[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C4NGGP4AMNF3MCTGMHYNPB":3,"skill-same:01M6C4NGGP4AMNF3MCTGMHYNPB":180},{"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":25,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":13,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6C4NGGP4AMNF3MCTGMHYNPB","ai-multimodal",null,"Process and generate multimedia content using Google Gemini API for better vision capabilities. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music\u002Fsound analysis up to 9.5 hours), understand images (better image analysis than Claude models, captioning, reasoning, object detection, design extraction, OCR, visual Q&A, segmentation, handle multiple images), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image with Imagen 4, editing, composition, refinement), generate videos (text-to-video with Veo 3, 8-second clips with native audio). Use when working with audio\u002Fvideo files, analyzing images or screenshots (instead of default vision capabilities of Claude, only fallback to Claude's vision capabilities if needed), processing PDF documents, extracting structured data from media, creating images\u002Fvideos from text prompts, or implementing multimodal AI features. Supports Gemini 3\u002F2.5, Imagen 4, and Veo 3 models with context windows up to 2M tokens.","hungtrandigital\u002FResidentOne","hungtrandigital","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F25002353?v=4","01M6C49HS2DVC9NWE17SC8HMSJ","01M6C4NEXA9Y9VXK786C1JBATW",0,"MIT","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"ai multimodal","multimodal","Video","up","only","analyze","Bash","Charts","Audio","Media","Composition","PDF","api","analysis","segmentation","ocr","capabilities","documents","image","design","2026-11-10 10:12:30",false,"---\nname: ai-multimodal\ndescription: Process and generate multimedia content using Google Gemini API for better vision capabilities. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music\u002Fsound analysis up to 9.5 hours), understand images (better image analysis than Claude models, captioning, reasoning, object detection, design extraction, OCR, visual Q&A, segmentation, handle multiple images), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image with Imagen 4, editing, composition, refinement), generate videos (text-to-video with Veo 3, 8-second clips with native audio). Use when working with audio\u002Fvideo files, analyzing images or screenshots (instead of default vision capabilities of Claude, only fallback to Claude's vision capabilities if needed), processing PDF documents, extracting structured data from media, creating images\u002Fvideos from text prompts, or implementing multimodal AI features. Supports Gemini 3\u002F2.5, Imagen 4, and Veo 3 models with context windows up to 2M tokens.\nlicense: MIT\nallowed-tools:\n  - Bash\n  - Read\n  - Write\n  - Edit\n---\n\n# AI Multimodal\n\nProcess audio, images, videos, documents, and generate images\u002Fvideos using Google Gemini's multimodal API.\n\n## Setup\n\n```bash\nexport GEMINI_API_KEY=\"your-key\"  # Get from https:\u002F\u002Faistudio.google.com\u002Fapikey\npip install google-genai python-dotenv pillow\n```\n\n## Quick Start\n\n**Verify setup**: `python scripts\u002Fcheck_setup.py`\n**Analyze media**: `python scripts\u002Fgemini_batch_process.py --files \u003Cfile> --task \u003Canalyze|transcribe|extract>`\n  - TIP: When you're asked to analyze an image, check if `gemini` command is available, then use `\"\u003Cprompt to analyze image>\" | gemini -y -m gemini-2.5-flash` command. If `gemini` command is not available, use `python scripts\u002Fgemini_batch_process.py --files \u003Cfile> --task analyze` command.\n**Generate content**: `python scripts\u002Fgemini_batch_process.py --task \u003Cgenerate|generate-video> --prompt \"description\"`\n\n> **Stdin support**: You can pipe files directly via stdin (auto-detects PNG\u002FJPG\u002FPDF\u002FWAV\u002FMP3).