[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-nemo-data-designer-plugin":3,"mdc-r3rjii-key":34,"related-repo-nvidia-nemo-data-designer-plugin":1010,"related-org-nvidia-nemo-data-designer-plugin":1114},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"nemo-data-designer-plugin","design NeMo synthetic data pipelines","Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Datasets","datasets","tag",{"name":17,"slug":18,"type":15},"Data Engineering","data-engineering",{"name":20,"slug":21,"type":15},"Data Pipeline","data-pipeline",{"name":9,"slug":8,"type":15},2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-20T05:58:18.666558","Apache-2.0",281,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fnemo-data-designer-plugin","---\n\nname: nemo-data-designer-plugin\ndescription: Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.\nargument-hint: [describe the dataset you want to generate]\nlicense: Apache-2.0\nmetadata:\n  owner: nemo-platform\n---\n\n# Before You Start\n\nDo not explore the workspace first. The workflow's Learn step gives you everything you need.\n\n# Goal\n\nBuild a synthetic dataset using the Data Designer library that matches this description:\n\n$ARGUMENTS\n\n# Workflow\n\nUse **Autopilot** mode if the user implies they don't want to answer questions — e.g., they say something like \"be opinionated\", \"you decide\", \"make reasonable assumptions\", \"just build it\", \"surprise me\", etc. Otherwise, use **Interactive** mode (default).\n\nRead **only** the workflow file that matches the selected mode, then follow it:\n\n- **Interactive** → read `workflows\u002Finteractive.md`\n- **Autopilot** → read `workflows\u002Fautopilot.md`\n\n# Rules\n\n- Keep all columns in the output by default. The only exceptions for dropping a column are: (1) the user explicitly asks, or (2) it is a helper column that exists solely to derive other columns (e.g., a sampled person object used to extract name, city, etc.). When in doubt, keep the column.\n- Do not suggest or ask about seed datasets. Only use one when the user explicitly provides seed data or asks to build from existing records. When using a seed, read `references\u002Fseed-datasets.md`.\n- When the dataset requires person data (names, demographics, addresses), read `references\u002Fperson-sampling.md`.\n- If a dataset script that matches the dataset description already exists, ask the user whether to edit it or create a new one.\n- For commands and context specific to this NeMo Platform plugin (e.g., sourcing model configs from IGW providers or in-script `ModelConfig`s, installing or publishing Nemotron Personas locales, platform-side resource pointers), read `references\u002Fnemo-platform-plugin-additions.md`.\n\n# Usage Tips and Common Pitfalls\n\n- **Sampler and validation columns need both a type and params.** E.g., `sampler_type=\"category\"` with `params=dd.CategorySamplerParams(...)`.\n- **Jinja2 templates** in `prompt`, `system_prompt`, and `expr` fields: reference columns with `{{ column_name }}`, nested fields with `{{ column_name.field }}`.\n- **`SamplerColumnConfig`:** Takes `params`, not `sampler_params`.\n- **LLM judge score access:** `LLMJudgeColumnConfig` produces a nested dict where each score name maps to `{reasoning: str, score: int}`. To get the numeric score, use the `.score` attribute. For example, for a judge column named `quality` with a score named `correctness`, use `{{ quality.correctness.score }}`. Using `{{ quality.correctness }}` returns the full dict, not the numeric score.\n\n# Troubleshooting\n\n- **`nemo data-designer` CLI not found:** Tell the user that `nemo data-designer` is not installed in this environment (requires Python >= 3.11). Ask if they would like you to create a virtual environment and install it, or if they prefer to do it themselves. Do not install anything without the user's permission.\n- **Network errors during preview:** A sandbox environment may be blocking outbound requests. Ask the user for permission to retry the command with the sandbox disabled. Only as a last resort, if retrying outside the sandbox also fails, tell the user to run the command themselves.\n\n# Output Template\n\nWrite a Python file to the current directory with a `load_config_builder()` function returning a `DataDesignerConfigBuilder`. Name the file descriptively (e.g., `customer_reviews.py`). Use PEP 723 inline metadata for dependencies.\n\n```python\n# \u002F\u002F\u002F script\n# dependencies = [\n#   \"data-designer\", # always required\n#   \"pydantic\", # only if this script imports from pydantic\n#   # add additional dependencies here\n# ]\n# \u002F\u002F\u002F\nimport data_designer.config as dd\nfrom pydantic import BaseModel, Field\n\n\n# Use Pydantic models when the output needs to conform to a specific schema\nclass MyStructuredOutput(BaseModel):\n    field_one: str = Field(description=\"...