[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73FHSXKQMJNWDK6FWA45Y98":3,"skill-same:01M73FHSXKQMJNWDK6FWA45Y98":49},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":26,"commitSha":27,"skillDirectory":28,"forksCount":29,"repositoryHtmlUrl":30,"repositoryHomepageUrl":31,"ownerHtmlUrl":32,"ownerBlogUrl":33,"ownerDescription":34,"updatedAt":35,"install":36,"resources":48},"01M73FHSXKQMJNWDK6FWA45Y98","run-llama--liteparse",null,"LiteParse — Rust document parser của LlamaIndex: PDF\u002FWord\u002FExcel\u002FPPT\u002Fimage → JSON với bounding boxes. Node.js\u002FPython\u002FWASM bindings. Tesseract OCR bundled, không cần cloud.","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Frun-llama--liteparse\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: run-llama--liteparse\ndescription: \"LiteParse — Rust document parser của LlamaIndex: PDF\u002FWord\u002FExcel\u002FPPT\u002Fimage → JSON với bounding boxes. Node.js\u002FPython\u002FWASM bindings. Tesseract OCR bundled, không cần cloud.\"\nallowed-tools: Bash, Read, Write\nuser-invocable: true\n---\n\nLiteParse: fast local document parser viết bằng Rust — PDF + office docs + images → structured JSON với spatial bounding boxes. Zero cloud dependency.\n\n## Install\n\n```bash\n# Node.js\nnpm install liteparse\n\n# Python\npip install liteparse\n\n# Rust\ncargo add liteparse\n```\n\n## Supported Formats\n\n```\nNative:   PDF (trực tiếp, không convert)\nOffice:   .doc .docx .odt .ppt .pptx .odp .xls .xlsx .ods .csv .tsv\nImages:   JPG PNG GIF BMP TIFF WebP SVG\n          (qua ImageMagick → OCR)\n```\n\n## API\n\n```typescript\nimport { parse } from 'liteparse'\n\n\u002F\u002F JSON với bounding boxes\nconst result = await parse('document.pdf', { format: 'json' })\n\u002F\u002F result.pages[0].blocks[0].bbox = { x, y, width, height }\n\u002F\u002F result.pages[0].blocks[0].text = \"...\"\n\n\u002F\u002F Plain text với layout preserved\nconst text = await parse('report.docx', { format: 'text' })\n\n\u002F\u002F Screenshot tại DPI tùy chỉnh\nawait parse('slide.pptx', { format: 'screenshot', dpi: 300 })\n```\n\n```python\nfrom liteparse import parse\n\nresult = parse(\"document.pdf\", format=\"json\")\nfor page in result[\"pages\"]:\n    for block in page[\"blocks\"]:\n        print(block[\"text\"], block[\"bbox\"])\n```\n\n## Performance\n\n```\nParallel OCR processing  — configurable worker threads\nSelective OCR            — chỉ chạy khi cần (không phải mọi page)\nPage-range targeting     — parse trang 10-20, skip phần còn lại\nDefault DPI: 150         — tùy chỉnh được\n```\n\n## OCR Options\n\n```\nDefault: Tesseract (bundled, offline)\nHTTP backends: EasyOCR, PaddleOCR (accuracy cao hơn)\nCustom: implement theo API spec\n```\n\n## CLI\n\n```bash\nliteparse document.pdf --format json --pages 1-10 --output result.json\nliteparse batch\u002F --format text --workers 4\n```\n\n## Khi nào dùng\n\n- RAG pipeline: parse docs → chunk → embed\n- Extract structured data từ PDF reports\n- Convert office docs sang Markdown cho LLM context\n- OCR images trong batch pipeline\n\n## Source\n\nhttps:\u002F\u002Fgithub.com\u002Frun-llama\u002Fliteparse · Apache-2.0 · +1613⭐\u002Fweek\n","LiteParse: fast local document parser viết bằng Rust — PDF + office docs + images → structured JSON với spatial bounding boxes. Zero cloud dependency.\n\n## Install\n\n```bash\n# Node.js\nnpm install liteparse\n\n# Python\npip install liteparse\n\n# Rust\ncargo add liteparse\n```\n\n## Supported Formats\n\n```\nNative:   PDF (trực tiếp, không convert)\nOffice:   .doc .docx .odt .ppt .pptx .odp .xls .xlsx .ods .csv .tsv\nImages:   JPG PNG GIF BMP TIFF WebP SVG\n          (qua ImageMagick → OCR)\n```\n\n## API\n\n```typescript\nimport { parse } from 'liteparse'\n\n\u002F\u002F JSON với bounding boxes\nconst result = await parse('document.pdf', { format: 'json' })\n\u002F\u002F result.pages[0].blocks[0].bbox = { x, y, width, height }\n\u002F\u002F result.pages[0].blocks[0].text = \"...\"\n\n\u002F\u002F Plain text với layout preserved\nconst text = await parse('report.docx', { format: 'text' })\n\n\u002F\u002F Screenshot tại DPI tùy chỉnh\nawait parse('slide.pptx', { format: 'screenshot', dpi: 300 })\n```\n\n```python\nfrom liteparse import parse\n\nresult = parse(\"document.pdf\", format=\"json\")\nfor page in result[\"pages\"]:\n    for block in page[\"blocks\"]:\n        print(block[\"text\"], block[\"bbox\"])\n```\n\n## Performance\n\n```\nParallel OCR processing  — configurable worker threads\nSelective OCR            — chỉ chạy khi cần (không phải mọi page)\nPage-range targeting     — parse trang 10-20, skip phần còn lại\nDefault DPI: 150         — tùy chỉnh được\n```\n\n## OCR Options\n\n```\nDefault: Tesseract (bundled, offline)\nHTTP backends: EasyOCR, PaddleOCR (accuracy cao hơn)\nCustom: implement theo API spec\n```\n\n## CLI\n\n```bash\nliteparse document.pdf --format json --pages 1-10 --output result.json\nliteparse batch\u002F --format text --workers 4\n```\n\n## Khi nào dùng\n\n- RAG pipeline: parse docs → chunk → embed\n- Extract structured data từ PDF reports\n- Convert office docs sang Markdown cho LLM context\n- OCR images trong batch pipeline\n\n## Source\n\nhttps:\u002F\u002Fgithub.com\u002Frun-llama\u002Fliteparse · Apache-2.0 · +1613⭐\u002Fweek\n",{"name":5,"description":7,"allowed-tools":24,"user-invocable":25},"Bash, Read, Write",true,"main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Frun-llama--liteparse",2,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-12 23:12:27",{"source":8,"skillName":5,"commands":37,"prompt":47,"zipAvailable":25},[38,41,44],{"tool":39,"command":40},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill run-llama--liteparse",{"tool":42,"command":43},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill run-llama--liteparse",{"tool":45,"command":46},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill run-llama--liteparse","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Frun-llama--liteparse\u002FSKILL.md",[],[50,58,65,73,81,89],{"skillNo":51,"name":52,"nameZh":6,"description":53,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":54,"licenseFileUrl":6,"sourceHtmlUrl":55,"categories":56,"keywords":57,"publishedAt":19},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"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":64,"publishedAt":19},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":69,"sourceHtmlUrl":70,"categories":71,"keywords":72,"publishedAt":19},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":77,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":19},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":85,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":19},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":90,"name":91,"nameZh":6,"description":6,"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":94,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]