\n> - `cat image.png | python scripts\u002Fgemini_batch_process.py --task analyze --prompt \"Describe this\"`\n> - `python scripts\u002Fgemini_batch_process.py --files image.png --task analyze` (traditional)\n\n## Models\n\n- **Image generation**: `imagen-4.0-generate-001` (standard), `imagen-4.0-ultra-generate-001` (quality), `imagen-4.0-fast-generate-001` (speed)\n- **Video generation**: `veo-3.1-generate-preview` (8s clips with audio)\n- **Analysis**: `gemini-2.5-flash` (recommended), `gemini-2.5-pro` (advanced)\n\n## Scripts\n\n- **`gemini_batch_process.py`**: CLI orchestrator for `transcribe|analyze|extract|generate|generate-video` that auto-resolves API keys, picks sensible default models per task, streams files inline vs File API, and saves structured outputs (text\u002FJSON\u002FCSV\u002Fmarkdown plus generated assets) for Imagen 4 + Veo workflows.\n- **`media_optimizer.py`**: ffmpeg\u002FPillow-based preflight tool that compresses\u002Fresizes\u002Fconverts audio, image, and video inputs, enforces target sizes\u002Fbitrates, splits long clips into hour chunks, and batch-processes directories so media stays within Gemini limits.\n- **`document_converter.py`**: Gemini-powered converter that uploads PDFs\u002Fimages\u002FOffice docs, applies a markdown-preserving prompt, batches multiple files, auto-names outputs under `docs\u002Fassets`, and exposes CLI flags for model, prompt, auto-file naming, and verbose logging.\n- **`check_setup.py`**: Interactive readiness checker that verifies directory layout, centralized env resolver, required Python deps, and GEMINI_API_KEY availability\u002Fformat, then performs a live Gemini API call and prints remediation instructions if anything fails.\n\nUse `--help` for options.\n\n## References\n\nLoad for detailed guidance:\n\n| Topic | File | Description |\n|-------|------|-------------|\n| Audio | `references\u002Faudio-processing.md` | Audio formats and limits, transcription (timestamps, speakers, segments), non-speech analysis, File API vs inline input, TTS models, best practices, cost and token math, and concrete meeting\u002Fpodcast\u002Finterview recipes. |\n| Images | `references\u002Fvision-understanding.md` | Vision capabilities overview, supported formats and models, captioning\u002Fclassification\u002FVQA, detection and segmentation, OCR and document reading, multi-image workflows, structured JSON output, token costs, best practices, and common product\u002Fscreenshot\u002Fchart\u002Fscene use cases. |\n| Image Gen | `references\u002Fimage-generation.md` | Imagen 4 and Gemini image model overview, generate_images vs generate_content APIs, aspect ratios and costs, text\u002Fimage\u002Fboth modalities, editing and composition, style and quality control, safety settings, best practices, troubleshooting, and common marketing\u002Fconcept-art\u002FUI scenarios. |\n| Video | `references\u002Fvideo-analysis.md` | Video analysis capabilities and supported formats, model\u002Fcontext choices, local\u002Finline\u002FYouTube inputs, clipping and FPS control, multi-video comparison, temporal Q&A and scene detection, transcription with visual context, token and cost guidance, and optimization\u002Fbest-practice patterns. |\n| Video Gen | `references\u002Fvideo-generation.md` | Veo model matrix, text-to-video and image-to-video quick start, multi-reference and extension flows, camera and timing control, configuration (resolution, aspect, audio, safety), prompt design patterns, performance tips, limitations, troubleshooting, and cost estimates. |\n\n## Limits\n\n**Formats**: Audio (WAV\u002FMP3\u002FAAC, 9.5h), Images (PNG\u002FJPEG\u002FWEBP, 3.6k), Video (MP4\u002FMOV, 6h), PDF (1k pages)\n**Size**: 20MB inline, 2GB File API\n\n## Resources\n\n- [API Docs](https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002F)\n- [Pricing](https:\u002F\u002Fai.google.dev\u002Fpricing)\n","# AI Multimodal\n\nProcess audio, images, videos, documents, and generate images\u002Fvideos using Google Gemini's multimodal API.