\")\n    field_two: int = Field(description=\"...\")\n\n\n# Use custom generators when built-in column types aren't enough\n@dd.custom_column_generator(\n    required_columns=[\"col_a\"],\n    side_effect_columns=[\"extra_col\"],\n)\ndef generator_function(row: dict) -> dict:\n    # add custom logic here that depends on \"col_a\" and update row in place\n    row[\"name_in_custom_column_config\"] = \"custom value\"\n    row[\"extra_col\"] = \"extra value\"\n    return row\n\n\ndef load_config_builder() -> dd.DataDesignerConfigBuilder:\n    config_builder = dd.DataDesignerConfigBuilder(\n        # Declaring model configs programmatically here is the portable path:\n        # it works for both local `run` and cluster `submit`, while the local\n        # YAML registry alternative only works for `run`. The provider below\n        # is a common default created during `nemo setup` — confirm it (or\n        # discover others) with `nemo inference providers list`. See\n        # references\u002Fnemo-platform-plugin-additions.md for the local-YAML alternative.\n        model_configs=[\n            dd.ModelConfig(\n                alias=\"text\",\n                model=\"...\",\n                provider=\"default\u002Fnvidia-build\",\n                inference_parameters=dd.ChatCompletionInferenceParams(),\n            ),\n        ],\n    )\n\n    # Seed dataset (only if the user explicitly mentions a seed dataset path)\n    # config_builder.with_seed_dataset(dd.LocalFileSeedSource(path=\"path\u002Fto\u002Fseed.parquet\"))\n\n    # config_builder.add_column(...)\n    # config_builder.add_processor(...)\n\n    return config_builder\n```\n\nOnly include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them. Prefer including `model_configs` when the dataset uses LLM columns — declaring it in the script keeps the config portable between local `run` and cluster `submit`, while the local YAML registry alternative only works for `run`.\n",{"data":35,"body":40},{"name":4,"description":6,"argument-hint":36,"license":26,"metadata":38},[37],"describe the dataset you want to generate",{"owner":39},"nemo-platform",{"type":41,"children":42},"root",[43,52,58,64,69,74,80,100,112,147,153,211,217,391,397,433,439,468,969,1004],{"type":44,"tag":45,"props":46,"children":48},"element","h1",{"id":47},"before-you-start",[49],{"type":50,"value":51},"text","Before You Start",{"type":44,"tag":53,"props":54,"children":55},"p",{},[56],{"type":50,"value":57},"Do not explore the workspace first. 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The only exceptions for dropping a column are: (1) the user explicitly asks, or (2) it is a helper column that exists solely to derive other columns (e.g., a sampled person object used to extract name, city, etc.). When in doubt, keep the column.",{"type":44,"tag":117,"props":162,"children":163},{},[164,166,172],{"type":50,"value":165},"Do not suggest or ask about seed datasets. Only use one when the user explicitly provides seed data or asks to build from existing records. When using a seed, read ",{"type":44,"tag":127,"props":167,"children":169},{"className":168},[],[170],{"type":50,"value":171},"references\u002Fseed-datasets.md",{"type":50,"value":173},".",{"type":44,"tag":117,"props":175,"children":176},{},[177,179,185],{"type":50,"value":178},"When the dataset requires person data (names, demographics, addresses), read ",{"type":44,"tag":127,"props":180,"children":182},{"className":181},[],[183],{"type":50,"value":184},"references\u002Fperson-sampling.md",{"type":50,"value":173},{"type":44,"tag":117,"props":187,"children":188},{},[189],{"type":50,"value":190},"If a dataset script that matches the dataset description already exists, ask the user whether to edit it or create a new one.",{"type":44,"tag":117,"props":192,"children":193},{},[194,196,202,204,210],{"type":50,"value":195},"For commands and context specific to this NeMo Platform plugin (e.g., sourcing model configs from IGW providers or in-script ",{"type":44,"tag":127,"props":197,"children":199},{"className":198},[],[200],{"type":50,"value":201},"ModelConfig",{"type":50,"value":203},"s, installing or publishing Nemotron Personas locales, platform-side resource pointers), read ",{"type":44,"tag":127,"props":205,"children":207},{"className":206},[],[208],{"type":50,"value":209},"references\u002Fnemo-platform-plugin-additions.md",{"type":50,"value":173},{"type":44,"tag":45,"props":212,"children":214},{"id":213},"usage-tips-and-common-pitfalls",[215],{"type":50,"value":216},"Usage Tips and