[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QXZ2JBW36ESB3ZG1D107DF":3,"skill-same:01M6QXZ2JBW36ESB3ZG1D107DF":92},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M6QXZ2JBW36ESB3ZG1D107DF","tensorrt-llm",null,"Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100\u002FH100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8\u002FINT4), in-flight batching, and multi-GPU scaling.","Mishit18\u002Fclaude-code-templates","Mishit18","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F244127857?v=4","01M6QXBYKWM76HSKYDNWSKKE41","Mishit Sharma","01M6QXBYRJ0CAVM51S1RTEKTZZ",1,"MIT","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-tensorrt-llm\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"tensorrt","tensorrt llm","latency","when","batching","for","orchestra","you","with","throughput","H100","pytorch","scaling","use","llm inference","Inference Serving","NVIDIA","FP8","Optimization","deployment","2026-11-10 10:12:30",false,"---\nname: tensorrt-llm\ndescription: Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100\u002FH100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8\u002FINT4), in-flight batching, and multi-GPU scaling.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Inference Serving, TensorRT-LLM, NVIDIA, Inference Optimization, High Throughput, Low Latency, Production, FP8, INT4, In-Flight Batching, Multi-GPU]\ndependencies: [tensorrt-llm, torch]\n---\n\n# TensorRT-LLM\n\nNVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs.\n\n## When to use TensorRT-LLM\n\n**Use TensorRT-LLM when:**\n- Deploying on NVIDIA GPUs (A100, H100, GB200)\n- Need maximum throughput (24,000+ tokens\u002Fsec on Llama 3)\n- Require low latency for real-time applications\n- Working with quantized models (FP8, INT4, FP4)\n- Scaling across multiple GPUs or nodes\n\n**Use vLLM instead when:**\n- Need simpler setup and Python-first API\n- Want PagedAttention without TensorRT compilation\n- Working with AMD GPUs or non-NVIDIA hardware\n\n**Use llama.cpp instead when:**\n- Deploying on CPU or Apple Silicon\n- Need edge deployment without NVIDIA GPUs\n- Want simpler GGUF quantization format\n\n## Quick start\n\n### Installation\n\n```bash\n# Docker (recommended)\ndocker pull nvidia\u002Ftensorrt_llm:latest\n\n# pip install\npip install tensorrt_llm==1.2.0rc3\n\n# Requires CUDA 13.0.0, TensorRT 10.13.2, Python 3.10-3.12\n```\n\n### Basic inference\n\n```python\nfrom tensorrt_llm import LLM, SamplingParams\n\n# Initialize model\nllm = LLM(model=\"meta-llama\u002FMeta-Llama-3-8B\")\n\n# Configure sampling\nsampling_params = SamplingParams(\n    max_tokens=100,\n    temperature=0.7,\n    top_p=0.9\n)\n\n# Generate\nprompts = [\"Explain quantum computing\"]\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n    print(output.text)\n```\n\n### Serving with trtllm-serve\n\n```bash\n# Start server (automatic model download and compilation)\ntrtllm-serve meta-llama\u002FMeta-Llama-3-8B \\\n    --tp_size 4 \\              # Tensor parallelism (4 GPUs)\n    --max_batch_size 256 \\\n    --max_num_tokens 4096\n\n# Client request\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"meta-llama\u002FMeta-Llama-3-8B\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Key features\n\n### Performance optimizations\n- **In-flight batching**: Dynamic batching during generation\n- **Paged KV cache**: Efficient memory management\n- **Flash Attention**: Optimized attention kernels\n- **Quantization**: FP8, INT4, FP4 for 2-4× faster inference\n- **CUDA graphs**: Reduced kernel launch overhead\n\n### Parallelism\n- **Tensor parallelism (TP)**: Split model across GPUs\n- **Pipeline parallelism (PP)**: Layer-wise distribution\n- **Expert parallelism**: For Mixture-of-Experts models\n- **Multi-node**: Scale beyond single machine\n\n### Advanced features\n- **Speculative decoding**: Faster generation with draft models\n- **LoRA serving**: Efficient multi-adapter deployment\n- **Disaggregated serving**: Separate prefill and generation\n\n## Common patterns\n\n### Quantized model (FP8)\n\n```python\nfrom tensorrt_llm import LLM\n\n# Load FP8 quantized model (2× faster, 50% memory)\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-70B\",\n    dtype=\"fp8\",\n    max_num_tokens=8192\n)\n\n# Inference same as before\noutputs = llm.generate([\"Summarize this article...\"])\n```\n\n### Multi-GPU deployment\n\n```python\n# Tensor parallelism across 8 GPUs\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-405B\",\n    tensor_parallel_size=8,\n    dtype=\"fp8\"\n)\n```\n\n### Batch inference\n\n```python\n# Process 100 prompts efficiently\nprompts = [f\"Question {i}: ...