mirror of
https://github.com/NVIDIA-NeMo/DataDesigner
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0a7b9e0d6d
* docs: add skeleton for "Data Designer Got Skills" dev note * create assets folder and add blog directory name * docs: add Claude Code plugin marketplace configuration Register the repo as a Claude Code plugin marketplace so users can install the data-designer skill via `/plugin marketplace add`. * docs: write first draft of "Data Designer Got Skills" dev note Full prose for all sections: intro with hero benchmark figure, agents as first-class users, baseline trace walkthrough, CLI and skill design, benchmark results (228 sessions), getting started with marketplace and npx install paths, and what's next. * docs: add error breakdown table and minor refinements * docs: add sdg and data-designer keywords to plugin metadata * docs: refine CLI framing, reduce em dashes, slop guard pass * docs: fix grammar in dev note (serial comma, double-which clause) * update hero image * docs: swap hero image, move benchmark figure, minor wording tweaks * docs: add narrative lead-in to skill trace summary * docs: refine quality bullet, streamline getting started modes * remove old image * slope-guard tweaks
472 lines
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472 lines
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<!-- ── 1. Typical Skill Session ── -->
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<details class="session-trace" open>
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<summary>
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<span class="st-arrow">▶</span>
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<span class="st-badge skill">Skill</span>
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<span class="st-name">Typical Skill Session</span>
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<span class="st-stats">
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<span class="st-stat">9 tools</span>
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<span class="st-stat zero">0 errors</span>
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<span class="st-stat">92s</span>
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</span>
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</summary>
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<div class="st-body">
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<div class="st-step">
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<span class="st-tool msg">Agent</span>
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<span class="st-desc"><em>I'll start by learning about the Data Designer library to understand the available column types, samplers, validators, and processors.</em></span>
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</div>
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<div class="st-step">
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<span class="st-num">1</span>
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<span class="st-tool bash">Bash</span>
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<span class="st-desc">
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<code>data-designer agent context 2>&1</code><details class="st-expand"><summary><span class="st-arrow">▶</span> output (7,427 chars)</summary><div>Data Designer v0.5.4rc2
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## Config Module
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The config module contains all user-facing configuration types. Do not search other modules in the library.
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config_root: .venv/lib/python3.13/site-packages/data_designer/config
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builder: {config_root}/config_builder.py
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base: {config_root}/base.py (read for inherited fields shared by columns and processors)
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All config types are accessible via: import data_designer.config as dd
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## Types
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### columns
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file: {config_root}/column_configs.py
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type description
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------------------------- ---------------------------------------------------------------------------------
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CustomColumnConfig Configuration for custom user-defined column generators.
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EmbeddingColumnConfig Configuration for embedding generation columns.
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ExpressionColumnConfig Configuration for derived columns using Jinja2 expressions.
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ImageColumnConfig Configuration for image generation columns.
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LLMCodeColumnConfig Configuration for code generation columns using Large Language Models.
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LLMJudgeColumnConfig Configuration for LLM-as-a-judge quality assessment and scoring columns.
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LLMStructuredColumnConfig Configuration for structured JSON generation columns using Large Language Models.
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LLMTextColumnConfig Configuration for text generation columns using Large Language Models.
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SamplerColumnConfig Configuration for columns generated using numerical samplers.
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SeedDatasetColumnConfig Configuration for columns sourced from seed datasets.
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ValidationColumnConfig Configuration for validation columns that validate existing columns.
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### constraints
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file: {config_root}/sampler_constraints.py
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type description
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-------------------------- --------------------------------------------------------------------------
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ColumnInequalityConstraint Constrain a sampler column to be less/greater than another sampler column.
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ScalarInequalityConstraint Constrain a sampler column to be less/greater than a scalar value.
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### processors
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file: {config_root}/processors.py
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type description
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------------------------------ ---------------------------------------------------------------------------------
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DropColumnsProcessorConfig Drop columns from the output dataset (prefer ``drop=True`` in the column config).
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SchemaTransformProcessorConfig Configuration for transforming the dataset schema using Jinja2 templates.
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### samplers
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file: {config_root}/sampler_params.py
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type description
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----------------------------- -------------------------------------------------------------------------------------
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BernoulliSamplerParams Parameters for sampling from a Bernoulli distribution.
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BernoulliMixtureSamplerParams Parameters for sampling from a Bernoulli mixture distribution.
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BinomialSamplerParams Parameters for sampling from a Binomial distribution.
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CategorySamplerParams Parameters for categorical sampling with optional probability weighting.
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DatetimeSamplerParams Parameters for uniform datetime sampling within a specified range.
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GaussianSamplerParams Parameters for sampling from a Gaussian (Normal) distribution.
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PersonSamplerParams Parameters for sampling synthetic person data with demographic attributes.
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PersonFromFakerSamplerParams Parameters for sampling synthetic person data with demographic attributes from Faker.
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PoissonSamplerParams Parameters for sampling from a Poisson distribution.
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ScipySamplerParams Parameters for sampling from any scipy.stats continuous or discrete distribution.
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SubcategorySamplerParams Parameters for subcategory sampling conditioned on a parent category column.
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TimeDeltaSamplerParams Parameters for sampling time deltas relative to a reference datetime column.
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UniformSamplerParams Parameters for sampling from a continuous Uniform distribution.
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UUIDSamplerParams Parameters for generating UUID (Universally Unique Identifier) values.
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### validators
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file: {config_root}/validator_params.py
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type description
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---------------------------- -----------------------------------------------------------------------------------------------------
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CodeValidatorParams Configuration for code validation. Supports Python and SQL code validation.
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LocalCallableValidatorParams Configuration for local callable validation. Expects a function to be passed that validates the data.
