[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CACH7XFDYRY1R1AXSXBTAA":3,"skill-same:01M6CACH7XFDYRY1R1AXSXBTAA":82},{"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":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":51,"install":52,"resources":65},"01M6CACH7XFDYRY1R1AXSXBTAA","find-nearby",null,"Find nearby places (restaurants, cafes, bars, pharmacies, etc.) using OpenStreetMap. Works with coordinates, addresses, cities, zip codes, or Telegram location pins. No API keys needed.","jleechanorg\u002Fjleechan-skills","jleechanorg","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F221404966?v=4","01M6C1VJ99XG950X1DWPEBCBGX","01M6C1VJDK5YVDNT3362RT5WSB",6,"MIT","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002Fhermes\u002Fskills\u002Fleisure\u002Ffind-nearby\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"find","find nearby","api","telegram","maps","location","coordinates","places","local","codes","with","restaurants","zip","addresses","keys","api keys","2026-11-10 10:12:30",false,"---\nname: find-nearby\ndescription: Find nearby places (restaurants, cafes, bars, pharmacies, etc.) using OpenStreetMap. Works with coordinates, addresses, cities, zip codes, or Telegram location pins. No API keys needed.\nversion: 1.0.0\nmetadata:\n  hermes:\n    tags: [location, maps, nearby, places, restaurants, local]\n    related_skills: []\n---\n\n# Find Nearby — Local Place Discovery\n\nFind restaurants, cafes, bars, pharmacies, and other places near any location. Uses OpenStreetMap (free, no API keys). Works with:\n\n- **Coordinates** from Telegram location pins (latitude\u002Flongitude in conversation)\n- **Addresses** (\"near 123 Main St, Springfield\")\n- **Cities** (\"restaurants in downtown Austin\")\n- **Zip codes** (\"pharmacies near 90210\")\n- **Landmarks** (\"cafes near Times Square\")\n\n## Quick Reference\n\n```bash\n# By coordinates (from Telegram location pin or user-provided)\npython3 SKILL_DIR\u002Fscripts\u002Ffind_nearby.py --lat \u003CLAT> --lon \u003CLON> --type restaurant --radius 1500\n\n# By address, city, or landmark (auto-geocoded)\npython3 SKILL_DIR\u002Fscripts\u002Ffind_nearby.py --near \"Times Square, New York\" --type cafe\n\n# Multiple place types\npython3 SKILL_DIR\u002Fscripts\u002Ffind_nearby.py --near \"downtown austin\" --type restaurant --type bar --limit 10\n\n# JSON output\npython3 SKILL_DIR\u002Fscripts\u002Ffind_nearby.py --near \"90210\" --type pharmacy --json\n```\n\n### Parameters\n\n| Flag | Description | Default |\n|------|-------------|---------|\n| `--lat`, `--lon` | Exact coordinates | — |\n| `--near` | Address, city, zip, or landmark (geocoded) | — |\n| `--type` | Place type (repeatable for multiple) | restaurant |\n| `--radius` | Search radius in meters | 1500 |\n| `--limit` | Max results | 15 |\n| `--json` | Machine-readable JSON output | off |\n\n### Common Place Types\n\n`restaurant`, `cafe`, `bar`, `pub`, `fast_food`, `pharmacy`, `hospital`, `bank`, `atm`, `fuel`, `parking`, `supermarket`, `convenience`, `hotel`\n\n## Workflow\n\n1. **Get the location.** Look for coordinates (`latitude: ... \u002F longitude: ...`) from a Telegram pin, or ask the user for an address\u002Fcity\u002Fzip.\n\n2. **Ask for preferences** (only if not already stated): place type, how far they're willing to go, any specifics (cuisine, \"open now\", etc.).\n\n3. **Run the script** with appropriate flags. Use `--json` if you need to process results programmatically.\n\n4. **Present results** with names, distances, and Google Maps links. If the user asked about hours or \"open now,\" check the `hours` field in results — if missing or unclear, verify with `web_search`.