Common Pitfalls",{"type":44,"tag":113,"props":218,"children":219},{},[220,245,294,325],{"type":44,"tag":117,"props":221,"children":222},{},[223,228,230,236,238,244],{"type":44,"tag":86,"props":224,"children":225},{},[226],{"type":50,"value":227},"Sampler and validation columns need both a type and params.",{"type":50,"value":229}," E.g., ",{"type":44,"tag":127,"props":231,"children":233},{"className":232},[],[234],{"type":50,"value":235},"sampler_type=\"category\"",{"type":50,"value":237}," with ",{"type":44,"tag":127,"props":239,"children":241},{"className":240},[],[242],{"type":50,"value":243},"params=dd.CategorySamplerParams(...)",{"type":50,"value":173},{"type":44,"tag":117,"props":246,"children":247},{},[248,253,255,261,263,269,271,277,279,285,287,293],{"type":44,"tag":86,"props":249,"children":250},{},[251],{"type":50,"value":252},"Jinja2 templates",{"type":50,"value":254}," in ",{"type":44,"tag":127,"props":256,"children":258},{"className":257},[],[259],{"type":50,"value":260},"prompt",{"type":50,"value":262},", ",{"type":44,"tag":127,"props":264,"children":266},{"className":265},[],[267],{"type":50,"value":268},"system_prompt",{"type":50,"value":270},", and ",{"type":44,"tag":127,"props":272,"children":274},{"className":273},[],[275],{"type":50,"value":276},"expr",{"type":50,"value":278}," fields: reference columns with ",{"type":44,"tag":127,"props":280,"children":282},{"className":281},[],[283],{"type":50,"value":284},"{{ column_name }}",{"type":50,"value":286},", nested fields with ",{"type":44,"tag":127,"props":288,"children":290},{"className":289},[],[291],{"type":50,"value":292},"{{ column_name.field }}",{"type":50,"value":173},{"type":44,"tag":117,"props":295,"children":296},{},[297,308,310,316,318,324],{"type":44,"tag":86,"props":298,"children":299},{},[300,306],{"type":44,"tag":127,"props":301,"children":303},{"className":302},[],[304],{"type":50,"value":305},"SamplerColumnConfig",{"type":50,"value":307},":",{"type":50,"value":309}," Takes ",{"type":44,"tag":127,"props":311,"children":313},{"className":312},[],[314],{"type":50,"value":315},"params",{"type":50,"value":317},", not ",{"type":44,"tag":127,"props":319,"children":321},{"className":320},[],[322],{"type":50,"value":323},"sampler_params",{"type":50,"value":173},{"type":44,"tag":117,"props":326,"children":327},{},[328,333,335,341,343,349,351,357,359,365,367,373,375,381,383,389],{"type":44,"tag":86,"props":329,"children":330},{},[331],{"type":50,"value":332},"LLM judge score access:",{"type":50,"value":334}," ",{"type":44,"tag":127,"props":336,"children":338},{"className":337},[],[339],{"type":50,"value":340},"LLMJudgeColumnConfig",{"type":50,"value":342}," produces a nested dict where each score name maps to ",{"type":44,"tag":127,"props":344,"children":346},{"className":345},[],[347],{"type":50,"value":348},"{reasoning: str, score: int}",{"type":50,"value":350},". To get the numeric score, use the ",{"type":44,"tag":127,"props":352,"children":354},{"className":353},[],[355],{"type":50,"value":356},".score",{"type":50,"value":358}," attribute. For example, for a judge column named ",{"type":44,"tag":127,"props":360,"children":362},{"className":361},[],[363],{"type":50,"value":364},"quality",{"type":50,"value":366}," with a score named ",{"type":44,"tag":127,"props":368,"children":370},{"className":369},[],[371],{"type":50,"value":372},"correctness",{"type":50,"value":374},", use ",{"type":44,"tag":127,"props":376,"children":378},{"className":377},[],[379],{"type":50,"value":380},"{{ quality.correctness.score }}",{"type":50,"value":382},". Using ",{"type":44,"tag":127,"props":384,"children":386},{"className":385},[],[387],{"type":50,"value":388},"{{ quality.correctness }}",{"type":50,"value":390}," returns the full dict, not the numeric score.",{"type":44,"tag":45,"props":392,"children":394},{"id":393},"troubleshooting",[395],{"type":50,"value":396},"Troubleshooting",{"type":44,"tag":113,"props":398,"children":399},{},[400,423],{"type":44,"tag":117,"props":401,"children":402},{},[403,414,416,421],{"type":44,"tag":86,"props":404,"children":405},{},[406,412],{"type":44,"tag":127,"props":407,"children":409},{"className":408},[],[410],{"type":50,"value":411},"nemo data-designer",{"type":50,"value":413}," CLI not found:",{"type":50,"value":415}," Tell the user that ",{"type":44,"tag":127,"props":417,"children":419},{"className":418},[],[420],{"type":50,"value":411},{"type":50,"value":422}," is not installed in this environment (requires Python >= 3.11). Ask if they would like you to create a virtual environment and install it, or if they prefer to do it themselves. Do not install anything without the user's