[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6N15QEW4966J4THC5VST9VF":3,"skill-same:01M6N15QEW4966J4THC5VST9VF":94},{"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":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6N15QEW4966J4THC5VST9VF","langcache",null,"This skill should be used when the user asks to \"enable semantic caching\", \"cache LLM responses\", \"reduce API costs\", \"speed up AI responses\", \"configure LangCache\", \"search the semantic cache\", \"store responses in cache\", or mentions Redis LangCache, semantic similarity caching, or LLM response caching. Provides integration with Redis LangCache managed service for semantic caching of prompts and responses.","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\u002Fopenclaw-langcache\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"caching","managed","reduce","for","with","Managed Service","store","similarity","speed","search","up","webfetch","read","configure","Bash","api","redis","cache","integration","2026-11-10 10:12:30",false,"---\nname: langcache\ndescription: This skill should be used when the user asks to \"enable semantic caching\", \"cache LLM responses\", \"reduce API costs\", \"speed up AI responses\", \"configure LangCache\", \"search the semantic cache\", \"store responses in cache\", or mentions Redis LangCache, semantic similarity caching, or LLM response caching. Provides integration with Redis LangCache managed service for semantic caching of prompts and responses.\nversion: 1.0.0\ntools: Read, Bash, WebFetch\n---\n\n# Redis LangCache Semantic Caching\n\nThis skill integrates Redis LangCache, a fully-managed semantic caching service, into OpenClaw workflows. LangCache stores LLM prompts and responses, returning cached results for semantically similar queries to reduce costs and latency.\n\n## Prerequisites\n\nBefore using LangCache, ensure the following environment variables are configured:\n\n```bash\nLANGCACHE_HOST=\u003Cyour-langcache-host>\nLANGCACHE_CACHE_ID=\u003Cyour-cache-id>\nLANGCACHE_API_KEY=\u003Cyour-api-key>\n```\n\nStore these in `~\u002F.openclaw\u002Fsecrets.env` or configure them in the OpenClaw settings.\n\n## Core Operations\n\n### Search for Cached Response\n\nBefore calling an LLM, check if a semantically similar response exists:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh search \"What is semantic caching?\"\n```\n\nWith similarity threshold (0.0-1.0, higher = stricter match):\n\n```bash\n.\u002Fscripts\u002Flangcache.sh search \"What is semantic caching?\" --threshold 0.95\n```\n\nWith attribute filtering:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh search \"What is semantic caching?\" --attr \"model=gpt-5\"\n```\n\n### Store New Response\n\nAfter receiving an LLM response, cache it for future use:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh store \"What is semantic caching?\" \"Semantic caching stores responses based on meaning similarity...\"\n```\n\nWith attributes for filtering\u002Forganization:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh store \"prompt\" \"response\" --attr \"model=gpt-5\" --attr \"user_id=123\"\n```\n\n### Delete Cached Entries\n\nBy entry ID:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh delete --id \"\u003Centry-id>\"\n```\n\nBy attributes:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh delete --attr \"user_id=123\"\n```\n\n### Flush Cache\n\nClear all entries (use with caution):\n\n```bash\n.\u002Fscripts\u002Flangcache.sh flush\n```\n\n## Integration Pattern\n\nThe recommended pattern for integrating LangCache into agent workflows:\n\n```\n1. Receive user prompt\n2. Search LangCache for similar cached response\n3. If cache hit (similarity >= threshold):\n   - Return cached response immediately\n   - Log cache hit for observability\n4. If cache miss:\n   - Call LLM API\n   - Store prompt + response in LangCache\n   - Return LLM response\n```\n\n## Default Caching Policy\n\nThis policy is enforced automatically. All cache operations MUST respect these rules.\n\n### CACHEABLE (white-list)\n\n| Category | Examples | Threshold |\n|----------|----------|-----------|\n| Factual Q&A | \"What is X?\", \"How does Y work?\" | 0.90 |\n| Definitions \u002F docs \u002F help text | API docs, command help, explanations | 0.90 |\n| Command explanations | \"What does `git rebase` do?