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Optionally validate dataset availability with `\u002Fis-valid`.\r\n2. Resolve `config` + `split` with `\u002Fsplits`.\r\n3. Preview with `\u002Ffirst-rows`.\r\n4. Paginate content with `\u002Frows` using `offset` and `length` (max 100).\r\n5. Use `\u002Fsearch` for text matching and `\u002Ffilter` for row predicates.\r\n6. Retrieve parquet links via `\u002Fparquet` and totals\u002Fmetadata via `\u002Fsize` and `\u002Fstatistics`.\r\n\r\n## Defaults\r\n\r\n- Base URL: `https:\u002F\u002Fdatasets-server.huggingface.co`\r\n- Default API method: `GET`\r\n- Query params should be URL-encoded.\r\n- `offset` is 0-based.\r\n- `length` max is usually `100` for row-like endpoints.\r\n- Gated\u002Fprivate datasets require `Authorization: Bearer \u003CHF_TOKEN>`.\r\n\r\n## Dataset Viewer\r\n\r\n- `Validate dataset`: `\u002Fis-valid?dataset=\u003Cnamespace\u002Frepo>`\r\n- `List subsets and splits`: `\u002Fsplits?dataset=\u003Cnamespace\u002Frepo>`\r\n- `Preview first rows`: `\u002Ffirst-rows?dataset=\u003Cnamespace\u002Frepo>&config=\u003Cconfig>&split=\u003Csplit>`\r\n- `Paginate rows`: `\u002Frows?dataset=\u003Cnamespace\u002Frepo>&config=\u003Cconfig>&split=\u003Csplit>&offset=\u003Cint>&length=\u003Cint>`\r\n- `Search text`: `\u002Fsearch?dataset=\u003Cnamespace\u002Frepo>&config=\u003Cconfig>&split=\u003Csplit>&query=\u003Ctext>&offset=\u003Cint>&length=\u003Cint>`\r\n- `Filter with predicates`: `\u002Ffilter?dataset=\u003Cnamespace\u002Frepo>&config=\u003Cconfig>&split=\u003Csplit>&where=\u003Cpredicate>&orderby=\u003Csort>&offset=\u003Cint>&length=\u003Cint>`\r\n- `List parquet shards`: `\u002Fparquet?dataset=\u003Cnamespace\u002Frepo>`\r\n- `Get size totals`: `\u002Fsize?dataset=\u003Cnamespace\u002Frepo>`\r\n- `Get column statistics`: `\u002Fstatistics?dataset=\u003Cnamespace\u002Frepo>&config=\u003Cconfig>&split=\u003Csplit>`\r\n- `Get Croissant metadata (if available)`: `\u002Fcroissant?dataset=\u003Cnamespace\u002Frepo>`\r\n\r\nPagination pattern:\r\n\r\n```bash\r\ncurl \"https:\u002F\u002Fdatasets-server.huggingface.co\u002Frows?dataset=stanfordnlp\u002Fimdb&config=plain_text&split=train&offset=0&length=100\"\r\ncurl \"https:\u002F\u002Fdatasets-server.huggingface.co\u002Frows?dataset=stanfordnlp\u002Fimdb&config=plain_text&split=train&offset=100&length=100\"\r\n```\r\n\r\nWhen pagination is partial, use response fields such as `num_rows_total`, `num_rows_per_page`, and `partial` to drive continuation logic.\r\n\r\nSearch\u002Ffilter notes:\r\n\r\n- `\u002Fsearch` matches string columns (full-text style behavior is internal to the API).\r\n- `\u002Ffilter` requires predicate syntax in `where` and optional sort in `orderby`.\r\n- Keep filtering and searches read-only and side-effect free.\r\n\r\nFor CLI-based parquet URL discovery or SQL, use the `hf-cli` skill with `hf datasets parquet` and `hf datasets sql`.\r\n\r\n## Creating and Uploading Datasets\r\n\r\nUse one of these flows depending on dependency constraints.\r\n\r\nZero local dependencies (Hub UI):\r\n\r\n- Create dataset repo in browser: `https:\u002F\u002Fhuggingface.co\u002Fnew-dataset`\r\n- Upload parquet files in the repo \"Files and versions\" page.\r\n- Verify shards appear in Dataset Viewer:\r\n\r\n```bash\r\ncurl -s \"https:\u002F\u002Fdatasets-server.huggingface.co\u002Fparquet?dataset=\u003Cnamespace>\u002F\u003Crepo>\"\r\n```\r\n\r\nLow dependency CLI flow (`npx @huggingface\u002Fhub` \u002F `hfjs`):\r\n\r\n- Set auth token:\r\n\r\n```bash\r\nexport HF_TOKEN=\u003Cyour_hf_token>\r\n```\r\n\r\n- Upload parquet folder to a dataset repo (auto-creates repo if missing):\r\n\r\n```bash\r\nnpx -y @huggingface\u002Fhub upload datasets\u002F\u003Cnamespace>\u002F\u003Crepo> .