permission.",{"type":44,"tag":117,"props":424,"children":425},{},[426,431],{"type":44,"tag":86,"props":427,"children":428},{},[429],{"type":50,"value":430},"Network errors during preview:",{"type":50,"value":432}," A sandbox environment may be blocking outbound requests. Ask the user for permission to retry the command with the sandbox disabled. Only as a last resort, if retrying outside the sandbox also fails, tell the user to run the command themselves.",{"type":44,"tag":45,"props":434,"children":436},{"id":435},"output-template",[437],{"type":50,"value":438},"Output Template",{"type":44,"tag":53,"props":440,"children":441},{},[442,444,450,452,458,460,466],{"type":50,"value":443},"Write a Python file to the current directory with a ",{"type":44,"tag":127,"props":445,"children":447},{"className":446},[],[448],{"type":50,"value":449},"load_config_builder()",{"type":50,"value":451}," function returning a ",{"type":44,"tag":127,"props":453,"children":455},{"className":454},[],[456],{"type":50,"value":457},"DataDesignerConfigBuilder",{"type":50,"value":459},". Name the file descriptively (e.g., ",{"type":44,"tag":127,"props":461,"children":463},{"className":462},[],[464],{"type":50,"value":465},"customer_reviews.py",{"type":50,"value":467},"). Use PEP 723 inline metadata for dependencies.",{"type":44,"tag":469,"props":470,"children":475},"pre",{"className":471,"code":472,"language":473,"meta":474,"style":474},"language-python shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# \u002F\u002F\u002F script\n# dependencies = [\n#   \"data-designer\", # always required\n#   \"pydantic\", # only if this script imports from pydantic\n#   # add additional dependencies here\n# ]\n# \u002F\u002F\u002F\nimport data_designer.config as dd\nfrom pydantic import BaseModel, Field\n\n\n# Use Pydantic models when the output needs to conform to a specific schema\nclass MyStructuredOutput(BaseModel):\n    field_one: str = Field(description=\"...\")\n    field_two: int = Field(description=\"...\")\n\n\n# Use custom generators when built-in column types aren't enough\n@dd.custom_column_generator(\n    required_columns=[\"col_a\"],\n    side_effect_columns=[\"extra_col\"],\n)\ndef generator_function(row: dict) -> dict:\n    # add custom logic here that depends on \"col_a\" and update row in place\n    row[\"name_in_custom_column_config\"] = \"custom value\"\n    row[\"extra_col\"] = \"extra value\"\n    return row\n\n\ndef load_config_builder() -> dd.DataDesignerConfigBuilder:\n    config_builder = dd.DataDesignerConfigBuilder(\n        # Declaring model configs programmatically here is the portable path:\n        # it works for both local `run` and cluster `submit`, while the local\n        # YAML registry alternative only works for `run`. The provider below\n        # is a common default created during `nemo setup` — confirm it (or\n        # discover others) with `nemo inference providers list`. See\n        # references\u002Fnemo-platform-plugin-additions.md for the local-YAML alternative.\n        model_configs=[\n            dd.ModelConfig(\n                alias=\"text\",\n                model=\"...\",\n                provider=\"default\u002Fnvidia-build\",\n                inference_parameters=dd.ChatCompletionInferenceParams(),\n            ),\n        ],\n    )\n\n    # Seed dataset (only if the user explicitly mentions a seed dataset path)\n    # config_builder.with_seed_dataset(dd.LocalFileSeedSource(path=\"path\u002Fto\u002Fseed.parquet\"))\n\n    # config_builder.add_column(...)\n    # config_builder.add_processor(...)\n\n    return config_builder\n","python","",[476],{"type":44,"tag":127,"props":477,"children":478},{"__ignoreMap":474},[479,495,504,513,522,531,540,553,562,571,581,589,598,607,616,625,633,641,650,659,668,677,686,695,704,713,722,731,739,747,756,765,774,783,792,801,810,819,828,837,846,855,864,873,882,891,900,908,917,926,934,943,952,960],{"type":44,"tag":480,"props":481,"children":484},"span",{"class":482,"line":483},"line",1,[485,490],{"type":44,"tag":480,"props":486,"children":487},{},[488],{"type":50,"value":489},"#",{"type":44,"tag":480,"props":491,"children":492},{},[493],{"type":50,"value":494}," \u002F\u002F\u002F script\n",{"type":44,"tag":480,"props":496,"children":498},{"class":482,"line":497},2,[499],{"type":44,"tag":480,"props":500,"children":501},{},[502],{"type":50,"value":503},"# dependencies = [\n",{"type":44,"tag":480,"props":505,"children":507},{"class":482,"line":506},3,[508],{"type":44,"tag":480,"props":509,"children":510},{},[511],{"type":50,"value":512},"#   \"data-designer\", # always required\n",{"type":44,"tag":480,"props":514,"children":516},{"class":482,"line":515},4,[517],{"type":44,"tag":480,"props":518,"children":519},{},[520],{"type":50,"value":521},"#   \"pydantic\", # only if this script imports from pydantic\n",{"type":44,"tag":480,"props":523,"children":525},{"class":482,"line":524},5,[526],{"type":44,"tag":480,"props":527,"children":528},{},[529],{"type":50,"value":530},"#   # add additional dependencies here\n",{"type":44,"tag":480,"props":532,"children":534},{"class":482,"line":533},6,[535],{"type":44,"tag":480,"props":536,"children":537},{},[538],{"type":50,"value":539},"# ]\n",{"type":44,"tag":480,"props":541,"children":543},{"class":482,"line":542},7,[544,548],{"type":44,"tag":480,"props":545,"children":546},{},[547],{"type":50,"value":489},{"type":44,"tag":480,"props":549,"children":550},{},[551],{"type":50,"value":552}," \u002F\u002F\u002F\n",{"type":44,"tag":480,"props":554,"children":556},{"class":482,"line":555},8,[557],{"type":44,"tag":480,"props":558,"children":559},{},[560],{"type":50,"value":561},"import data_designer.config as dd\n",{"type":44,"tag":480,"props":563,"children":565},{"class":482,"line":564},9,[566],{"type":44,"tag":480,"props":567,"children":568},{},[569],{"type":50,"value":570},"from pydantic import BaseModel, Field\n",{"type":44,"tag":480,"props":572,"children":574},{"class":482,"line":573},10,[575],{"type":44,"tag":480,"props":576,"children":578},{"emptyLinePlaceholder":577},true,[579],{"type":50,"value":580},"\n",{"type":44,"tag":480,"props":582,"children":584},{"class":482,"line":583},11,[585],{"type":44,"tag":480,"props":586,"children":587},{"emptyLinePlaceholder":577},[588],{"type":50,"value":580},{"type":44,"tag":480,"props":590,"children":592},{"class":482,"line":591},12,[593],{"type":44,"tag":480,"props":594,"children":595},{},[596],{"type":50,"value":597},"# Use Pydantic models when the output needs to conform to a specific schema\n",{"type":44,"tag":480,"props":599,"children":601},{"class":482,"line":600},13,[602],{"type":44,"tag":480,"props":603,"children":604},{},[605],{"type":50,"value":606},"class MyStructuredOutput(BaseModel):\n",{"type":44,"tag":480,"props":608,"children":610},{"class":482,"line":609},14,[611],{"type":44,"tag":480,"props":612,"children":613},{},[614],{"type":50,"value":615},"    field_one: str = Field(description=\"...\")\n",{"type":44,"tag":480,"props":617,"children":619},{"class":482,"line":618},15,[620],{"type":44,"tag":480,"props":621,"children":622},{},[623],{"type":50,"value":624},"    field_two: int = Field(description=\"...\")\n",{"type":44,"tag":480,"props":626,"children":628},{"class":482,"line":627},16,[629],{"type":44,"tag":480,"props":630,"children":631},{"emptyLinePlaceholder":577},[632],{"type":50,"value":580},{"type":44,"tag":480,"props":634,"children":636},{"class":482,"line":635},17,[637],{"type":44,"tag":480,"props":638,"children":639},{"emptyLinePlaceholder":577},[640],{"type":50,"value":580},{"type":44,"tag":480,"props":642,"children":644},{"class":482,"line":643},18,[645],{"type":44,"tag":480,"props":646,"children":647},{},[648],{"type":50,"value":649},"# Use custom generators when built-in column types aren't enough\n",{"type":44,"tag":480,"props":651,"children":653},{"class":482,"line":652},19,[654],{"type":44,"tag":480,"props":655,"children":656},{},[657],{"type":50,"value":658},"@dd.custom_column_generator(\n",{"type":44,"tag":480,"props":660,"children":662},{"class":482,"line":661},20,[663],{"type":44,"tag":480,"props":664,"children":665},{},[666],{"type":50,"value":667},"    required_columns=[\"col_a\"],\n",{"type":44,"tag":480,"props":669,"children":671},{"class":482,"line":670},21,[672],{"type":44,"tag":480,"props":673,"children":674},{},[675],{"type":50,"value":676},"    side_effect_columns=[\"extra_col\"],\n",{"type":44,"tag":480,"props":678,"children":680},{"class":482,"line":679},22,[681],{"type":44,"tag":480,"props":682,"children":683},{},[684],{"type":50,"value":685},")\n",{"type":44,"tag":480,"props":687,"children":689},{"class":482,"line":688},23,[690],{"type":44,"tag":480,"props":691,"children":692},{},[693],{"type":50,"value":694},"def generator_function(row: dict) -> dict:\n",{"type":44,"tag":480,"props":696,"children":698},{"class":482,"line":697},24,[699],{"type":44,"tag":480,"props":700,"children":701},{},[702],{"type":50,"value":703},"    # add custom logic here that depends on \"col_a\" and update row in place\n",{"type":44,"tag":480,"props":705,"children":707},{"class":482,"line":706},25,[708],{"type":44,"tag":480,"props":709,"children":710},{},[711],{"type":50,"value":712},"    row[\"name_in_custom_column_config\"] = \"custom value\"\n",{"type":44,"tag":480,"props":714,"children":716},{"class":482,"line":715},26,[717],{"type":44,"tag":480,"props":718,"children":719},{},[720],{"type":50,"value":721},"    row[\"extra_col\"] = \"extra