\" | 0.92 |\n| Reusable reply templates | \"polite no\", \"follow-up\", \"scheduling\", \"intro\" | 0.88 |\n| Style transforms | \"make this warmer\u002Fshorter\u002Ffirmer\" | 0.85 |\n| Generic communication scripts | negotiation templates, professional responses | 0.88 |\n\n### NEVER CACHE (hard blocks)\n\nThese patterns are **blocked at the code level** - cache operations will refuse to store them.\n\n| Category | Patterns to Detect | Reason |\n|----------|-------------------|--------|\n| **Temporal info** | today, tomorrow, this week, deadline, ETA, \"in X minutes\", appointments, schedules | Stale immediately |\n| **Credentials** | API keys, tokens, passwords, OTP, 2FA codes, secrets | Security risk |\n| **Identifiers** | phone numbers, emails, addresses, account IDs, order numbers, message IDs, chat IDs, JIDs | Privacy \u002F PII |\n| **Personal context** | names + relationships, private history, \"who said what\", specific conversations | Privacy \u002F context-dependent |\n\n### Detection Patterns\n\nThe following regex patterns trigger a hard block:\n\n```\n# Temporal\n\\b(today|tomorrow|tonight|yesterday)\\b\n\\b(this|next|last)\\s+(week|month|year|monday|tuesday|...)\\b\n\\b(in\\s+\\d+\\s+(minutes?|hours?|days?))\\b\n\\b(deadline|eta|appointment|schedule[d]?)\\b\n\n# Credentials\n\\b(api[_-]?key|token|password|secret|otp|2fa)\\b\n\\b(bearer|auth[orization]*)\\s+\\S+\n\n# Identifiers\n\\b\\d{10,}\\b                          # phone numbers, long IDs\n\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+   # emails\n\\b(order|account|message|chat)[_-]?id\\b\n\n# Personal context\n\\b(my\\s+(wife|husband|partner|friend|boss|mom|dad|brother|sister))\\b\n\\b(said\\s+to\\s+me|told\\s+me|between\\s+us)\\b\n```\n\n### Attribute Strategies\n\nUse attributes to partition the cache:\n\n- `model`: LLM model used (useful when switching models)\n- `category`: `factual`, `template`, `style`, `command`\n- `skill`: Which skill generated the response\n- `version`: API or prompt version\n\n## Search Strategies\n\nLangCache supports two search strategies:\n\n- **semantic** (default): Vector similarity matching\n- **exact**: Case-insensitive exact match\n\nCombine both for hybrid search:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh search \"prompt\" --strategy \"exact,semantic\"\n```\n\n## Observability\n\nMonitor cache performance:\n- Track hit\u002Fmiss ratios\n- Log similarity scores for hits\n- Alert on high miss rates (may indicate threshold too high)\n- Review stored entries periodically for relevance\n\n## References\n\n- [API Reference](references\u002Fapi-reference.md) - Complete REST API documentation\n- [Best Practices](references\u002Fbest-practices.md) - Optimization techniques\n\n## Examples\n\n- [examples\u002Fbasic-caching.sh](examples\u002Fbasic-caching.sh) - Simple cache workflow\n- [examples\u002Fagent-integration.py](examples\u002Fagent-integration.py) - Python integration pattern\n","# Redis LangCache Semantic Caching\n\nThis skill integrates Redis LangCache, a fully-managed semantic caching service, into OpenClaw workflows. LangCache stores LLM prompts and responses, returning cached results for semantically similar queries to reduce costs and latency.\n\n## Prerequisites\n\nBefore using LangCache, ensure the following environment variables are configured:\n\n```bash\nLANGCACHE_HOST=\u003Cyour-langcache-host>\nLANGCACHE_CACHE_ID=\u003Cyour-cache-id>\nLANGCACHE_API_KEY=\u003Cyour-api-key>\n```\n\nStore these in `~\u002F.openclaw\u002Fsecrets.env` or configure them in the OpenClaw settings.\n\n## Core Operations\n\n### Search for Cached Response\n\nBefore calling an LLM, check if a semantically similar response exists:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh search \"What is semantic caching?\"\n```\n\nWith similarity threshold (0.0-1.0, higher = stricter match):\n\n```bash\n.\u002Fscripts\u002Flangcache.sh search \"What is semantic caching?\" --threshold 0.95\n```\n\nWith attribute filtering:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh search \"What is semantic caching?\" --attr \"model=gpt-5\"\n```\n\n### Store New Response\n\nAfter receiving an LLM response, cache it for future use:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh store \"What is semantic caching?\" \"Semantic caching stores responses based on meaning similarity...