[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7BPCARBKBK5EJCK8MH4TM49":3,"skill-same:01M7BPCARBKBK5EJCK8MH4TM49":47},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":29,"ownerHtmlUrl":30,"ownerBlogUrl":6,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M7BPCARBKBK5EJCK8MH4TM49","V3 Memory Unification",null,"Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).","YYC-Cube\u002FYYC3-Knowledge-Base","YYC-Cube","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235006543?v=4","01M79J2PH4SWD2X0825X6PN748","yyc3","01M79J2PHVK3F6ZBYT8K2SCZCW",1,"MIT","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Agent-智能体\u002FYYC3-Agent-智能体-ruflo\u002Fv3\u002F@claude-flow\u002Fmcp\u002F.claude\u002Fskills\u002Fv3-memory-unification\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: \"V3 Memory Unification\"\ndescription: \"Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).\"\n---\n\n# V3 Memory Unification\n\n## What This Skill Does\n\nConsolidates disparate memory systems into unified AgentDB backend with HNSW vector search, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.\n\n## Quick Start\n\n```bash\n# Initialize memory unification\nTask(\"Memory architecture\", \"Design AgentDB unification strategy\", \"v3-memory-specialist\")\n\n# AgentDB integration\nTask(\"AgentDB setup\", \"Configure HNSW indexing and vector search\", \"v3-memory-specialist\")\n\n# Data migration\nTask(\"Memory migration\", \"Migrate SQLite\u002FMarkdown to AgentDB\", \"v3-memory-specialist\")\n```\n\n## Systems to Unify\n\n### Legacy Systems → AgentDB\n```\n┌─────────────────────────────────────────┐\n│  • MemoryManager (basic operations)     │\n│  • DistributedMemorySystem (clustering) │\n│  • SwarmMemory (agent-specific)         │\n│  • AdvancedMemoryManager (features)     │\n│  • SQLiteBackend (structured)           │\n│  • MarkdownBackend (file-based)         │\n│  • HybridBackend (combination)          │\n└─────────────────────────────────────────┘\n                    ↓\n┌─────────────────────────────────────────┐\n│       🚀 AgentDB with HNSW             │\n│  • 150x-12,500x faster search          │\n│  • Unified query interface             │\n│  • Cross-agent memory sharing          │\n│  • SONA learning integration           │\n└─────────────────────────────────────────┘\n```\n\n## Implementation Architecture\n\n### Unified Memory Service\n```typescript\nclass UnifiedMemoryService implements IMemoryBackend {\n  constructor(\n    private agentdb: AgentDBAdapter,\n    private indexer: HNSWIndexer,\n    private migrator: DataMigrator\n  ) {}\n\n  async store(entry: MemoryEntry): Promise\u003Cvoid> {\n    await this.agentdb.store(entry);\n    await this.indexer.index(entry);\n  }\n\n  async query(query: MemoryQuery): Promise\u003CMemoryEntry[]> {\n    if (query.semantic) {\n      return this.indexer.search(query); \u002F\u002F 150x-12,500x faster\n    }\n    return this.agentdb.query(query);\n  }\n}\n```\n\n### HNSW Vector Search\n```typescript\nclass HNSWIndexer {\n  constructor(dimensions: number = 1536) {\n    this.index = new HNSWIndex({\n      dimensions,\n      efConstruction: 200,\n      M: 16,\n      speedupTarget: '150x-12500x'\n    });\n  }\n\n  async search(query: MemoryQuery): Promise\u003CMemoryEntry[]> {\n    const embedding = await this.embedContent(query.content);\n    const results = this.index.search(embedding, query.limit || 10);\n    return this.retrieveEntries(results);\n  }\n}\n```\n\n## Migration Strategy\n\n### Phase 1: Foundation\n```typescript\n\u002F\u002F AgentDB adapter setup\nconst agentdb = new AgentDBAdapter({\n  dimensions: 1536,\n  indexType: 'HNSW',\n  speedupTarget: '150x-12500x'\n});\n```\n\n### Phase 2: Data Migration\n```typescript\n\u002F\u002F SQLite → AgentDB\nconst migrateFromSQLite = async () => {\n  const entries = await sqlite.getAll();\n  for (const entry of entries) {\n    const embedding = await generateEmbedding(entry.content);\n    await agentdb.store({ ...entry, embedding });\n  }\n};\n\n\u002F\u002F Markdown → AgentDB\nconst migrateFromMarkdown = async () => {\n  const files = await