[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-huggingface-hf-cloud-python-env-setup":3,"mdc--cbkm96-key":36,"related-org-huggingface-hf-cloud-python-env-setup":948,"related-repo-huggingface-hf-cloud-python-env-setup":1113},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"hf-cloud-python-env-setup","set up Python environments for AWS","Set up an isolated Python environment for SageMaker \u002F AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to \"set up the environment\". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"huggingface","Hugging Face","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fhuggingface.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Python","python","tag",{"name":17,"slug":18,"type":15},"Engineering","engineering",{"name":20,"slug":21,"type":15},"AI Infrastructure","ai-infrastructure",{"name":23,"slug":24,"type":15},"AWS","aws",10861,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills","2026-07-08T05:55:32.505017",null,721,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":33},[],"Give your agents the power of the Hugging Face ecosystem","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fhf-cloud-python-env-setup","---\nname: hf-cloud-python-env-setup\ndescription: 'Set up an isolated Python environment for SageMaker \u002F AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to \"set up the environment\". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.'\n---\n\n# Python Environment Setup for SageMaker\n\nMost SageMaker deployment failures that look like AWS problems are actually Python environment problems: wrong Python version, broken dependency resolution, stale SDK that doesn't know about a current API. This skill makes env setup boring and correct.\n\n## Core rules\n\n1. **Never use the system Python.** Always work inside an isolated environment.\n2. **Pin the Python version, not the package versions.** Use 3.10, 3.11, or 3.12. Avoid 3.13+ — ML libraries lag on wheel availability and dependency resolution breaks in confusing ways.\n3. **Install the latest of each package.** Don't defensively pin `boto3` or `awscli`. Newer ones have current API surfaces and security fixes. Only pin if the user explicitly requires a specific version.\n4. **Check installed versions correctly.** Use `importlib.metadata.version(\"package-name\")`, never `module.__version__`. The latter is inconsistent across packages.\n5. **The bundled scripts use `boto3` directly.** The SageMaker Python SDK is a valid alternative — see \"boto3 vs the SageMaker SDK\" below.\n\n## boto3 vs the SageMaker SDK\n\nThe bundled deploy scripts (`deploy.py`, `deploy_async.py`, `teardown.py`) use `boto3` directly and read image URIs from [AWS's published Deep Learning Containers catalog](https:\u002F\u002Faws.github.io\u002Fdeep-learning-containers\u002Freference\u002Favailable_images\u002F). That fits this workflow's explicit-stages design — each skill produces a concrete value (region, role ARN, image URI) that the next one consumes — and `boto3` is the stable underlying API client.\n\nThe SageMaker Python SDK (v3) is fine to use when the user prefers it or their project already does. Since [PR #5960](https:\u002F\u002Fgithub.com\u002Faws\u002Fsagemaker-python-sdk\u002Fpull\u002F5960) (June 2026), `ModelBuilder` auto-routes HuggingFace models to the current containers (text-generation → HuggingFace vLLM, multimodal → vLLM-Omni, embeddings → TEI). Don't avoid the SDK over stale-image or wrong-container concerns — that routing is fixed.\n\nTwo specific SDK cases that still need care:\n\n- **Generative rerankers**: the SDK routes the `text-ranking` task to TEI unconditionally, which is wrong for causal-LM rerankers like Qwen3-Reranker — those need vLLM (see `hf-cloud-serving-image-selection`). Pass the container explicitly for these models.\n- **SSO assumed-role credentials**: v3 has had credential-resolution regressions in `ModelTrainer` \u002F `FrameworkProcessor` under SSO profiles. If SDK calls fail with credential errors while `aws sts get-caller-identity` succeeds in the same shell, suspect this rather than your AWS config.\n\nIf you use the SDK, install it into the isolated env like everything else (`.venv\u002Fbin\u002Fpython -m pip install sagemaker`). The bundled scripts don't require it.