\n\n5. **For directions**, use the `directions_url` from results, or construct: `https:\u002F\u002Fwww.google.com\u002Fmaps\u002Fdir\u002F?api=1&origin=\u003CLAT>,\u003CLON>&destination=\u003CLAT>,\u003CLON>`\n\n## Tips\n\n- If results are sparse, widen the radius (1500 → 3000m)\n- For \"open now\" requests: check the `hours` field in results, cross-reference with `web_search` for accuracy since OSM hours aren't always complete\n- Zip codes alone can be ambiguous globally — prompt the user for country\u002Fstate if results look wrong\n- The script uses OpenStreetMap data which is community-maintained; coverage varies by region\n","# Find Nearby — Local Place Discovery\n\nFind restaurants, cafes, bars, pharmacies, and other places near any location. Uses OpenStreetMap (free, no API keys). Works with:\n\n- **Coordinates** from Telegram location pins (latitude\u002Flongitude in conversation)\n- **Addresses** (\"near 123 Main St, Springfield\")\n- **Cities** (\"restaurants in downtown Austin\")\n- **Zip codes** (\"pharmacies near 90210\")\n- **Landmarks** (\"cafes near Times Square\")\n\n## Quick Reference\n\n```bash\n# By coordinates (from Telegram location pin or user-provided)\npython3 SKILL_DIR\u002Fscripts\u002Ffind_nearby.py --lat \u003CLAT> --lon \u003CLON> --type restaurant --radius 1500\n\n# By address, city, or landmark (auto-geocoded)\npython3 SKILL_DIR\u002Fscripts\u002Ffind_nearby.py --near \"Times Square, New York\" --type cafe\n\n# Multiple place types\npython3 SKILL_DIR\u002Fscripts\u002Ffind_nearby.py --near \"downtown austin\" --type restaurant --type bar --limit 10\n\n# JSON output\npython3 SKILL_DIR\u002Fscripts\u002Ffind_nearby.py --near \"90210\" --type pharmacy --json\n```\n\n### Parameters\n\n| Flag | Description | Default |\n|------|-------------|---------|\n| `--lat`, `--lon` | Exact coordinates | — |\n| `--near` | Address, city, zip, or landmark (geocoded) | — |\n| `--type` | Place type (repeatable for multiple) | restaurant |\n| `--radius` | Search radius in meters | 1500 |\n| `--limit` | Max results | 15 |\n| `--json` | Machine-readable JSON output | off |\n\n### Common Place Types\n\n`restaurant`, `cafe`, `bar`, `pub`, `fast_food`, `pharmacy`, `hospital`, `bank`, `atm`, `fuel`, `parking`, `supermarket`, `convenience`, `hotel`\n\n## Workflow\n\n1. **Get the location.** Look for coordinates (`latitude: ... \u002F longitude: ...`) from a Telegram pin, or ask the user for an address\u002Fcity\u002Fzip.\n\n2. **Ask for preferences** (only if not already stated): place type, how far they're willing to go, any specifics (cuisine, \"open now\", etc.).\n\n3. **Run the script** with appropriate flags. Use `--json` if you need to process results programmatically.\n\n4. **Present results** with names, distances, and Google Maps links. If the user asked about hours or \"open now,\" check the `hours` field in results — if missing or unclear, verify with `web_search`.\n\n5. **For directions**, use the `directions_url` from results, or construct: `https:\u002F\u002Fwww.google.com\u002Fmaps\u002Fdir\u002F?api=1&origin=\u003CLAT>,\u003CLON>&destination=\u003CLAT>,\u003CLON>`\n\n## Tips\n\n- If results are sparse, widen the radius (1500 → 3000m)\n- For \"open now\" requests: check the `hours` field in results, cross-reference with `web_search` for accuracy since OSM hours aren't always complete\n- Zip codes alone can be ambiguous globally — prompt the user for country\u002Fstate if results look wrong\n- The script uses OpenStreetMap data which is community-maintained; coverage varies by region\n",{"name":5,"version":39,"metadata":40,"description":7},"1.0.0",{"hermes":41},{"tags":42,"related_skills":44},[23,22,43,25,29,26],"nearby",[],"main","05df7380981a2c4af868ce0fa57962ae0eea62a9","hermes\u002Fskills\u002Fleisure\u002Ffind-nearby",0,"https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills","https:\u002F\u002Fgithub.com\u002Fjleechanorg","2026-11-11 22:19:36",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add jleechanorg\u002Fjleechan-skills --skill find-nearby",{"tool":58,"command":59},"bunx","bunx skills add jleechanorg\u002Fjleechan-skills --skill find-nearby",{"tool":61,"command":62},"pnpm","pnpm dlx skills add jleechanorg\u002Fjleechan-skills --skill