value\"\n",{"type":44,"tag":480,"props":723,"children":725},{"class":482,"line":724},27,[726],{"type":44,"tag":480,"props":727,"children":728},{},[729],{"type":50,"value":730},"    return row\n",{"type":44,"tag":480,"props":732,"children":734},{"class":482,"line":733},28,[735],{"type":44,"tag":480,"props":736,"children":737},{"emptyLinePlaceholder":577},[738],{"type":50,"value":580},{"type":44,"tag":480,"props":740,"children":742},{"class":482,"line":741},29,[743],{"type":44,"tag":480,"props":744,"children":745},{"emptyLinePlaceholder":577},[746],{"type":50,"value":580},{"type":44,"tag":480,"props":748,"children":750},{"class":482,"line":749},30,[751],{"type":44,"tag":480,"props":752,"children":753},{},[754],{"type":50,"value":755},"def load_config_builder() -> dd.DataDesignerConfigBuilder:\n",{"type":44,"tag":480,"props":757,"children":759},{"class":482,"line":758},31,[760],{"type":44,"tag":480,"props":761,"children":762},{},[763],{"type":50,"value":764},"    config_builder = dd.DataDesignerConfigBuilder(\n",{"type":44,"tag":480,"props":766,"children":768},{"class":482,"line":767},32,[769],{"type":44,"tag":480,"props":770,"children":771},{},[772],{"type":50,"value":773},"        # Declaring model configs programmatically here is the portable path:\n",{"type":44,"tag":480,"props":775,"children":777},{"class":482,"line":776},33,[778],{"type":44,"tag":480,"props":779,"children":780},{},[781],{"type":50,"value":782},"        # it works for both local `run` and cluster `submit`, while the local\n",{"type":44,"tag":480,"props":784,"children":786},{"class":482,"line":785},34,[787],{"type":44,"tag":480,"props":788,"children":789},{},[790],{"type":50,"value":791},"        # YAML registry alternative only works for `run`. The provider below\n",{"type":44,"tag":480,"props":793,"children":795},{"class":482,"line":794},35,[796],{"type":44,"tag":480,"props":797,"children":798},{},[799],{"type":50,"value":800},"        # is a common default created during `nemo setup` — confirm it (or\n",{"type":44,"tag":480,"props":802,"children":804},{"class":482,"line":803},36,[805],{"type":44,"tag":480,"props":806,"children":807},{},[808],{"type":50,"value":809},"        # discover others) with `nemo inference providers list`. See\n",{"type":44,"tag":480,"props":811,"children":813},{"class":482,"line":812},37,[814],{"type":44,"tag":480,"props":815,"children":816},{},[817],{"type":50,"value":818},"        # references\u002Fnemo-platform-plugin-additions.md for the local-YAML alternative.\n",{"type":44,"tag":480,"props":820,"children":822},{"class":482,"line":821},38,[823],{"type":44,"tag":480,"props":824,"children":825},{},[826],{"type":50,"value":827},"        model_configs=[\n",{"type":44,"tag":480,"props":829,"children":831},{"class":482,"line":830},39,[832],{"type":44,"tag":480,"props":833,"children":834},{},[835],{"type":50,"value":836},"            dd.ModelConfig(\n",{"type":44,"tag":480,"props":838,"children":840},{"class":482,"line":839},40,[841],{"type":44,"tag":480,"props":842,"children":843},{},[844],{"type":50,"value":845},"                alias=\"text\",\n",{"type":44,"tag":480,"props":847,"children":849},{"class":482,"line":848},41,[850],{"type":44,"tag":480,"props":851,"children":852},{},[853],{"type":50,"value":854},"                model=\"...\",\n",{"type":44,"tag":480,"props":856,"children":858},{"class":482,"line":857},42,[859],{"type":44,"tag":480,"props":860,"children":861},{},[862],{"type":50,"value":863},"                provider=\"default\u002Fnvidia-build\",\n",{"type":44,"tag":480,"props":865,"children":867},{"class":482,"line":866},43,[868],{"type":44,"tag":480,"props":869,"children":870},{},[871],{"type":50,"value":872},"                inference_parameters=dd.ChatCompletionInferenceParams(),\n",{"type":44,"tag":480,"props":874,"children":876},{"class":482,"line":875},44,[877],{"type":44,"tag":480,"props":878,"children":879},{},[880],{"type":50,"value":881},"            ),\n",{"type":44,"tag":480,"props":883,"children":885},{"class":482,"line":884},45,[886],{"type":44,"tag":480,"props":887,"children":888},{},[889],{"type":50,"value":890},"        ],\n",{"type":44,"tag":480,"props":892,"children":894},{"class":482,"line":893},46,[895],{"type":44,"tag":480,"props":896,"children":897},{},[898],{"type":50,"value":899},"    )\n",{"type":44,"tag":480,"props":901,"children":903},{"class":482,"line":902},47,[904],{"type":44,"tag":480,"props":905,"children":906},{"emptyLinePlaceholder":577},[907],{"type":50,"value":580},{"type":44,"tag":480,"props":909,"children":911},{"class":482,"line":910},48,[912],{"type":44,"tag":480,"props":913,"children":914},{},[915],{"type":50,"value":916},"    # Seed dataset (only if the user explicitly mentions a seed dataset path)\n",{"type":44,"tag":480,"props":918,"children":920},{"class":482,"line":919},49,[921],{"type":44,"tag":480,"props":922,"children":923},{},[924],{"type":50,"value":925},"    # config_builder.with_seed_dataset(dd.LocalFileSeedSource(path=\"path\u002Fto\u002Fseed.parquet\"))\n",{"type":44,"tag":480,"props":927,"children":929},{"class":482,"line":928},50,[930],{"type":44,"tag":480,"props":931,"children":932},{"emptyLinePlaceholder":577},[933],{"type":50,"value":580},{"type":44,"tag":480,"props":935,"children":937},{"class":482,"line":936},51,[938],{"type":44,"tag":480,"props":939,"children":940},{},[941],{"type":50,"value":942},"    # config_builder.add_column(...)