\n\n## Setup\n\n```bash\nexport GEMINI_API_KEY=\"your-key\"  # Get from https:\u002F\u002Faistudio.google.com\u002Fapikey\npip install google-genai python-dotenv pillow\n```\n\n## Quick Start\n\n**Verify setup**: `python scripts\u002Fcheck_setup.py`\n**Analyze media**: `python scripts\u002Fgemini_batch_process.py --files \u003Cfile> --task \u003Canalyze|transcribe|extract>`\n  - TIP: When you're asked to analyze an image, check if `gemini` command is available, then use `\"\u003Cprompt to analyze image>\" | gemini -y -m gemini-2.5-flash` command. If `gemini` command is not available, use `python scripts\u002Fgemini_batch_process.py --files \u003Cfile> --task analyze` command.\n**Generate content**: `python scripts\u002Fgemini_batch_process.py --task \u003Cgenerate|generate-video> --prompt \"description\"`\n\n> **Stdin support**: You can pipe files directly via stdin (auto-detects PNG\u002FJPG\u002FPDF\u002FWAV\u002FMP3).\n> - `cat image.png | python scripts\u002Fgemini_batch_process.py --task analyze --prompt \"Describe this\"`\n> - `python scripts\u002Fgemini_batch_process.py --files image.png --task analyze` (traditional)\n\n## Models\n\n- **Image generation**: `imagen-4.0-generate-001` (standard), `imagen-4.0-ultra-generate-001` (quality), `imagen-4.0-fast-generate-001` (speed)\n- **Video generation**: `veo-3.1-generate-preview` (8s clips with audio)\n- **Analysis**: `gemini-2.5-flash` (recommended), `gemini-2.5-pro` (advanced)\n\n## Scripts\n\n- **`gemini_batch_process.py`**: CLI orchestrator for `transcribe|analyze|extract|generate|generate-video` that auto-resolves API keys, picks sensible default models per task, streams files inline vs File API, and saves structured outputs (text\u002FJSON\u002FCSV\u002Fmarkdown plus generated assets) for Imagen 4 + Veo workflows.\n- **`media_optimizer.py`**: ffmpeg\u002FPillow-based preflight tool that compresses\u002Fresizes\u002Fconverts audio, image, and video inputs, enforces target sizes\u002Fbitrates, splits long clips into hour chunks, and batch-processes directories so media stays within Gemini limits.\n- **`document_converter.py`**: Gemini-powered converter that uploads PDFs\u002Fimages\u002FOffice docs, applies a markdown-preserving prompt, batches multiple files, auto-names outputs under `docs\u002Fassets`, and exposes CLI flags for model, prompt, auto-file naming, and verbose logging.\n- **`check_setup.py`**: Interactive readiness checker that verifies directory layout, centralized env resolver, required Python deps, and GEMINI_API_KEY availability\u002Fformat, then performs a live Gemini API call and prints remediation instructions if anything fails.\n\nUse `--help` for options.\n\n## References\n\nLoad for detailed guidance:\n\n| Topic | File | Description |\n|-------|------|-------------|\n| Audio | `references\u002Faudio-processing.md` | Audio formats and limits, transcription (timestamps, speakers, segments), non-speech analysis, File API vs inline input, TTS models, best practices, cost and token math, and concrete meeting\u002Fpodcast\u002Finterview recipes. |\n| Images | `references\u002Fvision-understanding.md` | Vision capabilities overview, supported formats and models, captioning\u002Fclassification\u002FVQA, detection and segmentation, OCR and document reading, multi-image workflows, structured JSON output, token costs, best practices, and common product\u002Fscreenshot\u002Fchart\u002Fscene use cases. |\n| Image Gen | `references\u002Fimage-generation.md` | Imagen 4 and Gemini image model overview, generate_images vs generate_content APIs, aspect ratios and costs, text\u002Fimage\u002Fboth modalities, editing and composition, style and quality control, safety settings, best practices, troubleshooting, and common marketing\u002Fconcept-art\u002FUI scenarios. |\n| Video | `references\u002Fvideo-analysis.md` | Video analysis capabilities and supported formats, model\u002Fcontext choices, local\u002Finline\u002FYouTube inputs, clipping and FPS control, multi-video comparison, temporal Q&A and scene detection, transcription with visual context, token and cost guidance, and optimization\u002Fbest-practice patterns. |\n| Video Gen | `references\u002Fvideo-generation.md` | Veo model matrix, text-to-video and image-to-video quick start, multi-reference and extension flows, camera and timing control, configuration (resolution, aspect, audio, safety), prompt design patterns, performance tips, limitations, troubleshooting, and cost estimates. |\n\n## Limits\n\n**Formats**: Audio (WAV\u002FMP3\u002FAAC, 9.5h), Images (PNG\u002FJPEG\u002FWEBP, 3.6k), Video (MP4\u002FMOV, 6h), PDF (1k pages)\n**Size**: 20MB inline, 2GB File API\n\n## Resources\n\n- [API Docs](https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002F)\n- [Pricing](https:\u002F\u002Fai.google.dev\u002Fpricing)\n",{"name":5,"license":14,"description":7,"allowed-tools":51},[32,52,53,54],"Read","Write","Edit","main","89c86fd97855e6925d9bc26192f1e911bcbbb291","0-agents\u002Fagents\u002Fskills\u002Fai-multimodal","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne","https:\u002F\u002Fgithub.com\u002Fhungtrandigital","2025-12-15 11:00:17",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add hungtrandigital\u002FResidentOne --skill ai-multimodal",{"tool":67,"command":68},"bunx","bunx skills add hungtrandigital\u002FResidentOne --skill ai-multimodal",{"tool":70,"command":71},"pnpm","pnpm dlx skills add hungtrandigital\u002FResidentOne --skill ai-multimodal","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002FSKILL.md",true,[75,81,87,91,95,99,103,107,111,115,119,123,127,131,135,141,145,149,153,157,161,164,168,172,176],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},".env.example","OTHER","其他",7511,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002F.env.example",{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"manifest.json","REFERENCE","参考文档",165,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FPlatform\u002FOfficial-DotNet-Upgrade\u002Fskills\u002Fmigrate-dotnet10-to-dotnet11\u002Fmanifest.json",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Faspnetcore-dotnet10to11.md",1588,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FPlatform\u002FOfficial-DotNet-Upgrade\u002Fskills\u002Fmigrate-dotnet10-to-dotnet11\u002Freferences\u002Faspnetcore-dotnet10to11.md",{"path":92,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Faudio-processing.md",9752,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Freferences\u002Faudio-processing.md",{"path":96,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fcore-libraries-dotnet10to11.md",7038,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FPlatform\u002FOfficial-DotNet-Upgrade\u002Fskills\u002Fmigrate-dotnet10-to-dotnet11\u002Freferences\u002Fcore-libraries-dotnet10to11.md",{"path":100,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fcryptography-dotnet10to11.md",1826,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FPlatform\u002FOfficial-DotNet-Upgrade\u002Fskills\u002Fmigrate-dotnet10-to-dotnet11\u002Freferences\u002Fcryptography-dotnet10to11.md",{"path":104,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fcsharp-compiler-dotnet10to11.md",5431,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FPlatform\u002FOfficial-DotNet-Upgrade\u002Fskills\u002Fmigrate-dotnet10-to-dotnet11\u002Freferences\u002Fcsharp-compiler-dotnet10to11.md",{"path":108,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":109,"sourceHtmlUrl":110},"references\u002Fefcore-dotnet10to11.md",5039,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FPlatform\u002FOfficial-DotNet-Upgrade\u002Fskills\u002Fmigrate-dotnet10-to-dotnet11\u002Freferences\u002Fefcore-dotnet10to11.md",{"path":112,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":113,"sourceHtmlUrl":114},"references\u002Fimage-generation.md",18503,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Freferences\u002Fimage-generation.md",{"path":116,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":117,"sourceHtmlUrl":118},"references\u002Fruntime-jit-dotnet10to11.md",3371,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FPlatform\u002FOfficial-DotNet-Upgrade\u002Fskills\u002Fmigrate-dotnet10-to-dotnet11\u002Freferences\u002Fruntime-jit-dotnet10to11.md",{"path":120,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":121,"sourceHtmlUrl":122},"references\u002Fsdk-msbuild-dotnet10to11.md",1659,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FPlatform