\n\n## How to set up\n\nThe fastest path is the bundled script — it's Python, so it runs the same on Windows, macOS, and Linux:\n\n```bash\npython3 scripts\u002Fsetup_env.py        # macOS \u002F Linux\npython  scripts\u002Fsetup_env.py        # Windows (PowerShell \u002F cmd)\n```\n\nThis script detects `uv` and uses it if available (faster), falls back to the stdlib `venv` module, creates `.venv\u002F` with Python 3.12 (override: `python3 setup_env.py .venv 3.11`), refuses unsupported Python versions, installs from the bundled `requirements.txt`, and is idempotent. It also prints the correct interpreter path for the host OS (see below).\n\nManual equivalent:\n\n```bash\n# Preferred: uv\nuv venv --python 3.12 .venv\nuv pip install --python .venv\u002Fbin\u002Fpython --upgrade boto3 awscli   # Windows: .venv\\Scripts\\python.exe\n\n# Fallback: stdlib venv\npython3.12 -m venv .venv\n.venv\u002Fbin\u002Fpython -m pip install --upgrade pip boto3 awscli\n```\n\nAfter setup, **invoke the env's Python explicitly** rather than activating the venv. The interpreter path differs by platform:\n\n```bash\n.venv\u002Fbin\u002Fpython deploy.py            # macOS \u002F Linux\n.venv\\Scripts\\python.exe deploy.py    # Windows\n```\n\nThis works the same in scripts, interactive shells, and agent tool calls. The rest of this skill writes `.venv\u002Fbin\u002Fpython` for brevity — on Windows substitute `.venv\\Scripts\\python.exe`.\n\n## Verifying\n\n```bash\n.venv\u002Fbin\u002Fpython scripts\u002Fcheck_versions.py\n```\n\nPrints versions of `boto3`, `botocore`, `awscli`. Uses `importlib.metadata.version()` so it works on every package, including ones without `__version__`. Pass arbitrary names: `... check_versions.py transformers huggingface_hub`.\n\n## Deployment-specific extras\n\nDefault `requirements.txt` covers SageMaker orchestration. Some deployments need extras (`huggingface_hub` for model inspection, `transformers` for tokenizer validation). Add these to a deployment-specific requirements file in the project, install with the env's Python, don't pin unless there's a reason.\n\n## Common pitfalls\n\n**Mysterious `pip install` resolution errors**\nAlmost always Python 3.13+ trying to install packages without wheels yet, or installing into a polluted system Python. Recreate at 3.12: delete `.venv` and re-run `python3 setup_env.py .venv 3.12` (the script recreates the env when the version doesn't match, so you can also just re-run it).\n\n**`pip install` succeeded but the script says \"module not found\"**\nYou installed into a different interpreter than the one running the script. Always invoke Python explicitly: `.venv\u002Fbin\u002Fpython -m pip install ...` and `.venv\u002Fbin\u002Fpython deploy.py`.\n\n**Inline `python -c \"...\"` one-liners fail in PowerShell**\nPowerShell's quoting rules mangle nested\u002Fescaped quotes in inline Python. Don't debug the quoting — write the snippet to a small `.py` file and run that. (All bundled helpers are files for exactly this reason.)\n\n**boto3 call fails with \"unknown parameter\"**\nYour boto3 is older than the API surface. Upgrade with `.venv\u002Fbin\u002Fpython -m pip install --upgrade boto3`. Don't downgrade the script to match an old version.\n\n**`sagemaker` (the SDK) installed but the bundled scripts fail**\nThe bundled scripts don't use the SDK — they only need `boto3`\u002F`awscli` from `requirements.txt`. Installing `sagemaker` alongside is harmless, but it doesn't replace the requirements install.\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,50,56,63,159,165,218,240,245,309,322,328,333,386,431,436,605,617,658,677,683,702,750,756,784,790,824,854,880,898,942],{"type":42,"tag":43,"props":44,"children":46},"element","h1",{"id":45},"python-environment-setup-for-sagemaker",[47],{"type":48,"value":49},"text","Python Environment Setup for SageMaker",{"type":42,"tag":51,"props":52,"children":53},"p",{},[54],{"type":48,"value":55},"Most SageMaker deployment failures that look like AWS problems are actually Python environment problems: wrong Python version, broken dependency resolution, stale SDK that doesn't know about a current API. This skill makes env setup boring and correct.",{"type":42,"tag":57,"props":58,"children":60},"h2",{"id":59},"core-rules",[61],{"type":48,"value":62},"Core rules",{"type":42,"tag":64,"props":65,"children":66},"ol",{},[67,79,89,116,142],{"type":42,"tag":68,"props":69,"children":70},"li",{},[71,77],{"type":42,"tag":72,"props":73,"children":74},"strong",{},[75],{"type":48,"value":76},"Never use the system Python.",{"type":48,"value":78}," Always work inside an isolated environment.",{"type":42,"tag":68,"props":80,"children":81},{},[82,87],{"type":42,"tag":72,"props":83,"children":84},{},[85],{"type":48,"value":86},"Pin