[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6N3K480KZC2ZDXQ3374AX0B":3,"skill-same:01M6N3K480KZC2ZDXQ3374AX0B":81},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":6,"ownerDescription":49,"updatedAt":50,"install":51,"resources":64},"01M6N3K480KZC2ZDXQ3374AX0B","vector-memory-hack",null,"Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, \u003C10ms search time.","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-hack\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"vector memory hack","vector","vector memory","memory","context","for","reading","relevant","embedding","time","ai agent memory","files","instant","markdown","sqlite","ZeRO","search","semantic search","documentation","ai agent","2026-11-10 10:12:30",false,"---\nname: vector-memory-hack\ndescription: Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, \u003C10ms search time.\n---\n\n# Vector Memory Hack\n\nUltra-lightweight semantic search for AI agent memory systems. Find relevant context in milliseconds without heavy dependencies.\n\n## Why Use This?\n\n**Problem:** AI agents waste tokens reading entire MEMORY.md files (3000+ tokens) just to find 2-3 relevant sections.\n\n**Solution:** Vector Memory Hack enables semantic search that finds relevant context in \u003C10ms using only Python standard library + SQLite.\n\n**Benefits:**\n- ⚡ **Fast:** \u003C10ms search across 50+ sections\n- ? **Accurate:** TF-IDF + Cosine Similarity finds semantically related content\n- ? **Token Efficient:** Read 3-5 sections instead of entire file\n- ?️ **Zero Dependencies:** No PyTorch, no transformers, no heavy installs\n- ? **Multilingual:** Works with CZ\u002FEN\u002FDE and other languages\n\n## Quick Start\n\n### 1. Index your memory file\n\n```bash\npython3 scripts\u002Fvector_search.py --rebuild\n```\n\n### 2. Search for context\n\n```bash\n# Using the CLI wrapper\nvsearch \"backup config rules\"\n\n# Or directly\npython3 scripts\u002Fvector_search.py --search \"backup config rules\" --top-k 5\n```\n\n### 3. Use results in your workflow\n\nThe search returns top-k most relevant sections with similarity scores:\n\n```\n1. [0.288] Auto-Backup System\n   Script: \u002Froot\u002F.openclaw\u002Fworkspace\u002Fscripts\u002Fbackup-config.sh\n   ...\n\n2. [0.245] Security Rules\n   Never send emails without explicit user consent...\n```\n\n## How It Works\n\n```\nMEMORY.md\n    ↓\n[Parse Sections] → Extract headers and content\n    ↓\n[TF-IDF Vectorizer] → Create sparse vectors\n    ↓\n[SQLite Storage] → vectors.db\n    ↓\n[Cosine Similarity] → Find top-k matches\n```\n\n**Technology Stack:**\n- **Tokenization:** Custom multilingual tokenizer with stopword removal\n- **Vectors:** TF-IDF (Term Frequency - Inverse Document Frequency)\n- **Storage:** SQLite with JSON-encoded sparse vectors\n- **Similarity:** Cosine similarity scoring\n\n## Commands\n\n### Rebuild Index\n```bash\npython3 scripts\u002Fvector_search.py --rebuild\n```\nParses MEMORY.md, computes TF-IDF vectors, stores in SQLite.\n\n### Incremental Update\n```bash\npython3 scripts\u002Fvector_search.py --update\n```\nOnly processes changed sections (hash-based detection).\n\n### Search\n```bash\npython3 scripts\u002Fvector_search.py --search \"your query\" --top-k 5\n```\n\n### Statistics\n```bash\npython3 scripts\u002Fvector_search.py --stats\n```\n\n## Integration for Agents\n\n**Required step before every task:**\n\n```bash\n# Agent receives task: \"Update SSH config\"\n# Step 1: Find relevant context\nvsearch \"ssh config changes\"\n\n# Step 2: Read top results to understand:\n#   - Server addresses and credentials\n#   - Backup requirements\n#   - Deployment procedures\n\n# Step 3: Execute task with full context\n```\n\n## Configuration\n\nEdit these variables in `scripts\u002Fvector_search.py`:\n\n```python\nMEMORY_PATH = Path(\"\u002Fpath\u002Fto\u002Fyour\u002FMEMORY.md\")\nVECTORS_DIR = Path(\"\u002Fpath\u002Fto\u002Fvectors\u002Fstorage\")\nDB_PATH = VECTORS_DIR \u002F \"vectors.db\"\n```\n\n## Customization\n\n### Adding Stopwords\nEdit the `stopwords` set in `_tokenize()` method for your language.\n\n### Changing Similarity Metric\nModify `_cosine_similarity()` for different scoring (Euclidean, Manhattan, etc.)\n\n### Batch Processing\nUse `rebuild()` for full reindex, `update()` for incremental changes.