\"\n```\n\nWith attributes for filtering\u002Forganization:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh store \"prompt\" \"response\" --attr \"model=gpt-5\" --attr \"user_id=123\"\n```\n\n### Delete Cached Entries\n\nBy entry ID:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh delete --id \"\u003Centry-id>\"\n```\n\nBy attributes:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh delete --attr \"user_id=123\"\n```\n\n### Flush Cache\n\nClear all entries (use with caution):\n\n```bash\n.\u002Fscripts\u002Flangcache.sh flush\n```\n\n## Integration Pattern\n\nThe recommended pattern for integrating LangCache into agent workflows:\n\n```\n1. Receive user prompt\n2. Search LangCache for similar cached response\n3. If cache hit (similarity >= threshold):\n   - Return cached response immediately\n   - Log cache hit for observability\n4. If cache miss:\n   - Call LLM API\n   - Store prompt + response in LangCache\n   - Return LLM response\n```\n\n## Default Caching Policy\n\nThis policy is enforced automatically. All cache operations MUST respect these rules.\n\n### CACHEABLE (white-list)\n\n| Category | Examples | Threshold |\n|----------|----------|-----------|\n| Factual Q&A | \"What is X?\", \"How does Y work?\" | 0.90 |\n| Definitions \u002F docs \u002F help text | API docs, command help, explanations | 0.90 |\n| Command explanations | \"What does `git rebase` do?\" | 0.92 |\n| Reusable reply templates | \"polite no\", \"follow-up\", \"scheduling\", \"intro\" | 0.88 |\n| Style transforms | \"make this warmer\u002Fshorter\u002Ffirmer\" | 0.85 |\n| Generic communication scripts | negotiation templates, professional responses | 0.88 |\n\n### NEVER CACHE (hard blocks)\n\nThese patterns are **blocked at the code level** - cache operations will refuse to store them.\n\n| Category | Patterns to Detect | Reason |\n|----------|-------------------|--------|\n| **Temporal info** | today, tomorrow, this week, deadline, ETA, \"in X minutes\", appointments, schedules | Stale immediately |\n| **Credentials** | API keys, tokens, passwords, OTP, 2FA codes, secrets | Security risk |\n| **Identifiers** | phone numbers, emails, addresses, account IDs, order numbers, message IDs, chat IDs, JIDs | Privacy \u002F PII |\n| **Personal context** | names + relationships, private history, \"who said what\", specific conversations | Privacy \u002F context-dependent |\n\n### Detection Patterns\n\nThe following regex patterns trigger a hard block:\n\n```\n# Temporal\n\\b(today|tomorrow|tonight|yesterday)\\b\n\\b(this|next|last)\\s+(week|month|year|monday|tuesday|...)\\b\n\\b(in\\s+\\d+\\s+(minutes?|hours?|days?))\\b\n\\b(deadline|eta|appointment|schedule[d]?)\\b\n\n# Credentials\n\\b(api[_-]?key|token|password|secret|otp|2fa)\\b\n\\b(bearer|auth[orization]*)\\s+\\S+\n\n# Identifiers\n\\b\\d{10,}\\b                          # phone numbers, long IDs\n\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+   # emails\n\\b(order|account|message|chat)[_-]?id\\b\n\n# Personal context\n\\b(my\\s+(wife|husband|partner|friend|boss|mom|dad|brother|sister))\\b\n\\b(said\\s+to\\s+me|told\\s+me|between\\s+us)\\b\n```\n\n### Attribute Strategies\n\nUse attributes to partition the cache:\n\n- `model`: LLM model used (useful when switching models)\n- `category`: `factual`, `template`, `style`, `command`\n- `skill`: Which skill generated the response\n- `version`: API or prompt version\n\n## Search Strategies\n\nLangCache supports two search strategies:\n\n- **semantic** (default): Vector similarity matching\n- **exact**: Case-insensitive exact match\n\nCombine both for hybrid search:\n\n```bash\n.\u002Fscripts\u002Flangcache.sh search \"prompt\" --strategy \"exact,semantic\"\n```\n\n## Observability\n\nMonitor cache performance:\n- Track hit\u002Fmiss ratios\n- Log similarity scores for hits\n- Alert on high miss rates (may indicate threshold too high)\n- Review stored entries periodically for relevance\n\n## References\n\n- [API Reference](references\u002Fapi-reference.md) - Complete REST API documentation\n- [Best Practices](references\u002Fbest-practices.md) - Optimization techniques\n\n## Examples\n\n- [examples\u002Fbasic-caching.sh](examples\u002Fbasic-caching.sh) - Simple cache workflow\n- [examples\u002Fagent-integration.py](examples\u002Fagent-integration.py) - Python integration pattern\n",{"name":5,"tools":42,"version":43,"description":7},"Read, Bash, WebFetch","1.0.0","main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","人工智能与大型语言模型\u002Fopenclaw-langcache",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":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill langcache",{"tool":58,"command":59},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill langcache",{"tool":61,"command":62},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill langcache","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\u002Fopenclaw-langcache\u002FSKILL.md",true,[66,72,78,82,86,90],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"_meta.json","REFERENCE","参考文档",314,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fopenclaw-langcache\u002F_meta.json",{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"examples\u002Fagent-integration.py","SCRIPT","脚本",15791,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fopenclaw-langcache\u002Fexamples\u002Fagent-integration.py",{"path":79,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":80,"sourceHtmlUrl":81},"examples\u002Fbasic-caching.sh",1798,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fopenclaw-langcache\u002Fexamples\u002Fbasic-caching.sh",{"path":83,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fapi-reference.md",5006,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fopenclaw-langcache\u002Freferences\u002Fapi-reference.md",{"path":87,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fbest-practices.md",5734,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fopenclaw-langcache\u002Freferences\u002Fbest-practices.md",{"path":91,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":92,"sourceHtmlUrl":93},"scripts\u002Flangcache.sh",15085,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fopenclaw-langcache\u002Fscripts\u002Flangcache.sh",[95,119,154,165,175,204],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":37},"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",[],[97,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118],"safe","ethereum","defi","Optimization","user","use","Wallet","base","deposit","withdraw","yield","arbitrum","non-custodial","passive","defi yield","earn","yield optimization",{"skillNo":120,"name":121,"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":122,"categories":123,"keywords":137,"publishedAt":37},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[124,129,132],{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":130,"categoryName":131,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[33,138,139,140,141,142,143,104,144,108,145,146,147,148,149,150,151,152,153],"zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","across","positions","token","prices",{"skillNo":155,"name":156,"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":157,"categories":158,"keywords":162,"publishedAt":37},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[159,160,161],{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},{"categoryNo":130,"categoryName":131,"taxonomyType":127,"taxonomyTypeDesc":128},{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},[33,163,164,138,139,140,141,142,143,104,144,108,146,147,148,149,150,151,152,153],"api skill","zerion api skill",{"skillNo":166,"name":167,"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":168,"categories":169,"keywords":173,"publishedAt":37},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[170,171,172],{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},{"categoryNo":130,"categoryName":131,"taxonomyType":127,"taxonomyTypeDesc":128},{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},[140,33,174,138,139,141,142,104,144,108,145,146,147,148,149,150,151,152,153],"zerion api mcp",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":184,"publishedAt":37},"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",[181],{"categoryNo":182,"categoryName":183,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000040","财务",[185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,21],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","sentiment","real-time","python3","install","Market","usd","news","time","currency","gbp","pip","market data",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":37},"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",[210],{"categoryNo":182,"categoryName":183,"taxonomyType":135,"taxonomyTypeDesc":136},[185,186,188,212,213,214,215,216,28,196,217,218,219,220,221,222,223,224,153,203],"cli","homebrew","find","check","brew","local","get","stock","link","trending","sf","quotes","ln"]