\n",{"type":44,"tag":480,"props":944,"children":946},{"class":482,"line":945},52,[947],{"type":44,"tag":480,"props":948,"children":949},{},[950],{"type":50,"value":951},"    # config_builder.add_processor(...)\n",{"type":44,"tag":480,"props":953,"children":955},{"class":482,"line":954},53,[956],{"type":44,"tag":480,"props":957,"children":958},{"emptyLinePlaceholder":577},[959],{"type":50,"value":580},{"type":44,"tag":480,"props":961,"children":963},{"class":482,"line":962},54,[964],{"type":44,"tag":480,"props":965,"children":966},{},[967],{"type":50,"value":968},"    return config_builder\n",{"type":44,"tag":53,"props":970,"children":971},{},[972,974,980,982,988,990,996,998,1003],{"type":50,"value":973},"Only include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them. Prefer including ",{"type":44,"tag":127,"props":975,"children":977},{"className":976},[],[978],{"type":50,"value":979},"model_configs",{"type":50,"value":981}," when the dataset uses LLM columns — declaring it in the script keeps the config portable between local ",{"type":44,"tag":127,"props":983,"children":985},{"className":984},[],[986],{"type":50,"value":987},"run",{"type":50,"value":989}," and cluster ",{"type":44,"tag":127,"props":991,"children":993},{"className":992},[],[994],{"type":50,"value":995},"submit",{"type":50,"value":997},", while the local YAML registry alternative only works for ",{"type":44,"tag":127,"props":999,"children":1001},{"className":1000},[],[1002],{"type":50,"value":987},{"type":50,"value":173},{"type":44,"tag":1005,"props":1006,"children":1007},"style",{},[1008],{"type":50,"value":1009},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1011,"total":1113},[1012,1027,1041,1055,1067,1084,1099],{"slug":1013,"name":1013,"fn":1014,"description":1015,"org":1016,"tags":1017,"stars":23,"repoUrl":24,"updatedAt":1026},"accelerated-computing-cudf","accelerate data processing with cuDF","Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV\u002FParquet I\u002FO, nullable semantics, and multi-GPU DataFrame workloads.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1018,1021,1022,1023],{"name":1019,"slug":1020,"type":15},"Data Analysis","data-analysis",{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":1024,"slug":1025,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1028,"name":1028,"fn":1029,"description":1030,"org":1031,"tags":1032,"stars":23,"repoUrl":24,"updatedAt":1040},"aiq-deploy","deploy and manage NVIDIA AI-Q infrastructure","Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1033,1036,1039],{"name":1034,"slug":1035,"type":15},"Deployment","deployment",{"name":1037,"slug":1038,"type":15},"Infrastructure","infrastructure",{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1042,"name":1042,"fn":1043,"description":1044,"org":1045,"tags":1046,"stars":23,"repoUrl":24,"updatedAt":1054},"aiq-research","conduct deep research with AI-Q","Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1047,1050,1051],{"name":1048,"slug":1049,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":1052,"slug":1053,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1056,"name":1056,"fn":1057,"description":1058,"org":1059,"tags":1060,"stars":23,"repoUrl":24,"updatedAt":1066},"amc-run-sample-calibration","run AMC sample dataset calibration","Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1061,1062,1063],{"name":1019,"slug":1020,"type":15},{"name":9,"slug":8,"type":15},{"name":1064,"slug":1065,"type":15},"Testing","testing","2026-07-17T05:29:03.913266",{"slug":1068,"name":1068,"fn":1069,"description":1070,"org":1071,"tags":1072,"stars":23,"repoUrl":24,"updatedAt":1083},"amc-run-video-calibration","calibrate video datasets with