\u002Flocal\u002Fparquet-folder data\r\n```\r\n\r\n- Upload as private repo on creation:\r\n\r\n```bash\r\nnpx -y @huggingface\u002Fhub upload datasets\u002F\u003Cnamespace>\u002F\u003Crepo> .\u002Flocal\u002Fparquet-folder data --private\r\n```\r\n\r\nAfter upload, call `\u002Fparquet` to discover `\u003Cconfig>\u002F\u003Csplit>\u002F\u003Cshard>` values for querying with `@~parquet`.\r\n\r\n## Agent Traces\r\n\r\nThe Hub supports raw agent session traces from Claude Code, Codex, and Pi Agent. Upload them to Hugging Face Datasets as original JSONL files and the Hub can auto-detect the trace format, tag the dataset as `Traces`, and enable the trace viewer for browsing sessions, turns, tool calls, and model responses. Common local session directories:\r\n\r\n- Claude Code: `~\u002F.claude\u002Fprojects`\r\n- Codex: `~\u002F.codex\u002Fsessions`\r\n- Pi: `~\u002F.pi\u002Fagent\u002Fsessions`\r\n\r\nDefault to private dataset repos because traces can contain prompts, file paths, tool outputs, secrets, or PII. 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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},[1258,1259,1260,1263],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1261,"slug":1262,"type":15},"Deployment","deployment",{"name":9,"slug":20,"type":15},"2026-07-08T05:55:37.387689",{"slug":1266,"name":1266,"fn":1267,"description":1268,"org":1269,"tags":1270,"stars":27,"repoUrl":28,"updatedAt":1279},"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},[1271,1272,1273,1276],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1274,"slug":1275,"type":15},"Permissions","permissions",{"name":1277,"slug":1278,"type":15},"Security","security","2026-07-08T05:55:34.948771",{"slug":1281,"name":1281,"fn":1282,"description":1283,"org":1284,"tags":1285,"stars":27,"repoUrl":28,"updatedAt":1293},"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},[1286,1287,1288,1289,1290],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1261,"slug":1262,"type":15},{"name":9,"slug":20,"type":15},{"name":1291,"slug":1292,"type":15},"Monitoring","monitoring","2026-07-08T05:55:38.664702",{"slug":1295,"name":1295,"fn":1296,"description":1297,"org":1298,"tags":1299,"stars":27,"repoUrl":28,"updatedAt":1303},"hf-cloud-serving-image-selection","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},[1300,1301,1302],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1261,"slug":1262,"type":15},"2026-07-08T05:55:36.173465",24,{"items":1306,"total":1427},[1307,1325,1339,1346,1352,1359,1366,1373,1381,1387,1400,1414],{"slug":1308,"name":1308,"fn":1309,"description":1310,"org":1311,"tags":1312,"stars":1322,"repoUrl":1323,"updatedAt":1324},"train-sentence-transformers","train sentence-transformers models","Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval \u002F pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1313,1316,1317,1318,1319],{"name":1314,"slug":1315,"type":15},"Deep Learning","deep-learning",{"name":9,"slug":20,"type":15},{"name":22,"slug":23,"type":15},{"name":1249,"slug":1250,"type":15},{"name":1320,"slug":1321,"type":15},"Search","search",18914,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fsentence-transformers","2026-05-08T05:09:16.820066",{"slug":1326,"name":1326,"fn":1327,"description":1328,"org":1329,"tags":1330,"stars":1336,"repoUrl":1337,"updatedAt":1338},"trl-training","train and fine-tune LLMs with