\u002FOfficial-DotNet-Upgrade\u002Fskills\u002Fmigrate-dotnet10-to-dotnet11\u002Freferences\u002Fsdk-msbuild-dotnet10to11.md",{"path":124,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":125,"sourceHtmlUrl":126},"references\u002Fvideo-analysis.md",11916,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Freferences\u002Fvideo-analysis.md",{"path":128,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":129,"sourceHtmlUrl":130},"references\u002Fvideo-generation.md",11094,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Freferences\u002Fvideo-generation.md",{"path":132,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":133,"sourceHtmlUrl":134},"references\u002Fvision-understanding.md",11040,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Freferences\u002Fvision-understanding.md",{"path":136,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":139,"sourceHtmlUrl":140},"scripts\u002F.coverage","SCRIPT","脚本",53248,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002F.coverage",{"path":142,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":143,"sourceHtmlUrl":144},"scripts\u002Fcheck_setup.py",8752,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Fcheck_setup.py",{"path":146,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":147,"sourceHtmlUrl":148},"scripts\u002Fdocument_converter.py",12495,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Fdocument_converter.py",{"path":150,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":151,"sourceHtmlUrl":152},"scripts\u002Fgemini_batch_process.py",38019,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Fgemini_batch_process.py",{"path":154,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":155,"sourceHtmlUrl":156},"scripts\u002Fmedia_optimizer.py",16550,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Fmedia_optimizer.py",{"path":158,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":159,"sourceHtmlUrl":160},"scripts\u002Frequirements.txt",442,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Frequirements.txt",{"path":162,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":139,"sourceHtmlUrl":163},"scripts\u002Ftests\u002F.coverage","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Ftests\u002F.coverage",{"path":165,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":166,"sourceHtmlUrl":167},"scripts\u002Ftests\u002Frequirements.txt",352,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Ftests\u002Frequirements.txt",{"path":169,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":170,"sourceHtmlUrl":171},"scripts\u002Ftests\u002Ftest_document_converter.py",2229,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Ftests\u002Ftest_document_converter.py",{"path":173,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":174,"sourceHtmlUrl":175},"scripts\u002Ftests\u002Ftest_gemini_batch_process.py",12677,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Ftests\u002Ftest_gemini_batch_process.py",{"path":177,"resourceType":137,"resourceTypeDesc":138,"sizeBytes":178,"sourceHtmlUrl":179},"scripts\u002Ftests\u002Ftest_media_optimizer.py",11824,"https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fai-multimodal\u002Fscripts\u002Ftests\u002Ftest_media_optimizer.py",[181,207,234,257,270,283],{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":187,"publishedAt":46},"01M6C4SJJB90YSQZT2MTSN0GC5","web-frameworks","Build modern full-stack web applications with Next.js (App Router, Server Components, RSC, PPR, SSR, SSG, ISR), Turborepo (monorepo management, task pipelines, remote caching, parallel execution), and RemixIcon (3100+ SVG icons in outlined\u002Ffilled styles). Use when creating React applications, implementing server-side rendering, setting up monorepos with multiple packages, optimizing build performance and caching strategies, adding icon libraries, managing shared dependencies, or working with TypeScript full-stack projects.","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fweb-frameworks\u002FSKILL.md",[],[188,189,190,191,192,193,194,29,195,196,197,198,199,200,201,202,203,204,205,206],"web