glob('**\u002F*.md');\n  for (const file of files) {\n    const content = await fs.readFile(file, 'utf-8');\n    await agentdb.store({\n      id: generateId(),\n      content,\n      embedding: await generateEmbedding(content),\n      metadata: { originalFile: file }\n    });\n  }\n};\n```\n\n## SONA Integration\n\n### Learning Pattern Storage\n```typescript\nclass SONAMemoryIntegration {\n  async storePattern(pattern: LearningPattern): Promise\u003Cvoid> {\n    await this.memory.store({\n      id: pattern.id,\n      content: pattern.data,\n      metadata: {\n        sonaMode: pattern.mode,\n        reward: pattern.reward,\n        adaptationTime: pattern.adaptationTime\n      },\n      embedding: await this.generateEmbedding(pattern.data)\n    });\n  }\n\n  async retrieveSimilarPatterns(query: string): Promise\u003CLearningPattern[]> {\n    return this.memory.query({\n      type: 'semantic',\n      content: query,\n      filters: { type: 'learning_pattern' }\n    });\n  }\n}\n```\n\n## Performance Targets\n\n- **Search Speed**: 150x-12,500x improvement via HNSW\n- **Memory Usage**: 50-75% reduction through optimization\n- **Query Latency**: \u003C100ms for 1M+ entries\n- **Cross-Agent Sharing**: Real-time memory synchronization\n- **SONA Integration**: \u003C0.05ms adaptation time\n\n## Success Metrics\n\n- [ ] All 7 legacy memory systems migrated to AgentDB\n- [ ] 150x-12,500x search performance validated\n- [ ] 50-75% memory usage reduction achieved\n- [ ] Backward compatibility maintained\n- [ ] SONA learning patterns integrated\n- [ ] Cross-agent memory sharing operational","# V3 Memory Unification\n\n## What This Skill Does\n\nConsolidates disparate memory systems into unified AgentDB backend with HNSW vector search, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.\n\n## Quick Start\n\n```bash\n# Initialize memory unification\nTask(\"Memory architecture\", \"Design AgentDB unification strategy\", \"v3-memory-specialist\")\n\n# AgentDB integration\nTask(\"AgentDB setup\", \"Configure HNSW indexing and vector search\", \"v3-memory-specialist\")\n\n# Data migration\nTask(\"Memory migration\", \"Migrate SQLite\u002FMarkdown to AgentDB\", \"v3-memory-specialist\")\n```\n\n## Systems to Unify\n\n### Legacy Systems → AgentDB\n```\n┌─────────────────────────────────────────┐\n│  • MemoryManager (basic operations)     │\n│  • DistributedMemorySystem (clustering) │\n│  • SwarmMemory (agent-specific)         │\n│  • AdvancedMemoryManager (features)     │\n│  • SQLiteBackend (structured)           │\n│  • MarkdownBackend (file-based)         │\n│  • HybridBackend (combination)          │\n└─────────────────────────────────────────┘\n                    ↓\n┌─────────────────────────────────────────┐\n│       🚀 AgentDB with HNSW             │\n│  • 150x-12,500x faster search          │\n│  • Unified query interface             │\n│  • Cross-agent memory sharing          │\n│  • SONA learning integration           │\n└─────────────────────────────────────────┘\n```\n\n## Implementation Architecture\n\n### Unified Memory Service\n```typescript\nclass UnifiedMemoryService implements IMemoryBackend {\n  constructor(\n    private agentdb: AgentDBAdapter,\n    private indexer: HNSWIndexer,\n    private migrator: DataMigrator\n  ) {}\n\n  async store(entry: MemoryEntry): Promise\u003Cvoid> {\n    await this.agentdb.store(entry);\n    await this.indexer.index(entry);\n  }\n\n  async query(query: MemoryQuery): Promise\u003CMemoryEntry[]> {\n    if (query.semantic) {\n      return this.indexer.search(query); \u002F\u002F 150x-12,500x faster\n    }\n    return this.agentdb.query(query);\n  }\n}\n```\n\n### HNSW Vector Search\n```typescript\nclass HNSWIndexer {\n  constructor(dimensions: number = 1536) {\n    this.index = new HNSWIndex({\n      dimensions,\n      efConstruction: 200,\n      M: 16,\n      speedupTarget: '150x-12500x'\n    });\n  }\n\n  async search(query: MemoryQuery): Promise\u003CMemoryEntry[]> {\n    const embedding = await this.embedContent(query.content);\n    const results = this.index.search(embedding, query.limit || 10);\n    return