\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nid: neural-training\nname_vn: neural-training\nversion: 1.0.0\nauthor: Zenith Legacy\ndomain: UNKNOWN\nintent_pairs: []\naliases_vn: []\nschema: {}\npriority: NORMAL\nrelated_skills: []\n\n---\n\n# neural-training (neural-training)\n\n## 🌟 TỔNG QUAN\n---\nname: neural-training\ndescription: >\n  Neural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation.\n  Use when: pattern learning, model optimization, knowledge transfer, adaptive routing.\n  Skip when: simple tasks, no learning required, one-off operations.\n---\n\n# Neural Training Skill\n\n## Purpose\nTrain and optimize neural patterns using SONA, MoE, and EWC++ systems.\n\n## When to Trigger\n- Training new patterns\n- Optimizing agent routing\n- Knowledge consolidation\n- Pattern recognition tasks\n\n## Intelligence Pipeline\n\n1. **RETRIEVE** — Fetch relevant patterns via HNSW (150x-12,500x faster)\n2. **JUDGE** — Evaluate with verdicts (success$failure)\n3. **DISTILL** — Extract key learnings via LoRA\n4. **CONSOLIDATE** — Prevent catastrophic forgetting via EWC++\n\n## Components\n\n| Component | Purpose | Performance |\n|-----------|---------|-------------|\n| SONA | Self-optimizing adaptation | \u003C0.05ms |\n| MoE | Expert routing | 8 experts |\n| HNSW | Pattern search | 150x-12,500x |\n| EWC++ | Prevent forgetting | Continuous |\n| Flash Attention | Speed | 2.49x-7.47x |\n\n## Commands\n\n### Train Patterns\n```bash\nnpx claude-flow neural train --model-type moe --epochs 10\n```\n\n### Check Status\n```bash\nnpx claude-flow neural status\n```\n\n### View Patterns\n```bash\nnpx claude-flow neural patterns --type all\n```\n\n### Predict\n```bash\nnpx claude-flow neural predict --input \"task description\"\n```\n\n### Optimize\n```bash\nnpx claude-flow neural optimize --target latency\n```\n\n## Best Practices\n1. Use pretrain hook for batch learning\n2. Store successful patterns after completion\n3. Consolidate regularly to prevent forgetting\n4. Route based on task complexity\n\n\n## 🛠️ PHÁC ĐỒ VẬN HÀNH (OPERATIONAL PROTOCOL)\n### 🔍 Phase 1: Investigation (Thẩm định)\n- Xác minh tham số đầu vào dựa trên Schema.\n- Kiểm tra bối cảnh hệ thống liên quan.\n\n### 🚀 Phase 2: Action (Thực thi)\n- Triệu hồi logic thực thi trong `logic.py`.\n- Trả về kết quả và chắt lọc kinh nghiệm.\n\n---\n## ⚠️ SAI LẦM THƯỜNG GẶP (COMMON PITFALLS)\n- Chưa ghi nhận.\n\n---\n*TRUNG THÀNH - CHÍNH XÁC - TỐI THƯỢNG* 💎🦾\n","# neural-training (neural-training)\n\n## 🌟 TỔNG QUAN\n---\nname: neural-training\ndescription: >\n  Neural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation.\n  Use when: pattern learning, model optimization, knowledge transfer, adaptive routing.\n  Skip when: simple tasks, no learning required, one-off operations.\n---\n\n# Neural Training Skill\n\n## Purpose\nTrain and optimize neural patterns using SONA, MoE, and EWC++ systems.\n\n## When to Trigger\n- Training new patterns\n- Optimizing agent routing\n- Knowledge consolidation\n- Pattern recognition tasks\n\n## Intelligence Pipeline\n\n1. **RETRIEVE** — Fetch relevant patterns via HNSW (150x-12,500x faster)\n2. **JUDGE** — Evaluate with verdicts (success$failure)\n3. **DISTILL** — Extract key learnings via LoRA\n4. **CONSOLIDATE** — Prevent catastrophic forgetting via EWC++\n\n## Components\n\n| Component | Purpose | Performance |\n|-----------|---------|-------------|\n| SONA | Self-optimizing adaptation | \u003C0.05ms |\n| MoE | Expert routing | 8 experts |\n| HNSW | Pattern search | 150x-12,500x |\n| EWC++ | Prevent forgetting | Continuous |\n| Flash Attention | Speed | 2.49x-7.47x |\n\n## Commands\n\n### Train Patterns\n```bash\nnpx claude-flow neural train --model-type moe --epochs 10\n```\n\n### Check Status\n```bash\nnpx claude-flow neural status\n```\n\n### View Patterns\n```bash\nnpx claude-flow neural patterns --type all\n```\n\n### Predict\n```bash\nnpx claude-flow neural predict --input \"task description\"\n```\n\n### Optimize\n```bash\nnpx claude-flow neural optimize --target latency\n```\n\n## Best Practices\n1. Use pretrain hook for batch learning\n2. Store successful patterns after completion\n3. Consolidate regularly to prevent forgetting\n4. Route based on task complexity\n\n\n## 🛠️ PHÁC ĐỒ VẬN HÀNH (OPERATIONAL PROTOCOL)\n### 🔍 Phase 1: Investigation (Thẩm định)\n- Xác minh tham số đầu vào dựa trên Schema.