find-nearby","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002Fhermes\u002Fskills\u002Fleisure\u002Ffind-nearby\u002FSKILL.md",true,[66,72,76],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"_meta.json","REFERENCE","参考文档",117,"https:\u002F\u002Fgithub.com\u002Frotifer-protocol\u002Frotifer-playground\u002Fblob\u002Fmain\u002Ftests\u002Ffixtures\u002Fclawhub\u002Ffixture-skill\u002F_meta.json",{"path":73,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":74,"sourceHtmlUrl":75},"README.md",136,"https:\u002F\u002Fgithub.com\u002Frotifer-protocol\u002Frotifer-playground\u002Fblob\u002Fmain\u002Ftests\u002Ffixtures\u002Fclawhub\u002Ffixture-skill\u002FREADME.md",{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"scripts\u002Ffind_nearby.py","SCRIPT","脚本",6808,"https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002Fhermes\u002Fskills\u002Fleisure\u002Ffind-nearby\u002Fscripts\u002Ffind_nearby.py",[83,101,123,137,151,167],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":89,"publishedAt":34},"01M716Y5MTJ6GYJZ2ATXCJK8G4","instinct","Use when the user invokes \u002Finstinct or \u002Fin, or asks to \"message Instinct\", \"ask Instinct\", \"tell Instinct\", \"check Instinct\", or read replies from the configured peer assistant. iMessage first, WhatsApp Web as fallback.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Finstinct\u002FSKILL.md",[],[85,90,91,92,93,94,95,96,97,98,99,100],"user","when","replies","check","use","identity","whatsapp","gmail","read","message","ask",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":112,"publishedAt":34},"01M716Y4HF7FVVMJSA2CCVN5VF","hr","Compatibility alias for history-resume; produce the same fresh-state, provenance-aware resume briefing.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fhr\u002FSKILL.md",[107],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[103,113,114,115,116,117,118,119,120,121,122],"resume","compatibility","provenance","for","history","produce","alias","briefing","state","fresh",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":130,"publishedAt":34},"01M716Y1FFNFKFJYYKF8HQA6T8","history-resume","Resume briefing for the current repository branch or worktree using fresh state and admitted history sources.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fhistory-resume\u002FSKILL.md",[129],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[117,113,131,132,90,133,120,134,116,135,136,121,122],"history resume","branch","analysis","repository","worktree","sources",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":143,"publishedAt":34},"01M716XWXJ8EPHK4RH9T14TR85","grok","Alias for the canonical grokbot browser conversation workflow; \u002Fgrok talks to the Grok bot the same way \u002Fdot talks to Dot.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fgrok\u002FSKILL.md",[],[139,144,145,146,116,147,119,148,149,150],"dot","bot","workflow","canonical","conversation","browser","grokbot",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":34},"01M70N9Q07DTE0EW5KFXMMMSJ0","codexa-bench","Compare `codexa` and normal `codex` on a fair split of comparable work by default; use `--strict` for duplicate runs of the exact same task.","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fcodexa-bench\u002FSKILL.md",[],[158,159,160,94,161,116,162,163,164,165,166],"codexa bench","work","split","default","duplicate","task","compare","normal","codex",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":171,"licenseFileUrl":172,"sourceHtmlUrl":173,"categories":174,"keywords":186,"publishedAt":34},"01M6C24S2X60SP8BBFGAEKPEGW","frontend-design","Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML\u002FCSS layouts, or when styling\u002Fbeautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fjleechanorg\u002Fjleechan-skills\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ffrontend-design\u002FSKILL.md",[175,180,183],{"categoryNo":176,"categoryName":177,"taxonomyType":178,"taxonomyTypeDesc":179},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":181,"categoryName":182,"taxonomyType":178,"taxonomyTypeDesc":179},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":184,"categoryName":185,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000032","设计师",[187,188,189,94,190,191,192,90,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,28,208,209,91,210],"frontend","design","frontend design","generic","ui design","react","html","components","code","artifacts","ui","aesthetics","Production","web components","landing","applications","quality","web","css","create","examples","react components","web ui","pages"]