\n\n## Performance\n\n| Metric | Value |\n|--------|-------|\n| Indexing Speed | ~50 sections\u002Fsecond |\n| Search Speed | \u003C10ms for 1000 vectors |\n| Memory Usage | ~10KB per section |\n| Disk Usage | Minimal (SQLite + JSON) |\n\n## Comparison with Alternatives\n\n| Solution | Dependencies | Speed | Setup | Best For |\n|----------|--------------|-------|-------|----------|\n| **Vector Memory Hack** | Zero (stdlib only) | \u003C10ms | Instant | Quick deployment, edge cases |\n| sentence-transformers | PyTorch + 500MB | ~100ms | 5+ min | High accuracy, offline capable |\n| OpenAI Embeddings | API calls | ~500ms | API key | Best accuracy, cloud-based |\n| ChromaDB | Docker + 4GB RAM | ~50ms | Complex | Large-scale production |\n\n**When to use Vector Memory Hack:**\n- ✅ Need instant deployment\n- ✅ Resource-constrained environments\n- ✅ Quick prototyping\n- ✅ Edge devices \u002F VPS with limited RAM\n- ✅ No GPU available\n\n**When to use heavier alternatives:**\n- Need state-of-the-art semantic accuracy\n- Have GPU resources\n- Large-scale production (10k+ documents)\n\n## File Structure\n\n```\nvector-memory-hack\u002F\n├── SKILL.md                  # This file\n└── scripts\u002F\n    ├── vector_search.py      # Main Python module\n    └── vsearch               # CLI wrapper (bash)\n```\n\n## Example Output\n\n```bash\n$ vsearch \"backup config rules\" 3\n\nSearch results for: 'backup config rules'\n\n1. [0.288] Auto-Backup System\n   Script: \u002Froot\u002F.openclaw\u002Fworkspace\u002Fscripts\u002Fbackup-config.sh\n   Target: \u002Froot\u002F.openclaw\u002Fbackups\u002Fconfig\u002F\n   Keep: Last 10 backups\n   \n2. [0.245] Security Protocol\n   CRITICAL: Never send emails without explicit user consent\n   Applies to: All agents including sub-agents\n   \n3. [0.198] Deployment Checklist\n   Before deployment:\n   1. Run backup-config.sh\n   2. Validate changes\n   3. Test thoroughly\n```\n\n## Troubleshooting\n\n### \"No sections found\"\n- Check MEMORY_PATH points to existing markdown file\n- Ensure file has ## or ### headers\n\n### \"All scores are 0.0\"\n- Rebuild index: `python3 scripts\u002Fvector_search.py --rebuild`\n- Check vocabulary contains your search terms\n\n### \"Database locked\"\n- Wait for other process to finish\n- Or delete vectors.db and rebuild\n\n## License\n\nMIT License - Free for personal and commercial use.\n\n---\n\n**Created by:** OpenClaw Agent (@mig6671)  \n**Published on:** ClawHub  \n**Version:** 1.0.0\n","# Vector Memory Hack\n\nUltra-lightweight semantic search for AI agent memory systems. Find relevant context in milliseconds without heavy dependencies.\n\n## Why Use This?\n\n**Problem:** AI agents waste tokens reading entire MEMORY.md files (3000+ tokens) just to find 2-3 relevant sections.\n\n**Solution:** Vector Memory Hack enables semantic search that finds relevant context in \u003C10ms using only Python standard library + SQLite.\n\n**Benefits:**\n- ⚡ **Fast:** \u003C10ms search across 50+ sections\n- ? **Accurate:** TF-IDF + Cosine Similarity finds semantically related content\n- ? **Token Efficient:** Read 3-5 sections instead of entire file\n- ?️ **Zero Dependencies:** No PyTorch, no transformers, no heavy installs\n- ? **Multilingual:** Works with CZ\u002FEN\u002FDE and other languages\n\n## Quick Start\n\n### 1. Index your memory file\n\n```bash\npython3 scripts\u002Fvector_search.py --rebuild\n```\n\n### 2. Search for context\n\n```bash\n# Using the CLI wrapper\nvsearch \"backup config rules\"\n\n# Or directly\npython3 scripts\u002Fvector_search.py --search \"backup config rules\" --top-k 5\n```\n\n### 3. Use results in your workflow\n\nThe search returns top-k most relevant sections with similarity scores:\n\n```\n1. [0.288] Auto-Backup System\n   Script: \u002Froot\u002F.openclaw\u002Fworkspace\u002Fscripts\u002Fbackup-config.sh\n   ...\n\n2. [0.245] Security Rules\n   Never send emails without explicit user consent...\n```\n\n## How It Works\n\n```\nMEMORY.md\n    ↓\n[Parse Sections] → Extract headers and content\n    ↓\n[TF-IDF Vectorizer] → Create sparse vectors\n    ↓\n[SQLite Storage] → vectors.db\n    ↓\n[Cosine Similarity] → Find top-k matches\n```\n\n**Technology Stack:**\n- **Tokenization:** Custom multilingual tokenizer with stopword removal\n- **Vectors:** TF-IDF (Term Frequency - Inverse Document Frequency)\n- **Storage:** SQLite with JSON-encoded sparse vectors\n- **Similarity:** Cosine similarity scoring\n\n## Commands\n\n### Rebuild Index\n```bash\npython3 scripts\u002Fvector_search.py --rebuild\n```\nParses MEMORY.md, computes TF-IDF vectors, stores in SQLite.