the Python version, not the package versions.",{"type":48,"value":88}," Use 3.10, 3.11, or 3.12. Avoid 3.13+ — ML libraries lag on wheel availability and dependency resolution breaks in confusing ways.",{"type":42,"tag":68,"props":90,"children":91},{},[92,97,99,106,108,114],{"type":42,"tag":72,"props":93,"children":94},{},[95],{"type":48,"value":96},"Install the latest of each package.",{"type":48,"value":98}," Don't defensively pin ",{"type":42,"tag":100,"props":101,"children":103},"code",{"className":102},[],[104],{"type":48,"value":105},"boto3",{"type":48,"value":107}," or ",{"type":42,"tag":100,"props":109,"children":111},{"className":110},[],[112],{"type":48,"value":113},"awscli",{"type":48,"value":115},". Newer ones have current API surfaces and security fixes. Only pin if the user explicitly requires a specific version.",{"type":42,"tag":68,"props":117,"children":118},{},[119,124,126,132,134,140],{"type":42,"tag":72,"props":120,"children":121},{},[122],{"type":48,"value":123},"Check installed versions correctly.",{"type":48,"value":125}," Use ",{"type":42,"tag":100,"props":127,"children":129},{"className":128},[],[130],{"type":48,"value":131},"importlib.metadata.version(\"package-name\")",{"type":48,"value":133},", never ",{"type":42,"tag":100,"props":135,"children":137},{"className":136},[],[138],{"type":48,"value":139},"module.__version__",{"type":48,"value":141},". The latter is inconsistent across packages.",{"type":42,"tag":68,"props":143,"children":144},{},[145,157],{"type":42,"tag":72,"props":146,"children":147},{},[148,150,155],{"type":48,"value":149},"The bundled scripts use ",{"type":42,"tag":100,"props":151,"children":153},{"className":152},[],[154],{"type":48,"value":105},{"type":48,"value":156}," directly.",{"type":48,"value":158}," The SageMaker Python SDK is a valid alternative — see \"boto3 vs the SageMaker SDK\" below.",{"type":42,"tag":57,"props":160,"children":162},{"id":161},"boto3-vs-the-sagemaker-sdk",[163],{"type":48,"value":164},"boto3 vs the SageMaker SDK",{"type":42,"tag":51,"props":166,"children":167},{},[168,170,176,178,184,185,191,193,198,200,209,211,216],{"type":48,"value":169},"The bundled deploy scripts (",{"type":42,"tag":100,"props":171,"children":173},{"className":172},[],[174],{"type":48,"value":175},"deploy.py",{"type":48,"value":177},", ",{"type":42,"tag":100,"props":179,"children":181},{"className":180},[],[182],{"type":48,"value":183},"deploy_async.py",{"type":48,"value":177},{"type":42,"tag":100,"props":186,"children":188},{"className":187},[],[189],{"type":48,"value":190},"teardown.py",{"type":48,"value":192},") use ",{"type":42,"tag":100,"props":194,"children":196},{"className":195},[],[197],{"type":48,"value":105},{"type":48,"value":199}," directly and read image URIs from ",{"type":42,"tag":201,"props":202,"children":206},"a",{"href":203,"rel":204},"https:\u002F\u002Faws.github.io\u002Fdeep-learning-containers\u002Freference\u002Favailable_images\u002F",[205],"nofollow",[207],{"type":48,"value":208},"AWS's published Deep Learning Containers catalog",{"type":48,"value":210},". That fits this workflow's explicit-stages design — each skill produces a concrete value (region, role ARN, image URI) that the next one consumes — and ",{"type":42,"tag":100,"props":212,"children":214},{"className":213},[],[215],{"type":48,"value":105},{"type":48,"value":217}," is the stable underlying API client.",{"type":42,"tag":51,"props":219,"children":220},{},[221,223,230,232,238],{"type":48,"value":222},"The SageMaker Python SDK (v3) is fine to use when the user prefers it or their project already does. Since ",{"type":42,"tag":201,"props":224,"children":227},{"href":225,"rel":226},"https:\u002F\u002Fgithub.com\u002Faws\u002Fsagemaker-python-sdk\u002Fpull\u002F5960",[205],[228],{"type":48,"value":229},"PR #5960",{"type":48,"value":231}," (June 2026), ",{"type":42,"tag":100,"props":233,"children":235},{"className":234},[],[236],{"type":48,"value":237},"ModelBuilder",{"type":48,"value":239}," auto-routes HuggingFace models to the current containers (text-generation → HuggingFace vLLM, multimodal → vLLM-Omni, embeddings → TEI). Don't avoid the SDK over stale-image or wrong-container concerns — that routing is fixed.",{"type":42,"tag":51,"props":241,"children":242},{},[243],{"type":48,"value":244},"Two specific SDK cases that still need