[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7BJS01ZGS95KG52MM9R0XNA":3,"skill-same:01M7BJS01ZGS95KG52MM9R0XNA":98},{"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":30,"commitSha":31,"skillDirectory":32,"forksCount":14,"repositoryHtmlUrl":33,"repositoryHomepageUrl":34,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":36,"updatedAt":37,"install":38,"resources":51},"01M7BJS01ZGS95KG52MM9R0XNA","data-designer",null,"Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.","YYC-Cube\u002FYYC3-AI-Agent-Archive","YYC-Cube","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235006543?v=4","01M79J2PH4SWD2X0825X6PN748","yyc3","01M79K9J2WD44BBY4662XKNZRB",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-AI-Agent-Archive\u002Fblob\u002Fmain\u002Fskills-hub\u002Fai-ml\u002Fnvidia-skills\u002Fskills\u002Fdata-designer\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: data-designer\ndescription: Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.\ncategory: development-code\nversion: 1.0.0\nargument-hint: [describe the dataset you want to generate]\nlicense: Apache-2.0\nmetadata:\n  owner: DataDesigner\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\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- **`data-designer` CLI not found:** Tell the user that `data-designer` is not installed in this environment (requires Python >= 3.10). 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\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.\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\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- **`data-designer` CLI not found:** Tell the user that `data-designer` is not installed in this environment (requires Python >= 3.10). 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\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.\n",{"name":5,"license":15,"version":24,"category":25,"metadata":26,"description":7,"argument-hint":28},"1.0.0","development-code",{"owner":27},"DataDesigner",[29],"describe the dataset you want to 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Do NOT trigger for general agent usage, full system health checks, project debugging, or normal development.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-AI-Agent-Archive\u002Fblob\u002Fmain\u002Ftools-hub\u002Fcode-ide\u002Fskills-marketplace\u002Fskills\u002Fskill-scanner\u002FSKILL.md",[],[],{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":132,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":19},"01M7BSWGTCFW7F123S4BM4XY6W","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends agent capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-AI-Agent-Archive\u002Fblob\u002Fmain\u002Ftools-hub\u002Fcode-ide\u002Fskills-marketplace\u002Fskills\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-AI-Agent-Archive\u002Fblob\u002Fmain\u002Ftools-hub\u002Fcode-ide\u002Fskills-marketplace\u002Fskills\u002Fskill-creator\u002FSKILL.md",[],[],{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":142,"publishedAt":19},"01M7BSVV4DH8JK2ZMQSCW25VCD","shader-dev","Comprehensive GLSL shader techniques for creating stunning visual effects — ray marching, SDF modeling, fluid simulation, particle systems, procedural generation, lighting, post-processing, and more.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-AI-Agent-Archive\u002Fblob\u002Fmain\u002Ftools-hub\u002Fcode-ide\u002Fskills-marketplace\u002Fskills\u002Fshader-dev\u002FSKILL.md",[],[]]