\n- Kiểm tra bối cảnh hệ thống liên quan.\n\n### 🚀 Phase 2: Action (Thực thi)\n- Triệu hồi logic thực thi trong `logic.py`.\n- Trả về kết quả và chắt lọc kinh nghiệm.\n\n---\n## ⚠️ SAI LẦM THƯỜNG GẶP (COMMON PITFALLS)\n- Chưa ghi nhận.\n\n---\n*TRUNG THÀNH - CHÍNH XÁC - TỐI THƯỢNG* 💎🦾\n",{"id":5,"author":22,"domain":23,"schema":24,"name_vn":5,"version":25,"priority":26,"aliases_vn":27,"intent_pairs":28,"related_skills":29},"Zenith Legacy","UNKNOWN",{},"1.0.0","NORMAL",[],[],[],"main","571327a911ec45873180fd5628525b6d4504ed79","intelligence\u002Fskills\u002FCORE\u002Fneural-training","https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith","https:\u002F\u002Fgithub.com\u002Fngdletrung","2026-11-08 11:23:33",{"source":7,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add ngdletrung\u002FJKAI-Zenith --skill neural-training",{"tool":42,"command":43},"bunx","bunx skills add ngdletrung\u002FJKAI-Zenith --skill neural-training",{"tool":45,"command":46},"pnpm","pnpm dlx skills add ngdletrung\u002FJKAI-Zenith --skill neural-training","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FCORE\u002Fneural-training\u002FSKILL.md",true,[50,56,62,66],{"path":51,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":54,"sourceHtmlUrl":55},"__init__.py","SCRIPT","脚本",64,"https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FCORE\u002Fneural-training\u002F__init__.py",{"path":57,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":60,"sourceHtmlUrl":61},"dossier.md","REFERENCE","参考文档",1987,"https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FCORE\u002Fneural-training\u002Fdossier.md",{"path":63,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":64,"sourceHtmlUrl":65},"logic.py",395,"https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FCORE\u002Fneural-training\u002Flogic.py",{"path":67,"resourceType":58,"resourceTypeDesc":59,"sizeBytes":68,"sourceHtmlUrl":69},"manifest.json",919,"https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FCORE\u002Fneural-training\u002Fmanifest.json",[71,78,84,91,97,103],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":8,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":77,"publishedAt":17},"01M73C2NC1W5P3A0V3GQAJ0SJB","standard_plugin","Hướng dẫn kích hoạt và vận hành cho kỹ năng standard_plugin.","https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002Ftemplates\u002Fstandard_plugin\u002FSKILL.md",[],[],{"skillNo":79,"name":80,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":8,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":17},"01M73C2KAN3Q7D5XEBKB3KZW1B","_STANDARD","https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002F_STANDARD\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":8,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":17},"01M73C2HV7KDJQTA6ACCR10HZB","test_dynamic_audit","Thao tác kiểm toán động","https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FTEST\u002Ftest_dynamic_audit\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":8,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":17},"01M73C2FRG0JHZ6W155V1KCHRP","security-audit","https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FSECURITY\u002Fsecurity-audit\u002FSKILL.md",[],[],{"skillNo":98,"name":99,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":8,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":17},"01M73C2EMWYHFTAR9RSZ41TBGP","security-and-hardening","https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FSECURITY\u002Fsecurity-and-hardening\u002FSKILL.md",[],[],{"skillNo":104,"name":105,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":8,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":17},"01M73C2CV0BQ59NAEWXKBQ370Z","performance-optimization","https:\u002F\u002Fgithub.com\u002Fngdletrung\u002FJKAI-Zenith\u002Fblob\u002Fmain\u002Fintelligence\u002Fskills\u002FSECURITY\u002Fperformance-optimization\u002FSKILL.md",[],[]]