TRL","Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1331,1332,1333,1334,1335],{"name":1242,"slug":1243,"type":15},{"name":1314,"slug":1315,"type":15},{"name":9,"slug":20,"type":15},{"name":22,"slug":23,"type":15},{"name":1249,"slug":1250,"type":15},18850,"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftrl","2026-04-06T18:25:32.746828",{"slug":601,"name":601,"fn":1210,"description":1211,"org":1340,"tags":1341,"stars":27,"repoUrl":28,"updatedAt":1220},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1342,1343,1344,1345],{"name":1215,"slug":1216,"type":15},{"name":17,"slug":18,"type":15},{"name":9,"slug":20,"type":15},{"name":22,"slug":23,"type":15},{"slug":1222,"name":1222,"fn":1223,"description":1224,"org":1347,"tags":1348,"stars":27,"repoUrl":28,"updatedAt":1234},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1349,1350,1351],{"name":1228,"slug":1229,"type":15},{"name":1215,"slug":1216,"type":15},{"name":1232,"slug":1233,"type":15},{"slug":1236,"name":1236,"fn":1237,"description":1238,"org":1353,"tags":1354,"stars":27,"repoUrl":28,"updatedAt":1251},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1355,1356,1357,1358],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1246,"slug":1247,"type":15},{"name":1249,"slug":1250,"type":15},{"slug":1253,"name":1253,"fn":1254,"description":1255,"org":1360,"tags":1361,"stars":27,"repoUrl":28,"updatedAt":1264},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1362,1363,1364,1365],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1261,"slug":1262,"type":15},{"name":9,"slug":20,"type":15},{"slug":1266,"name":1266,"fn":1267,"description":1268,"org":1367,"tags":1368,"stars":27,"repoUrl":28,"updatedAt":1279},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1369,1370,1371,1372],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1274,"slug":1275,"type":15},{"name":1277,"slug":1278,"type":15},{"slug":1281,"name":1281,"fn":1282,"description":1283,"org":1374,"tags":1375,"stars":27,"repoUrl":28,"updatedAt":1293},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1376,1377,1378,1379,1380],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1261,"slug":1262,"type":15},{"name":9,"slug":20,"type":15},{"name":1291,"slug":1292,"type":15},{"slug":1295,"name":1295,"fn":1296,"description":1297,"org":1382,"tags":1383,"stars":27,"repoUrl":28,"updatedAt":1303},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1384,1385,1386],{"name":1242,"slug":1243,"type":15},{"name":1228,"slug":1229,"type":15},{"name":1261,"slug":1262,"type":15},{"slug":1388,"name":1388,"fn":1389,"description":1390,"org":1391,"tags":1392,"stars":27,"repoUrl":28,"updatedAt":1399},"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},[1393,1394,1395,1396],{"name":17,"slug":18,"type":15},{"name":9,"slug":20,"type":15},{"name":22,"slug":23,"type":15},{"name":1397,"slug":1398,"type":15},"MCP","mcp","2026-04-06T18:25:50.364185",{"slug":1401,"name":1401,"fn":1402,"description":1403,"org":1404,"tags":1405,"stars":27,"repoUrl":28,"updatedAt":1413},"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},[1406,1407,1408,1409,1410],{"name":1242,"slug":1243,"type":15},{"name":1215,"slug":1216,"type":15},{"name":9,"slug":20,"type":15},{"name":22,"slug":23,"type":15},{"name":1411,"slug":1412,"type":15},"Performance","performance","2026-06-13T07:23:57.101435",{"slug":1415,"name":1415,"fn":1416,"description":1417,"org":1418,"tags":1419,"stars":27,"repoUrl":28,"updatedAt":1426},"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},[1420,1423,1424,1425],{"name":1421,"slug":1422,"type":15},"Analytics","analytics",{"name":9,"slug":20,"type":15},{"name":22,"slug":23,"type":15},{"name":13,"slug":14,"type":15},"2026-04-24T05:09:45.870658",37]