\" for i in range(100)]\n\noutputs = llm.generate(\n    prompts,\n    sampling_params=SamplingParams(max_tokens=200)\n)\n\n# Automatic in-flight batching for maximum throughput\n```\n\n## Performance benchmarks\n\n**Meta Llama 3-8B** (H100 GPU):\n- Throughput: 24,000 tokens\u002Fsec\n- Latency: ~10ms per token\n- vs PyTorch: **100× faster**\n\n**Llama 3-70B** (8× A100 80GB):\n- FP8 quantization: 2× faster than FP16\n- Memory: 50% reduction with FP8\n\n## Supported models\n\n- **LLaMA family**: Llama 2, Llama 3, CodeLlama\n- **GPT family**: GPT-2, GPT-J, GPT-NeoX\n- **Qwen**: Qwen, Qwen2, QwQ\n- **DeepSeek**: DeepSeek-V2, DeepSeek-V3\n- **Mixtral**: Mixtral-8x7B, Mixtral-8x22B\n- **Vision**: LLaVA, Phi-3-vision\n- **100+ models** on HuggingFace\n\n## References\n\n- **[Optimization Guide](references\u002Foptimization.md)** - Quantization, batching, KV cache tuning\n- **[Multi-GPU Setup](references\u002Fmulti-gpu.md)** - Tensor\u002Fpipeline parallelism, multi-node\n- **[Serving Guide](references\u002Fserving.md)** - Production deployment, monitoring, autoscaling\n\n## Resources\n\n- **Docs**: https:\u002F\u002Fnvidia.github.io\u002FTensorRT-LLM\u002F\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=tensorrt_llm\n\n\n\n","# TensorRT-LLM\n\nNVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs.\n\n## When to use TensorRT-LLM\n\n**Use TensorRT-LLM when:**\n- Deploying on NVIDIA GPUs (A100, H100, GB200)\n- Need maximum throughput (24,000+ tokens\u002Fsec on Llama 3)\n- Require low latency for real-time applications\n- Working with quantized models (FP8, INT4, FP4)\n- Scaling across multiple GPUs or nodes\n\n**Use vLLM instead when:**\n- Need simpler setup and Python-first API\n- Want PagedAttention without TensorRT compilation\n- Working with AMD GPUs or non-NVIDIA hardware\n\n**Use llama.cpp instead when:**\n- Deploying on CPU or Apple Silicon\n- Need edge deployment without NVIDIA GPUs\n- Want simpler GGUF quantization format\n\n## Quick start\n\n### Installation\n\n```bash\n# Docker (recommended)\ndocker pull nvidia\u002Ftensorrt_llm:latest\n\n# pip install\npip install tensorrt_llm==1.2.0rc3\n\n# Requires CUDA 13.0.0, TensorRT 10.13.2, Python 3.10-3.12\n```\n\n### Basic inference\n\n```python\nfrom tensorrt_llm import LLM, SamplingParams\n\n# Initialize model\nllm = LLM(model=\"meta-llama\u002FMeta-Llama-3-8B\")\n\n# Configure sampling\nsampling_params = SamplingParams(\n    max_tokens=100,\n    temperature=0.7,\n    top_p=0.9\n)\n\n# Generate\nprompts = [\"Explain quantum computing\"]\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n    print(output.text)\n```\n\n### Serving with trtllm-serve\n\n```bash\n# Start server (automatic model download and compilation)\ntrtllm-serve meta-llama\u002FMeta-Llama-3-8B \\\n    --tp_size 4 \\              # Tensor parallelism (4 GPUs)\n    --max_batch_size 256 \\\n    --max_num_tokens 4096\n\n# Client request\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"meta-llama\u002FMeta-Llama-3-8B\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Key features\n\n### Performance optimizations\n- **In-flight batching**: Dynamic batching during generation\n- **Paged KV cache**: Efficient memory management\n- **Flash Attention**: Optimized attention kernels\n- **Quantization**: FP8, INT4, FP4 for 2-4× faster inference\n- **CUDA graphs**: Reduced kernel launch overhead\n\n### Parallelism\n- **Tensor parallelism (TP)**: Split model across GPUs\n- **Pipeline parallelism (PP)**: Layer-wise distribution\n- **Expert parallelism**: For Mixture-of-Experts models\n- **Multi-node**: Scale beyond single machine\n\n### Advanced features\n- **Speculative decoding**: Faster generation with draft models\n- **LoRA serving**: Efficient multi-adapter deployment\n- **Disaggregated serving**: Separate prefill and generation\n\n## Common patterns\n\n### Quantized model (FP8)\n\n```python\nfrom tensorrt_llm import LLM\n\n# Load FP8 quantized model (2× faster, 50% memory)\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-70B\",\n    dtype=\"fp8\",\n    max_num_tokens=8192\n)\n\n# Inference same as before\noutputs = llm.generate([\"Summarize this article...\"])\n```\n\n### Multi-GPU deployment\n\n```python\n# Tensor parallelism across 8 GPUs\nllm = LLM(\n    model=\"meta-llama\u002FMeta-Llama-3-405B\",\n    tensor_parallel_size=8,\n    dtype=\"fp8\"\n)\n```\n\n### Batch inference\n\n```python\n# Process 100 prompts efficiently\nprompts = [f\"Question {i}: ...