AutoMagicCalib","Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1073,1076,1079,1080],{"name":1074,"slug":1075,"type":15},"Automation","automation",{"name":1077,"slug":1078,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1081,"slug":1082,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1085,"name":1085,"fn":1086,"description":1087,"org":1088,"tags":1089,"stars":23,"repoUrl":24,"updatedAt":1098},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1090,1091,1094,1095],{"name":1034,"slug":1035,"type":15},{"name":1092,"slug":1093,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":1096,"slug":1097,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":1100,"name":1100,"fn":1101,"description":1102,"org":1103,"tags":1104,"stars":23,"repoUrl":24,"updatedAt":1112},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1105,1106,1109],{"name":9,"slug":8,"type":15},{"name":1107,"slug":1108,"type":15},"Quantum Computing","quantum-computing",{"name":1110,"slug":1111,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305,{"items":1115,"total":1265},[1116,1134,1149,1160,1172,1186,1199,1213,1224,1233,1247,1256],{"slug":1117,"name":1117,"fn":1118,"description":1119,"org":1120,"tags":1121,"stars":1131,"repoUrl":1132,"updatedAt":1133},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1122,1125,1128],{"name":1123,"slug":1124,"type":15},"Documentation","documentation",{"name":1126,"slug":1127,"type":15},"MCP","mcp",{"name":1129,"slug":1130,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":1135,"name":1135,"fn":1136,"description":1137,"org":1138,"tags":1139,"stars":1146,"repoUrl":1147,"updatedAt":1148},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1140,1143,1144],{"name":1141,"slug":1142,"type":15},"Containers","containers",{"name":1034,"slug":1035,"type":15},{"name":1145,"slug":473,"type":15},"Python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":1150,"name":1150,"fn":1151,"description":1152,"org":1153,"tags":1154,"stars":1146,"repoUrl":1147,"updatedAt":1159},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1155,1158],{"name":1156,"slug":1157,"type":15},"CI\u002FCD","ci-cd",{"name":1034,"slug":1035,"type":15},"2026-07-14T05:25:59.97109",{"slug":1161,"name":1161,"fn":1162,"description":1163,"org":1164,"tags":1165,"stars":1146,"repoUrl":1147,"updatedAt":1171},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1166,1167,1168],{"name":1156,"slug":1157,"type":15},{"name":1034,"slug":1035,"type":15},{"name":1169,"slug":1170,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1173,"name":1173,"fn":1174,"description":1175,"org":1176,"tags":1177,"stars":1146,"repoUrl":1147,"updatedAt":1185},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1178,1181,1182],{"name":1179,"slug":1180,"type":15},"Debugging","debugging",{"name":1169,"slug":1170,"type":15},{"name":1183,"slug":1184,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1187,"name":1187,"fn":1188,"description":1189,"org":1190,"tags":1191,"stars":1146,"repoUrl":1147,"updatedAt":1198},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1192,1195],{"name":1193,"slug":1194,"type":15},"Best Practices","best-practices",{"name":1196,"slug":1197,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1200,"name":1200,"fn":1201,"description":1202,"org":1203,"tags":1204,"stars":1146,"repoUrl":1147,"updatedAt":1212},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1205,1208,1211],{"name":1206,"slug":1207,"type":15},"Machine Learning","machine-learning",{"name":1209,"slug":1210,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1214,"name":1214,"fn":1215,"description":1216,"org":1217,"tags":1218,"stars":1146,"repoUrl":1147,"updatedAt":1223},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1219,1222],{"name":1220,"slug":1221,"type":15},"QA","qa",{"name":1064,"slug":1065,"type":15},"2026-07-14T05:25:53.673039",{"slug":1225,"name":1225,"fn":1226,"description":1227,"org":1228,"tags":1229,"stars":1146,"repoUrl":1147,"updatedAt":1232},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1230,1231],{"name":1034,"slug":1035,"type":15},{"name":1037,"slug":1038,"type":15},"2026-07-14T05:25:49.362534",{"slug":1234,"name":1234,"fn":1235,"description":1236,"org":1237,"tags":1238,"stars":1146,"repoUrl":1147,"updatedAt":1246},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1239,1242,1243],{"name":1240,"slug":1241,"type":15},"Code Review","code-review",{"name":1169,"slug":1170,"type":15},{"name":1244,"slug":1245,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1248,"name":1248,"fn":1249,"description":1250,"org":1251,"tags":1252,"stars":1146,"repoUrl":1147,"updatedAt":1255},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1253,1254],{"name":1220,"slug":1221,"type":15},{"name":1064,"slug":1065,"type":15},"2026-07-14T05:25:54.928983",{"slug":1257,"name":1257,"fn":1258,"description":1259,"org":1260,"tags":1261,"stars":1146,"repoUrl":1147,"updatedAt":1264},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1262,1263],{"name":1074,"slug":1075,"type":15},{"name":1156,"slug":1157,"type":15},"2026-07-30T05:29:03.275638",496]