\n\n### Incremental Update\n```bash\npython3 scripts\u002Fvector_search.py --update\n```\nOnly processes changed sections (hash-based detection).\n\n### Search\n```bash\npython3 scripts\u002Fvector_search.py --search \"your query\" --top-k 5\n```\n\n### Statistics\n```bash\npython3 scripts\u002Fvector_search.py --stats\n```\n\n## Integration for Agents\n\n**Required step before every task:**\n\n```bash\n# Agent receives task: \"Update SSH config\"\n# Step 1: Find relevant context\nvsearch \"ssh config changes\"\n\n# Step 2: Read top results to understand:\n#   - Server addresses and credentials\n#   - Backup requirements\n#   - Deployment procedures\n\n# Step 3: Execute task with full context\n```\n\n## Configuration\n\nEdit these variables in `scripts\u002Fvector_search.py`:\n\n```python\nMEMORY_PATH = Path(\"\u002Fpath\u002Fto\u002Fyour\u002FMEMORY.md\")\nVECTORS_DIR = Path(\"\u002Fpath\u002Fto\u002Fvectors\u002Fstorage\")\nDB_PATH = VECTORS_DIR \u002F \"vectors.db\"\n```\n\n## Customization\n\n### Adding Stopwords\nEdit the `stopwords` set in `_tokenize()` method for your language.\n\n### Changing Similarity Metric\nModify `_cosine_similarity()` for different scoring (Euclidean, Manhattan, etc.)\n\n### Batch Processing\nUse `rebuild()` for full reindex, `update()` for incremental changes.\n\n## Performance\n\n| Metric | Value |\n|--------|-------|\n| Indexing Speed | ~50 sections\u002Fsecond |\n| Search Speed | \u003C10ms for 1000 vectors |\n| Memory Usage | ~10KB per section |\n| Disk Usage | Minimal (SQLite + JSON) |\n\n## Comparison with Alternatives\n\n| Solution | Dependencies | Speed | Setup | Best For |\n|----------|--------------|-------|-------|----------|\n| **Vector Memory Hack** | Zero (stdlib only) | \u003C10ms | Instant | Quick deployment, edge cases |\n| sentence-transformers | PyTorch + 500MB | ~100ms | 5+ min | High accuracy, offline capable |\n| OpenAI Embeddings | API calls | ~500ms | API key | Best accuracy, cloud-based |\n| ChromaDB | Docker + 4GB RAM | ~50ms | Complex | Large-scale production |\n\n**When to use Vector Memory Hack:**\n- ✅ Need instant deployment\n- ✅ Resource-constrained environments\n- ✅ Quick prototyping\n- ✅ Edge devices \u002F VPS with limited RAM\n- ✅ No GPU available\n\n**When to use heavier alternatives:**\n- Need state-of-the-art semantic accuracy\n- Have GPU resources\n- Large-scale production (10k+ documents)\n\n## File Structure\n\n```\nvector-memory-hack\u002F\n├── SKILL.md                  # This file\n└── scripts\u002F\n    ├── vector_search.py      # Main Python module\n    └── vsearch               # CLI wrapper (bash)\n```\n\n## Example Output\n\n```bash\n$ vsearch \"backup config rules\" 3\n\nSearch results for: 'backup config rules'\n\n1. [0.288] Auto-Backup System\n   Script: \u002Froot\u002F.openclaw\u002Fworkspace\u002Fscripts\u002Fbackup-config.sh\n   Target: \u002Froot\u002F.openclaw\u002Fbackups\u002Fconfig\u002F\n   Keep: Last 10 backups\n   \n2. [0.245] Security Protocol\n   CRITICAL: Never send emails without explicit user consent\n   Applies to: All agents including sub-agents\n   \n3. [0.198] Deployment Checklist\n   Before deployment:\n   1. Run backup-config.sh\n   2. Validate changes\n   3. Test thoroughly\n```\n\n## Troubleshooting\n\n### \"No sections found\"\n- Check MEMORY_PATH points to existing markdown file\n- Ensure file has ## or ### headers\n\n### \"All scores are 0.0\"\n- Rebuild index: `python3 scripts\u002Fvector_search.py --rebuild`\n- Check vocabulary contains your search terms\n\n### \"Database locked\"\n- Wait for other process to finish\n- Or delete vectors.db and rebuild\n\n## License\n\nMIT License - Free for personal and commercial use.