care:",{"type":42,"tag":246,"props":247,"children":248},"ul",{},[249,275],{"type":42,"tag":68,"props":250,"children":251},{},[252,257,259,265,267,273],{"type":42,"tag":72,"props":253,"children":254},{},[255],{"type":48,"value":256},"Generative rerankers",{"type":48,"value":258},": the SDK routes the ",{"type":42,"tag":100,"props":260,"children":262},{"className":261},[],[263],{"type":48,"value":264},"text-ranking",{"type":48,"value":266}," task to TEI unconditionally, which is wrong for causal-LM rerankers like Qwen3-Reranker — those need vLLM (see ",{"type":42,"tag":100,"props":268,"children":270},{"className":269},[],[271],{"type":48,"value":272},"hf-cloud-serving-image-selection",{"type":48,"value":274},"). Pass the container explicitly for these models.",{"type":42,"tag":68,"props":276,"children":277},{},[278,283,285,291,293,299,301,307],{"type":42,"tag":72,"props":279,"children":280},{},[281],{"type":48,"value":282},"SSO assumed-role credentials",{"type":48,"value":284},": v3 has had credential-resolution regressions in ",{"type":42,"tag":100,"props":286,"children":288},{"className":287},[],[289],{"type":48,"value":290},"ModelTrainer",{"type":48,"value":292}," \u002F ",{"type":42,"tag":100,"props":294,"children":296},{"className":295},[],[297],{"type":48,"value":298},"FrameworkProcessor",{"type":48,"value":300}," under SSO profiles. If SDK calls fail with credential errors while ",{"type":42,"tag":100,"props":302,"children":304},{"className":303},[],[305],{"type":48,"value":306},"aws sts get-caller-identity",{"type":48,"value":308}," succeeds in the same shell, suspect this rather than your AWS config.",{"type":42,"tag":51,"props":310,"children":311},{},[312,314,320],{"type":48,"value":313},"If you use the SDK, install it into the isolated env like everything else (",{"type":42,"tag":100,"props":315,"children":317},{"className":316},[],[318],{"type":48,"value":319},".venv\u002Fbin\u002Fpython -m pip install sagemaker",{"type":48,"value":321},"). 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Never guess the region or account ID — always use this skill to read it from the local configuration first.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1006,1007,1008],{"name":23,"slug":24,"type":15},{"name":992,"slug":993,"type":15},{"name":1009,"slug":1010,"type":15},"Configuration","configuration","2026-07-08T05:55:33.716099",{"slug":4,"name":4,"fn":5,"description":6,"org":1013,"tags":1014,"stars":25,"repoUrl":26,"updatedAt":27},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1015,1016,1017,1018],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"slug":1020,"name":1020,"fn":1021,"description":1022,"org":1023,"tags":1024,"stars":25,"repoUrl":26,"updatedAt":1031},"hf-cloud-sagemaker-deployment-planner","plan model deployments to Amazon SageMaker","Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like \"deploy a model\", \"host this LLM on AWS\", \"serve this embedding model\", \"deploy a reranker\", \"deploy a text-to-image \u002F diffusion model\", \"host this for async inference\", \"create an endpoint\", \"serve my fine-tuned model\", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. \"I just want to get this running on AWS, you figure it out\"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image \u002F diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1025,1026,1027,1030],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1028,"slug":1029,"type":15},"Deployment","deployment",{"name":9,"slug":960,"type":15},"2026-07-08T05:55:37.387689",{"slug":1033,"name":1033,"fn":1034,"description":1035,"org":1036,"tags":1037,"stars":25,"repoUrl":26,"updatedAt":1046},"hf-cloud-sagemaker-iam-preflight","configure SageMaker IAM roles","Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1038,1039,1040,1043],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1041,"slug":1042,"type":15},"Permissions","permissions",{"name":1044,"slug":1045,"type":15},"Security","security","2026-07-08T05:55:34.948771",{"slug":1048,"name":1048,"fn":1049,"description":1050,"org":1051,"tags":1052,"stars":25,"repoUrl":26,"updatedAt":1060},"hf-cloud-sagemaker-production-defaults","create production-ready SageMaker endpoints","Create a SageMaker endpoint (real-time or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints and deploy_async.py for async endpoints (with genuine scale-to-zero support). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1053,1054,1055,1056,1057],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1028,"slug":1029,"type":15},{"name":9,"slug":960,"type":15},{"name":1058,"slug":1059,"type":15},"Monitoring","monitoring","2026-07-08T05:55:38.664702",{"slug":272,"name":272,"fn":1062,"description":1063,"org":1064,"tags":1065,"stars":25,"repoUrl":26,"updatedAt":1069},"select SageMaker serving containers","Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says \"deploy this LLM\", \"host this HuggingFace model\", \"serve this fine-tuned model\", \"deploy this embedding model\", \"host a reranker\", \"serve a sentence-transformers model\", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings\u002Fcross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1066,1067,1068],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1028,"slug":1029,"type":15},"2026-07-08T05:55:36.173465",{"slug":1071,"name":1071,"fn":1072,"description":1073,"org":1074,"tags":1075,"stars":25,"repoUrl":26,"updatedAt":1082},"hf-mcp","access Hugging Face Hub via MCP","Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1076,1077,1078,1079],{"name":995,"slug":996,"type":15},{"name":9,"slug":960,"type":15},{"name":962,"slug":963,"type":15},{"name":1080,"slug":1081,"type":15},"MCP","mcp","2026-04-06T18:25:50.364185",{"slug":1084,"name":1084,"fn":1085,"description":1086,"org":1087,"tags":1088,"stars":25,"repoUrl":26,"updatedAt":1096},"hf-mem","estimate memory for Hugging Face models","Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1089,1090,1091,1092,1093],{"name":20,"slug":21,"type":15},{"name":992,"slug":993,"type":15},{"name":9,"slug":960,"type":15},{"name":962,"slug":963,"type":15},{"name":1094,"slug":1095,"type":15},"Performance","performance","2026-06-13T07:23:57.101435",{"slug":1098,"name":1098,"fn":1099,"description":1100,"org":1101,"tags":1102,"stars":25,"repoUrl":26,"updatedAt":1111},"huggingface-best","find and compare Hugging Face models","Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: \"best model for X\", \"what model should I use for\", \"top models for [task]\", \"which model runs on my laptop\u002Fmachine\u002Fdevice\", \"recommend a model for\", \"what LLM should I use for\", \"compare models for\", \"what's state of the art for\", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1103,1106,1107,1108],{"name":1104,"slug":1105,"type":15},"Analytics","analytics",{"name":9,"slug":960,"type":15},{"name":962,"slug":963,"type":15},{"name":1109,"slug":1110,"type":15},"Research","research","2026-04-24T05:09:45.870658",37,{"items":1114,"total":1163},[1115,1122,1128,1135,1142,1149,1157],{"slug":986,"name":986,"fn":987,"description":988,"org":1116,"tags":1117,"stars":25,"repoUrl":26,"updatedAt":999},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1118,1119,1120,1121],{"name":992,"slug":993,"type":15},{"name":995,"slug":996,"type":15},{"name":9,"slug":960,"type":15},{"name":962,"slug":963,"type":15},{"slug":1001,"name":1001,"fn":1002,"description":1003,"org":1123,"tags":1124,"stars":25,"repoUrl":26,"updatedAt":1011},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1125,1126,1127],{"name":23,"slug":24,"type":15},{"name":992,"slug":993,"type":15},{"name":1009,"slug":1010,"type":15},{"slug":4,"name":4,"fn":5,"description":6,"org":1129,"tags":1130,"stars":25,"repoUrl":26,"updatedAt":27},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1131,1132,1133,1134],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"slug":1020,"name":1020,"fn":1021,"description":1022,"org":1136,"tags":1137,"stars":25,"repoUrl":26,"updatedAt":1031},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1138,1139,1140,1141],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1028,"slug":1029,"type":15},{"name":9,"slug":960,"type":15},{"slug":1033,"name":1033,"fn":1034,"description":1035,"org":1143,"tags":1144,"stars":25,"repoUrl":26,"updatedAt":1046},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1145,1146,1147,1148],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1041,"slug":1042,"type":15},{"name":1044,"slug":1045,"type":15},{"slug":1048,"name":1048,"fn":1049,"description":1050,"org":1150,"tags":1151,"stars":25,"repoUrl":26,"updatedAt":1060},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1152,1153,1154,1155,1156],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1028,"slug":1029,"type":15},{"name":9,"slug":960,"type":15},{"name":1058,"slug":1059,"type":15},{"slug":272,"name":272,"fn":1062,"description":1063,"org":1158,"tags":1159,"stars":25,"repoUrl":26,"updatedAt":1069},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1160,1161,1162],{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1028,"slug":1029,"type":15},24]