[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6NWPC896Y6V3BDPC06G42JS":3,"skill-same:01M6NWPC896Y6V3BDPC06G42JS":57},{"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":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":14,"repositoryHtmlUrl":38,"repositoryHomepageUrl":6,"ownerHtmlUrl":39,"ownerBlogUrl":40,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M6NWPC896Y6V3BDPC06G42JS","tldr-stats",null,"Show full session token usage, costs, TLDR savings, and hook activity","rubicanjr\u002FFinCognis","rubicanjr","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F234912519?v=4","01M6NT156NND8J474G34SB3923","Rubi","01M6NT15B70RWXE1N3YV0BZY8A",0,"MIT","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Ftldr-stats\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29],"stats","tldr","tldr stats","savings","session","show","full","token","activity","hook","usage","2026-11-10 10:12:30",false,"---\ndescription: Show full session token usage, costs, TLDR savings, and hook activity\n---\n\n# TLDR Stats Skill\n\nShow a beautiful dashboard with token usage, actual API costs, TLDR savings, and hook activity.\n\n## When to Use\n- See how much TLDR is saving you in real $ terms\n- Check total session token usage and costs\n- Before\u002Fafter comparisons of TLDR effectiveness\n- Debug whether TLDR\u002Fhooks are being used\n- See which model is being used\n\n## Instructions\n\n**IMPORTANT:** Run the script AND display the output to the user.\n\n1. Run the stats script:\n```bash\npython3 $CLAUDE_PROJECT_DIR\u002F.claude\u002Fscripts\u002Ftldr_stats.py\n```\n\n2. **Copy the full output into your response** so the user sees the dashboard directly in the chat. Do not just run the command silently - the user wants to see the stats.\n\n### Sample Output\n\n```\n╔══════════════════════════════════════════════════════════════╗\n║  ? Session Stats                                            ║\n╚══════════════════════════════════════════════════════════════╝\n\n  You've spent  $96.52  this session\n\n  Tokens Used\n        1.2M sent to Claude\n      416.3K received back\n       97.8K from prompt cache (8% reused)\n\n  TLDR Savings\n\n    You sent:               1.2M\n    Without TLDR:           2.5M\n\n    ? TLDR saved you ~$18.83\n    (Without TLDR: $115.35 → With TLDR: $96.52)\n\n    File reads: 1.3M → 20.9K █████████░ 98% smaller\n\n  TLDR Cache\n    Re-reading the same file? TLDR remembers it.\n    █████░░░░░░░░░░ 37% cache hits\n    (35 reused \u002F 60 parsed fresh)\n\n  Hooks: 553 calls (✓ all ok)\n  History: █▃▄ ▇▃▇▆ avg 84% compression\n  Daemon: 24m up │ 3 sessions\n```\n\n## Understanding the Numbers\n\n| Metric | What it means |\n|--------|---------------|\n| **You've spent** | Actual $ spent on Claude API this session |\n| **You sent \u002F Without TLDR** | Actual tokens vs what it would have been |\n| **TLDR saved you** | Money saved by compressing file reads |\n| **File reads X → Y** | Raw file tokens compressed to TLDR summary |\n| **Cache hits** | How often TLDR reuses parsed file results |\n| **History sparkline** | Compression % over recent sessions (█ = high) |\n\n## Visual Elements\n\n- **Progress bars** show savings and cache efficiency at a glance\n- **Sparklines** show historical trends (█ = high savings, ▁ = low)\n- **Colors** indicate status (green = good, yellow = moderate, red = concern)\n- **Emojis** distinguish model types (? Opus, ? Sonnet, ? Haiku)\n\n## Notes\n\n- Token savings vary by file size (big files = more savings)\n- Cache hit rate starts low, increases as you re-read files\n- Cost estimates use: Opus $15\u002F1M, Sonnet $3\u002F1M, Haiku $0.25\u002F1M\n- Stats update in real-time as you work\n","# TLDR Stats Skill\n\nShow a beautiful dashboard with token usage, actual API costs, TLDR savings, and hook activity.\n\n## When to Use\n- See how much TLDR is saving you in real $ terms\n- Check total session token usage and costs\n- Before\u002Fafter comparisons of TLDR effectiveness\n- Debug whether TLDR\u002Fhooks are being used\n- See which model is being used\n\n## Instructions\n\n**IMPORTANT:** Run the script AND display the output to the user.\n\n1. Run the stats script:\n```bash\npython3 $CLAUDE_PROJECT_DIR\u002F.claude\u002Fscripts\u002Ftldr_stats.py\n```\n\n2. **Copy the full output into your response** so the user sees the dashboard directly in the chat. Do not just run the command silently - the user wants to see the stats.\n\n### Sample Output\n\n```\n╔══════════════════════════════════════════════════════════════╗\n║  ? Session Stats                                            ║\n╚══════════════════════════════════════════════════════════════╝\n\n  You've spent  $96.52  this session\n\n  Tokens Used\n        1.2M sent to Claude\n      416.3K received back\n       97.8K from prompt cache (8% reused)\n\n  TLDR Savings\n\n    You sent:               1.2M\n    Without TLDR:           2.5M\n\n    ? TLDR saved you ~$18.83\n    (Without TLDR: $115.35 → With TLDR: $96.52)\n\n    File reads: 1.3M → 20.9K █████████░ 98% smaller\n\n  TLDR Cache\n    Re-reading the same file? TLDR remembers it.