[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CPSAPS431F1FYJBWBVM5RF":3,"skill-same:01M7CPSAPS431F1FYJBWBVM5RF":50},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":28,"commitSha":29,"skillDirectory":30,"forksCount":31,"repositoryHtmlUrl":32,"repositoryHomepageUrl":6,"ownerHtmlUrl":33,"ownerBlogUrl":6,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M7CPSAPS431F1FYJBWBVM5RF","nemo-data-designer-plugin",null,"Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.","om-scogo\u002Fskillsh-scraper","om-scogo","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160466840?v=4","01M79AWB6DFVCQ7RK70CS34B96","Om Achrekar","01M79AWB6ZM9SKR8KDH1WSSM22",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fnvidia-skills\u002Fnemo-data-designer-plugin\u002Fskills\u002Fnemo-data-designer-plugin\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\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","# 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",{"name":5,"license":15,"metadata":24,"description":7,"argument-hint":26},{"owner":25},"nemo-platform",[27],"describe the dataset you want to generate","main","8859218955d9504cd18b59e7892100718039d3b4","data\u002Fskills\u002Fnvidia-skills\u002Fnemo-data-designer-plugin\u002Fskills\u002Fnemo-data-designer-plugin",1,"https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper","https:\u002F\u002Fgithub.com\u002Fom-scogo","SDE-1 @ Scogo ","2026-07-18 12:22:46",{"source":8,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add om-scogo\u002Fskillsh-scraper --skill nemo-data-designer-plugin",{"tool":42,"command":43},"bunx","bunx skills add om-scogo\u002Fskillsh-scraper --skill nemo-data-designer-plugin",{"tool":45,"command":46},"pnpm","pnpm dlx skills add om-scogo\u002Fskillsh-scraper --skill nemo-data-designer-plugin","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fnvidia-skills\u002Fnemo-data-designer-plugin\u002Fskills\u002Fnemo-data-designer-plugin\u002FSKILL.md",true,[],[51,59,64,71,76,83],{"skillNo":52,"name":53,"nameZh":6,"description":54,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":55,"licenseFileUrl":6,"sourceHtmlUrl":56,"categories":57,"keywords":58,"publishedAt":19},"01M7D6MC9FAAEV4CMNYX8W7JTG","arxiv-search","Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.","MIT","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fyorkeccak-scientific-skills\u002Farxiv-search\u002Fskills\u002Farxiv-search\u002FSKILL.md",[],[],{"skillNo":60,"name":53,"nameZh":6,"description":54,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":55,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":63,"publishedAt":19},"01M7D6M85CKKN87TA98VCQV4H7","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fyorkeccak-scientific-skills\u002Farxiv-search\u002FSkill.md",[],[],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":55,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":19},"01M7D4ZTCS4DR3CE4JY06X84T2","working-with-legacy-code","Safely change and test untested codebases using Feathers' \"Working Effectively with Legacy Code\". Use when the user mentions \"legacy code\", \"no tests\", \"untested codebase\", \"how do I test this\", \"seams\", \"characterization tests\", \"golden master\", \"sprout method\", \"afraid to change this code\", \"monster method\", \"dependency breaking\", or \"inherited a messy codebase\". Also trigger when changing code without tests safely, getting a class under test when constructors, statics, or singletons block it, adding features to tangled modules, or planning incremental test coverage for an old codebase. Covers the legacy-code change algorithm, seams, characterization tests, sprout\u002Fwrap, and dependency-breaking techniques. For refactoring code that already has tests, see refactoring-patterns. For day-to-day code quality, see clean-code.","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwondelai-skills\u002Fworking-with-legacy-code\u002Fworking-with-legacy-code\u002FSKILL.md",[],[],{"skillNo":72,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":55,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":19},"01M7D4ZHZKJD2RSNGAF63V14P4","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwondelai-skills\u002Fworking-with-legacy-code\u002FSkill.md",[],[],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":55,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":19},"01M7D4ZB4CJBFW5JE67CPH2QRV","web-typography","Select, pair, and implement typefaces for web projects. Use when the user mentions \"font pairing\", \"which typeface\", \"line height\", \"responsive typography\", \"web font loading\", \"type hierarchy\", \"variable fonts\", \"FOUT\u002FFOIT\", \"typographic scale\", or \"the text is hard to read\". Also trigger when choosing between system fonts and web fonts, optimizing font-loading performance, or designing readable long-form content. Covers readability evaluation, CSS implementation, and performance optimization. For overall UI design systems, see refactoring-ui. For dramatic typographic experiences, see top-design.","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwondelai-skills\u002Fweb-typography\u002Fweb-typography\u002FSKILL.md",[],[],{"skillNo":84,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":55,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":19},"01M7D4Z4MVYVY3NMJ18BNV6WNA","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwondelai-skills\u002Fweb-typography\u002FSkill.md",[],[]]