[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6N3KB54SWHK7PZDDSVKH1DF":3,"skill-same:01M6N3KB54SWHK7PZDDSVKH1DF":124},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":45,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":48,"updatedAt":49,"install":50,"resources":63},"01M6N3KB54SWHK7PZDDSVKH1DF","vector-memory",null,"Smart memory search with automatic vector fallback. Uses semantic embeddings when available, falls back to built-in search otherwise. Zero configuration - works immediately after ClawHub install. No setup required - just install and memory_search works immediately, gets better after optional sync.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fvector-memory\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"memory","vector","vector memory","automatic","clawhub","smart memory","with","semantic","back","configuration","fallback","install","just","ZeRO","memory search","embeddings","search","setup","sync","2026-11-10 10:12:30",false,"---\nname: vector-memory\ndescription: Smart memory search with automatic vector fallback. Uses semantic embeddings when available, falls back to built-in search otherwise. Zero configuration - works immediately after ClawHub install. No setup required - just install and memory_search works immediately, gets better after optional sync.\n---\n\n# Vector Memory\n\nSmart memory search that **automatically selects the best method**:\n- **Vector search** (semantic, high quality) when synced\n- **Built-in search** (keyword, fast) as fallback\n\n**Zero configuration required.** Works immediately after install.\n\n## Quick Start\n\n### Install from ClawHub\n```bash\nnpx clawhub install vector-memory\n```\n\nDone! `memory_search` now works with automatic method selection.\n\n### Optional: Sync for Better Results\n```bash\nnode vector-memory\u002Fsmart_memory.js --sync\n```\n\nAfter sync, searches use neural embeddings for semantic understanding.\n\n## How It Works\n\n### Smart Selection\n```javascript\n\u002F\u002F Same call, automatic best method\nmemory_search(\"James principles values\") \n\n\u002F\u002F If vector ready: finds \"autonomy, competence, creation\" (semantic match)\n\u002F\u002F If not ready: uses keyword search (fallback)\n```\n\n### Behavior Flow\n1. **Check**: Is vector index ready?\n2. **Yes**: Use semantic search (synonyms, concepts)\n3. **No**: Use built-in search (keywords)\n4. **Vector fails**: Automatically fall back\n\n## Tools\n\n### memory_search\n**Auto-selects best method**\n\nParameters:\n- `query` (string): Search query\n- `max_results` (number): Max results (default: 5)\n\nReturns: Matches with path, lines, score, snippet\n\n### memory_get\nGet full content from file.\n\n### memory_sync\nIndex memory files for vector search. Run after edits.\n\n### memory_status\nCheck which method is active.\n\n## Comparison\n\n| Feature | Built-in | Vector | Smart Wrapper |\n|---------|----------|--------|---------------|\n| Synonyms | ❌ | ✅ | ✅ (when ready) |\n| Setup | Built-in | Requires sync | ✅ Zero config |\n| Fallback | N\u002FA | Manual | ✅ Automatic |\n\n## Usage\n\n**Immediate (no action needed):**\n```bash\nnode vector-memory\u002Fsmart_memory.js --search \"query\"\n```\n\n**Better quality (after sync):**\n```bash\n# One-time setup\nnode vector-memory\u002Fsmart_memory.js --sync\n\n# Now all searches use vector\nnode vector-memory\u002Fsmart_memory.js --search \"query\"\n```\n\n## Files\n\n| File | Purpose |\n|------|---------|\n| `smart_memory.js` | Main entry - auto-selects method |\n| `vector_memory_local.js` | Vector implementation |\n| `memory.js` | OpenClaw wrapper |\n\n## Configuration\n\n**None required.** \n\nOptional environment variables:\n```bash\nexport MEMORY_DIR=\u002Fpath\u002Fto\u002Fmemory\nexport MEMORY_FILE=\u002Fpath\u002Fto\u002FMEMORY.md\n```\n\n## Scaling\n\n- **\u003C 1000 chunks**: Built-in + JSON (current)\n- **> 1000 chunks**: Use pgvector (see references\u002Fpgvector.md)\n\n## References\n\n- [Integration](references\u002Fintegration.md) - Detailed setup\n- [pgvector](references\u002Fpgvector.md) - Large-scale deployment","# Vector Memory\n\nSmart memory search that **automatically selects the best method**:\n- **Vector search** (semantic, high quality) when synced\n- **Built-in search** (keyword, fast) as fallback\n\n**Zero configuration required.** Works immediately after install.\n\n## Quick Start\n\n### Install from ClawHub\n```bash\nnpx clawhub install vector-memory\n```\n\nDone! `memory_search` now works with automatic method selection.\n\n### Optional: Sync for Better Results\n```bash\nnode vector-memory\u002Fsmart_memory.js --sync\n```\n\nAfter sync, searches use neural embeddings for semantic understanding.\n\n## How It Works\n\n### Smart Selection\n```javascript\n\u002F\u002F Same call, automatic best method\nmemory_search(\"James principles values\") \n\n\u002F\u002F If vector ready: finds \"autonomy, competence, creation\" (semantic match)\n\u002F\u002F If not ready: uses keyword search (fallback)\n```\n\n### Behavior Flow\n1. **Check**: Is vector index ready?\n2. **Yes**: Use semantic search (synonyms, concepts)\n3. **No**: Use built-in search (keywords)\n4. **Vector fails**: Automatically fall back\n\n## Tools\n\n### memory_search\n**Auto-selects best method**\n\nParameters:\n- `query` (string): Search query\n- `max_results` (number): Max results (default: 5)\n\nReturns: Matches with path, lines, score, snippet\n\n### memory_get\nGet full content from file.\n\n### memory_sync\nIndex memory files for vector search. Run after edits.\n\n### memory_status\nCheck which method is active.\n\n## Comparison\n\n| Feature | Built-in | Vector | Smart Wrapper |\n|---------|----------|--------|---------------|\n| Synonyms | ❌ | ✅ | ✅ (when ready) |\n| Setup | Built-in | Requires sync | ✅ Zero config |\n| Fallback | N\u002FA | Manual | ✅ Automatic |\n\n## Usage\n\n**Immediate (no action needed):**\n```bash\nnode vector-memory\u002Fsmart_memory.js --search \"query\"\n```\n\n**Better quality (after sync):**\n```bash\n# One-time setup\nnode vector-memory\u002Fsmart_memory.js --sync\n\n# Now all searches use vector\nnode vector-memory\u002Fsmart_memory.js --search \"query\"\n```\n\n## Files\n\n| File | Purpose |\n|------|---------|\n| `smart_memory.js` | Main entry - auto-selects method |\n| `vector_memory_local.js` | Vector implementation |\n| `memory.js` | OpenClaw wrapper |\n\n## Configuration\n\n**None required.** \n\nOptional environment variables:\n```bash\nexport MEMORY_DIR=\u002Fpath\u002Fto\u002Fmemory\nexport MEMORY_FILE=\u002Fpath\u002Fto\u002FMEMORY.md\n```\n\n## Scaling\n\n- **\u003C 1000 chunks**: Built-in + JSON (current)\n- **> 1000 chunks**: Use pgvector (see references\u002Fpgvector.md)\n\n## References\n\n- [Integration](references\u002Fintegration.md) - Detailed setup\n- [pgvector](references\u002Fpgvector.md) - Large-scale deployment",{"name":5,"description":7},"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","人工智能与大型语言模型\u002Fvector-memory",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill vector-memory",{"tool":56,"command":57},"bunx","bunx skills add 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