\n    █████░░░░░░░░░░ 37% cache hits\n    (35 reused \u002F 60 parsed fresh)\n\n  Hooks: 553 calls (✓ all ok)\n  History: █▃▄ ▇▃▇▆ avg 84% compression\n  Daemon: 24m up │ 3 sessions\n```\n\n## Understanding the Numbers\n\n| Metric | What it means |\n|--------|---------------|\n| **You've spent** | Actual $ spent on Claude API this session |\n| **You sent \u002F Without TLDR** | Actual tokens vs what it would have been |\n| **TLDR saved you** | Money saved by compressing file reads |\n| **File reads X → Y** | Raw file tokens compressed to TLDR summary |\n| **Cache hits** | How often TLDR reuses parsed file results |\n| **History sparkline** | Compression % over recent sessions (█ = high) |\n\n## Visual Elements\n\n- **Progress bars** show savings and cache efficiency at a glance\n- **Sparklines** show historical trends (█ = high savings, ▁ = low)\n- **Colors** indicate status (green = good, yellow = moderate, red = concern)\n- **Emojis** distinguish model types (? Opus, ? Sonnet, ? Haiku)\n\n## Notes\n\n- Token savings vary by file size (big files = more savings)\n- Cache hit rate starts low, increases as you re-read files\n- Cost estimates use: Opus $15\u002F1M, Sonnet $3\u002F1M, Haiku $0.25\u002F1M\n- Stats update in real-time as you work\n",{"description":7},"main","70e1c350500ee750987420bbae48837afa906b7e","skills\u002Ftldr-stats","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis","https:\u002F\u002Fgithub.com\u002Frubicanjr","linktr.ee\u002Ffinanskodu","Industrial Engineer & CIO @FinansKodu. Architecting AI-driven financial agents and autonomous workflows. 🏗️ Building systems, not just prompts.","2026-06-09 13:50:15",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add rubicanjr\u002FFinCognis --skill tldr-stats",{"tool":49,"command":50},"bunx","bunx skills add rubicanjr\u002FFinCognis --skill tldr-stats",{"tool":52,"command":53},"pnpm","pnpm dlx skills add rubicanjr\u002FFinCognis --skill tldr-stats","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Ftldr-stats\u002FSKILL.md",true,[],[58,79,88,108,138,157],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":69,"publishedAt":30},"01M6NWS9DAXJTADT4T5MB49X8W","workflow-router","Goal-based workflow orchestration - routes tasks to specialist agents based on user goals","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-router\u002FSKILL.md",[64],{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[70,71,72,73,74,75,76,77,78],"workflow router","workflow","router","orchestration","goals","goal","user","workflow orchestration","tasks",{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":30},"01M6NWS3RETSC0SHP16HP66E14","wiring","Wiring Verification","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fwiring\u002FSKILL.md",[],[81,86,87],"verification","false",{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":97,"publishedAt":30},"01M6NWS0XW9CVQZBDA1EGW0TWC","websocket-patterns","Connection management, room patterns, reconnection strategies, message buffering, and binary protocol design.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fwebsocket-patterns\u002Fskill.md",[94],{"categoryNo":95,"categoryName":96,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000015","视觉设计",[98,99,100,101,102,103,104,105,106,107],"websocket","patterns","websocket patterns","design","protocol","management","message","strategies","binary","connection",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":118,"publishedAt":30},"01M6NWRDQDX9ZNETAWRRMQWV52","visual-verdict","Screenshot comparison QA for frontend development. Takes a screenshot of the current implementation, scores it across multiple visual dimensions, and returns a structured PASS\u002FREVISE\u002FFAIL verdict with concrete fixes. Use when implementing UI from a design reference or verifying visual correctness.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fvisual-verdict\u002FSKILL.md",[114,117],{"categoryNo":115,"categoryName":116,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},[119,120,121,122,123,124,125,126,127,128,129,130,131,101,132,133,134,135,136,137],"visual","verdict","visual verdict","ui","across","revise","comparison","correctness","implementing","for","fixes","dimensions","frontend","development","qa","screenshot","returns","use","frontend development",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":30},"01M6NWR8HSNRS7MNDK2J02QYMN","verification-loop","Comprehensive verification system covering build, types, lint, tests, security, and diff review before a PR.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fverification-loop\u002FSKILL.md",[],[145,146,86,147,148,149,150,151,152,153,154,155,156],"verification loop","loop","lint","review","security","pr","system","diff","build","tests","diff review","types",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":163,"publishedAt":30},"01M6NWR4HHSFR0VR5EXFRWA747","vector-db-patterns","Embedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fvector-db-patterns\u002Fskill.md",[],[164,165,166,167,99,168,169,170,171,105,172,173,174,175,176,128,177,178],"vector db patterns","vector db","db","vector","semantic search","search","embedding strategies","algorithms","chunking","semantic","retrieval","embedding","Hybrid Search","hybrid","rag"]