\n\n---\n\n**Created by:** OpenClaw Agent (@mig6671)  \n**Published on:** ClawHub  \n**Version:** 1.0.0\n",{"name":5,"description":7},"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","人工智能与大型语言模型\u002Fvector-memory-hack",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":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill vector-memory-hack",{"tool":57,"command":58},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill vector-memory-hack",{"tool":60,"command":61},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill vector-memory-hack","安装技能，技能地址：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-hack\u002FSKILL.md",true,[65,71,75],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"_meta.json","REFERENCE","参考文档",467,"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-hack\u002F_meta.json",{"path":72,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":73,"sourceHtmlUrl":74},"README.md",11315,"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-hack\u002FREADME.md",{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"scripts\u002Fvector_search.py","SCRIPT","脚本",15614,"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-hack\u002Fscripts\u002Fvector_search.py",[82,106,142,153,163,191],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":38},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. Deploys a non-custodial subaccount (Safe) linked to their EOA, enables automated yield optimization, and lets them deposit\u002Fwithdraw anytime.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzyfai-sdk\u002FSKILL.md",[],[84,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105],"safe","ethereum","defi","Optimization","user","use","Wallet","base","deposit","withdraw","yield","arbitrum","non-custodial","passive","defi yield","earn","yield optimization",{"skillNo":107,"name":108,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":124,"publishedAt":38},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[111,116,119],{"categoryNo":112,"categoryName":113,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":117,"categoryName":118,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[125,126,127,128,129,130,131,91,132,95,133,134,135,136,137,138,139,140,141],"api","zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","across","positions","token","prices",{"skillNo":143,"name":144,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":150,"publishedAt":38},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[147,148,149],{"categoryNo":112,"categoryName":113,"taxonomyType":114,"taxonomyTypeDesc":115},{"categoryNo":117,"categoryName":118,"taxonomyType":114,"taxonomyTypeDesc":115},{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},[125,151,152,126,127,128,129,130,131,91,132,95,134,135,136,137,138,139,140,141],"api skill","zerion api skill",{"skillNo":154,"name":155,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":161,"publishedAt":38},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[158,159,160],{"categoryNo":112,"categoryName":113,"taxonomyType":114,"taxonomyTypeDesc":115},{"categoryNo":117,"categoryName":118,"taxonomyType":114,"taxonomyTypeDesc":115},{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},[128,125,162,126,127,129,130,91,132,95,133,134,135,136,137,138,139,140,141],"zerion api mcp",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":172,"publishedAt":38},"01M6Q5QQYZB7A501BDEQWKGSJT","yahoo-finance-forex","Fetches real-time FOREX news and market data from Yahoo Finance for major currency pairs (EUR\u002FUSD, GBP\u002FUSD, USD\u002FJPY, etc.). Analyzes sentiment and provides trading context.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-forex\u002FSKILL.md",[169],{"categoryNo":170,"categoryName":171,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000040","财务",[173,174,175,176,177,178,179,180,181,182,183,184,185,186,27,187,188,189,190,23],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","sentiment","real-time","python3","install","Market","usd","news","currency","gbp","pip","market data",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":198,"publishedAt":38},"01M6Q5QM6VS84WZVM7BKXXPF32","yahoo-finance","This skill should be used when the user asks to \"get stock prices\", \"check stock quotes\", \"look up earnings\", \"get financial data\", \"find trending stocks\", or needs stock market data from Yahoo Finance.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-cli\u002FSKILL.md",[197],{"categoryNo":170,"categoryName":171,"taxonomyType":122,"taxonomyTypeDesc":123},[173,174,176,199,200,201,202,203,204,184,205,206,207,208,209,210,211,212,141,190],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]