frameworks","web","components","performance","react","projects","parallel execution","router","use","monorepo","parallel","stack","Next.js","applications","execution","server","icons","packages","libraries",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":221,"publishedAt":46},"01M6C4S5DJXAB94E33E98Y0KFW","ui-styling","Create beautiful, accessible user interfaces with shadcn\u002Fui components (built on Radix UI + Tailwind), Tailwind CSS utility-first styling, and canvas-based visual designs. Use when building user interfaces, implementing design systems, creating responsive layouts, adding accessible components (dialogs, dropdowns, forms, tables), customizing themes and colors, implementing dark mode, generating visual designs and posters, or establishing consistent styling patterns across applications.","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fui-styling\u002FSKILL.md",[213,216],{"categoryNo":214,"categoryName":215,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":217,"categoryName":218,"taxonomyType":219,"taxonomyTypeDesc":220},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[222,223,224,225,196,226,227,228,201,229,230,231,190,232,45,233],"styling","ui","ui styling","first","radix ui","user","Tailwind","shadcn","design systems","create","canvas","css",{"skillNo":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":238,"categories":239,"keywords":240,"publishedAt":46},"01M6C4S1SK4FH1200A3FBS7MVN","threejs","Build immersive 3D web experiences with Three.js - WebGL\u002FWebGPU library for scenes, cameras, geometries, materials, lights, animations, loaders, post-processing, shaders (including node-based TSL), compute, physics, VR\u002FXR, and advanced rendering. Use when creating 3D visualizations, games, interactive graphics, data viz, product configurators, architectural walkthroughs, or WebGL\u002FWebGPU applications. Covers OrbitControls, GLTF\u002FFBX loading, PBR materials, shadow mapping, post-processing effects (bloom, SSAO, SSR), custom shaders, instancing, LOD, animation systems, and WebXR.","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fthreejs\u002FSKILL.md",[],[236,189,241,242,243,244,245,246,247,248,249,250,251,201,252,253,254,255,256,196],"webxr","webgl","pbr","graphics","gltf","webgpu","ssr","shaders","product","library","data","Animation","3D","visualizations","node","interactive",{"skillNo":258,"name":259,"nameZh":6,"description":260,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":261,"categories":262,"keywords":263,"publishedAt":46},"01M6C4RP0SV44MC0YCNME88W0Q","sequential-thinking","Apply structured, reflective problem-solving for complex tasks requiring multi-step analysis, revision capability, and hypothesis verification. Use for complex problem decomposition, adaptive planning, analysis needing course correction, problems with unclear scope, multi-step solutions, and hypothesis-driven work.","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fsequential-thinking\u002FSKILL.md",[],[264,265,196,39,266,267,268,269],"sequential thinking","work","hypothesis","verification","planning","tasks",{"skillNo":271,"name":272,"nameZh":6,"description":273,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":274,"categories":275,"keywords":279,"publishedAt":46},"01M6C4RM60Z0B06MVD3GGVZZV5","research","Use when you need to research, analyze, and plan technical solutions that are scalable, secure, and maintainable.","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fresearch\u002FSKILL.md",[276],{"categoryNo":277,"categoryName":278,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",[272,280,196,281,31,282],"plan","technical","secure",{"skillNo":284,"name":268,"nameZh":6,"description":285,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":286,"categories":287,"keywords":288,"publishedAt":46},"01M6C4RBZJRKX6H9CQ9ABECXX0","Use when you need to plan technical solutions that are scalable, secure, and maintainable.","https:\u002F\u002Fgithub.com\u002Fhungtrandigital\u002FResidentOne\u002Fblob\u002Fmain\u002F0-agents\u002Fagents\u002Fskills\u002Fplanning\u002FSKILL.md",[],[268,280,281,196,282]]