\" for i in range(100)]\n\noutputs = llm.generate(\n    prompts,\n    sampling_params=SamplingParams(max_tokens=200)\n)\n\n# Automatic in-flight batching for maximum throughput\n```\n\n## Performance benchmarks\n\n**Meta Llama 3-8B** (H100 GPU):\n- Throughput: 24,000 tokens\u002Fsec\n- Latency: ~10ms per token\n- vs PyTorch: **100× faster**\n\n**Llama 3-70B** (8× A100 80GB):\n- FP8 quantization: 2× faster than FP16\n- Memory: 50% reduction with FP8\n\n## Supported models\n\n- **LLaMA family**: Llama 2, Llama 3, CodeLlama\n- **GPT family**: GPT-2, GPT-J, GPT-NeoX\n- **Qwen**: Qwen, Qwen2, QwQ\n- **DeepSeek**: DeepSeek-V2, DeepSeek-V3\n- **Mixtral**: Mixtral-8x7B, Mixtral-8x22B\n- **Vision**: LLaVA, Phi-3-vision\n- **100+ models** on HuggingFace\n\n## References\n\n- **[Optimization Guide](references\u002Foptimization.md)** - Quantization, batching, KV cache tuning\n- **[Multi-GPU Setup](references\u002Fmulti-gpu.md)** - Tensor\u002Fpipeline parallelism, multi-node\n- **[Serving Guide](references\u002Fserving.md)** - Production deployment, monitoring, autoscaling\n\n## Resources\n\n- **Docs**: https:\u002F\u002Fnvidia.github.io\u002FTensorRT-LLM\u002F\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=tensorrt_llm\n\n\n\n",{"name":5,"tags":44,"author":53,"license":15,"version":54,"description":7,"dependencies":55},[34,45,35,46,47,48,49,36,50,51,52],"TensorRT-LLM","Inference Optimization","High Throughput","Low Latency","Production","INT4","In-Flight Batching","Multi-GPU","Orchestra Research","1.0.0",[5,56],"torch","main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-tensorrt-llm",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-11 17:58:24",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill tensorrt-llm",{"tool":70,"command":71},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill tensorrt-llm",{"tool":73,"command":74},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill tensorrt-llm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-tensorrt-llm\u002FSKILL.md",true,[78,84,88],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fmulti-gpu.md","REFERENCE","参考文档",6700,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-tensorrt-llm\u002Freferences\u002Fmulti-gpu.md",{"path":85,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Foptimization.md",5614,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-tensorrt-llm\u002Freferences\u002Foptimization.md",{"path":89,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fserving.md",9799,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-tensorrt-llm\u002Freferences\u002Fserving.md",[93,125,150,177,204,229],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":98,"sourceHtmlUrl":99,"categories":100,"keywords":106,"publishedAt":39},"01M6R4ARN4W2VZRDERDTMN1KP0","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[101],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[107,108,109,110,111,112,113,114,115,116,117,118,119,120,32,121,122,123,24,124],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","routing","state management","state","file","Tailwind","single-file","ui components","multi","single",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":129,"sourceHtmlUrl":130,"categories":131,"keywords":136,"publishedAt":39},"01M6R45GXREM5DP8T08MAC788H","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[132,135],{"categoryNo":133,"categoryName":134,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},[137,138,139,112,140,141,108,142,143,144,145,146,147,148,149,27,24],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","ui","logs","applications","behavior","local",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":154,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":39},"01M6R2GYJ470X8EB7R58V0B68K","think-tank","Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural\u002Fdesign\u002Fstrategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says \"think tank\", \"debate this\", \"perspectives on\", \"trade-offs\", \"should I use X or Y\", \"help me decide\", \"before we plan\", or asks for pros\u002Fcons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FSKILL.md",[],[158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,22],"think tank","think","design","run","feeds","analysis","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":181,"sourceHtmlUrl":182,"categories":183,"keywords":187,"publishedAt":39},"01M6R2FSR55AVRFA9YZ8SC2GZG","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[184],{"categoryNo":185,"categoryName":186,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000023","Skill与智能体开发",[188,189,190,191,192,193,194,195,196,197,198,199,200,201,27,24,202,22,203],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":210,"publishedAt":39},"01M6R2FK42ZZ4HJTJVVK9WJBBP","ship-learn-next","Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fship-learn-next\u002FSKILL.md",[],[211,212,213,214,215,216,217,218,32,219,220,221,222,223,224,225,226,227,228,22],"learn","ship learn next","ship","next","framework","learning","read","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":97,"licenseFileUrl":233,"sourceHtmlUrl":234,"categories":235,"keywords":242,"publishedAt":39},"01M6R2088G1RHDA5MDX16HASRB","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[236,239],{"categoryNo":237,"categoryName":238,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":240,"categoryName":241,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000016","图像与多媒体",[243,244,245,189,246,247,248,249,194,32,173,201,175,250,251,24,252,22],"slack gif creator","slack","gif","validation","Tools","constraints","Animation","Utilities","concepts","request"]