this.retrieveEntries(results);\n  }\n}\n```\n\n## Migration Strategy\n\n### Phase 1: Foundation\n```typescript\n\u002F\u002F AgentDB adapter setup\nconst agentdb = new AgentDBAdapter({\n  dimensions: 1536,\n  indexType: 'HNSW',\n  speedupTarget: '150x-12500x'\n});\n```\n\n### Phase 2: Data Migration\n```typescript\n\u002F\u002F SQLite → AgentDB\nconst migrateFromSQLite = async () => {\n  const entries = await sqlite.getAll();\n  for (const entry of entries) {\n    const embedding = await generateEmbedding(entry.content);\n    await agentdb.store({ ...entry, embedding });\n  }\n};\n\n\u002F\u002F Markdown → AgentDB\nconst migrateFromMarkdown = async () => {\n  const files = await glob('**\u002F*.md');\n  for (const file of files) {\n    const content = await fs.readFile(file, 'utf-8');\n    await agentdb.store({\n      id: generateId(),\n      content,\n      embedding: await generateEmbedding(content),\n      metadata: { originalFile: file }\n    });\n  }\n};\n```\n\n## SONA Integration\n\n### Learning Pattern Storage\n```typescript\nclass SONAMemoryIntegration {\n  async storePattern(pattern: LearningPattern): Promise\u003Cvoid> {\n    await this.memory.store({\n      id: pattern.id,\n      content: pattern.data,\n      metadata: {\n        sonaMode: pattern.mode,\n        reward: pattern.reward,\n        adaptationTime: pattern.adaptationTime\n      },\n      embedding: await this.generateEmbedding(pattern.data)\n    });\n  }\n\n  async retrieveSimilarPatterns(query: string): Promise\u003CLearningPattern[]> {\n    return this.memory.query({\n      type: 'semantic',\n      content: query,\n      filters: { type: 'learning_pattern' }\n    });\n  }\n}\n```\n\n## Performance Targets\n\n- **Search Speed**: 150x-12,500x improvement via HNSW\n- **Memory Usage**: 50-75% reduction through optimization\n- **Query Latency**: \u003C100ms for 1M+ entries\n- **Cross-Agent Sharing**: Real-time memory synchronization\n- **SONA Integration**: \u003C0.05ms adaptation time\n\n## Success Metrics\n\n- [ ] All 7 legacy memory systems migrated to AgentDB\n- [ ] 150x-12,500x search performance validated\n- [ ] 50-75% memory usage reduction achieved\n- [ ] Backward compatibility maintained\n- [ ] SONA learning patterns integrated\n- [ ] Cross-agent memory sharing operational",{"name":5,"description":7},"main","87afe6ce96220accf50a345dea46784c8c9f210e","YYC3-Agent-智能体\u002FYYC3-Agent-智能体-ruflo\u002Fv3\u002F@claude-flow\u002Fmcp\u002F.claude\u002Fskills\u002Fv3-memory-unification",0,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base","https:\u002F\u002Fkb.yyc3.top\u002F","https:\u002F\u002Fgithub.com\u002FYYC-Cube","万象归元于云枢 | 深栈智启新纪元","2026-11-10 10:37:32",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill \"V3 Memory Unification\"",{"tool":39,"command":40},"bunx","bunx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill \"V3 Memory Unification\"",{"tool":42,"command":43},"pnpm","pnpm dlx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill \"V3 Memory Unification\"","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Agent-智能体\u002FYYC3-Agent-智能体-ruflo\u002Fv3\u002F@claude-flow\u002Fmcp\u002F.claude\u002Fskills\u002Fv3-memory-unification\u002FSKILL.md",true,[],[48,55,62,69,76,83],{"skillNo":49,"name":50,"nameZh":6,"description":51,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":52,"categories":53,"keywords":54,"publishedAt":19},"01M7CTDWHERCXPQCP0K03A86AS","vercel-sandbox","Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across commands, or wants ephemeral isolated browser environments. 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Triggers include \"check my Slack\", \"what channels have unreads\", \"send a message to\", \"search Slack for\", \"extract from Slack\", \"find who said\", or any task requiring programmatic Slack interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fslack\u002FSKILL.md",[],[],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":68,"publishedAt":19},"01M7CTCSSB8ZP3Q58C3YZDZK1W","electron","Automate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol. 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