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RemoteValidatorParams Configuration for remote validation. Sends data to a remote endpoint for validation.
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## Model Aliases
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default_provider: nvidia
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model_alias model generation_type provider
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---------------- --------------------------------- --------------- --------
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nvidia-embedding nvidia/llama-3.2-nv-embedqa-1b-v2 embedding nvidia
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nvidia-reasoning openai/gpt-oss-20b chat-completion nvidia
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nvidia-text nvidia/nemotron-3-nano-30b-a3b chat-completion nvidia
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nvidia-vision nvidia/nemotron-nano-12b-v2-vl chat-completion nvidia
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openai-embedding text-embedding-3-large embedding openai
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openai-reasoning openai/openai/gpt-5.2 chat-completion openai
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openai-text us/azure/openai/gpt-4.1 chat-completion openai
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openai-vision openai/openai/gpt-5.2 chat-completion openai
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## Persona Datasets
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locale size installed
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---------- ------- ---------
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en_IN 2.39 GB True
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en_SG 0.30 GB True
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en_US 1.24 GB True
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hi_Deva_IN 4.14 GB True
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hi_Latn_IN 2.7 GB True
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ja_JP 1.69 GB True
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pt_BR 2.33 GB True
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## Commands
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command_pattern description
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------------------------------------------ -------------------------------------------------------------------
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data-designer agent context Prints output from all agent subcommands to bootstrap context.
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data-designer agent types [family] Type names, descriptions, and source files for one or all families.
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data-designer agent state model-aliases Model aliases and usability status.
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data-designer agent state persona-datasets Persona locales and install status.</div></details>
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</span>
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</div>
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<div class="st-step">
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<span class="st-tool msg">Agent</span>
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<span class="st-desc"><em>Now let me inspect the specific config schemas I'll need.</em></span>
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</div>
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<div class="st-step">
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<span class="st-num">2</span>
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<span class="st-tool read">Read</span>
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<span class="st-desc">
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<code>data_designer/config/base.py</code>
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</span>
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</div>
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<div class="st-step">
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<span class="st-num">3</span>
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<span class="st-tool read">Read</span>
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<span class="st-desc">
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<code>data_designer/config/column_configs.py</code>
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</span>
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</div>
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<div class="st-step">
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<span class="st-num">4</span>
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<span class="st-tool read">Read</span>
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<span class="st-desc">
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<code>data_designer/config/sampler_params.py</code>
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</span>
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</div>
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<div class="st-step">
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<span class="st-num">5</span>
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<span class="st-tool read">Read</span>
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<span class="st-desc">
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<code>data_designer/config/validator_params.py</code>
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</span>
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</div>
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<div class="st-step">
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<span class="st-num">6</span>
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<span class="st-tool read">Read</span>
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<span class="st-desc">
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<code>data_designer/config/config_builder.py</code>
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</span>
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</div>
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<div class="st-step">
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<span class="st-num">7</span>
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<span class="st-tool read">Read</span>
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<span class="st-desc">
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<code>data_designer/config/processors.py</code>
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</span>
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</div>
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<div class="st-step">
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<span class="st-tool msg">Agent</span>
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<span class="st-desc">
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<details class="st-expand"><summary><span class="st-arrow">▶</span> message (863 chars)</summary><div>Now I have a complete understanding of the library. Here are my key design decisions:
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**Schema:**
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1. `difficulty` — Category sampler: beginner, intermediate, advanced (equal weights)
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2. `subtopic` — Category sampler: 7 data science subtopics
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3. `instruction` — LLM text column: generates a natural language task description conditioned on difficulty + subtopic
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4. `solution` — LLM code column (Python): generates a complete solution conditioned on instruction + difficulty + subtopic
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5. `code_validation` — Validation column (code validator, Python): checks syntactic correctness of the solution
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6. `quality_score` — LLM judge column: scores correctness, code quality, and instruction clarity on 1-5 scale
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**Diversity axes:** difficulty x subtopic combinations ensure broad coverage. The LLM prompts emphasize varying libraries, complexity, and operation types.</div></details>
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</span>
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</div>
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<div class="st-step">
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<span class="st-num">8</span>
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<span class="st-tool write">Write</span>
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<span class="st-desc">
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<code>text_to_python.py</code><details class="st-expand"><summary><span class="st-arrow">▶</span> written content (7,540 chars)</summary><div># /// script
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# dependencies = [
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# "data-designer",
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# ]
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# ///
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import data_designer.config as dd
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def load_config_builder() -> dd.DataDesignerConfigBuilder:
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config_builder = dd.DataDesignerConfigBuilder()
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# 1. Difficulty level
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config_builder.add_column(
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dd.SamplerColumnConfig(
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name="difficulty",
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sampler_type="category",
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params=dd.CategorySamplerParams(
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values=["beginner", "intermediate", "advanced"],
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),
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)
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)
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# 2. Subtopic
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config_builder.add_column(
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dd.SamplerColumnConfig(
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name="subtopic",
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sampler_type="category",
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params=dd.CategorySamplerParams(
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values=[
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"data cleaning",
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"exploratory analysis",
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"aggregation and groupby operations",
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"visualization with matplotlib/seaborn",
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"statistical testing",
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"feature engineering",
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"working with messy or missing data",
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],
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),
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)
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)
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# 3. Natural language instruction
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config_builder.add_column(
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dd.LLMTextColumnConfig(
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name="instruction",
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model_alias="nvidia-text",
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system_prompt=(
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"You are an expert data science educator who writes clear, realistic Python coding tasks. "
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"Your instructions should be specific enough that a developer can implement a complete solution "
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"without ambiguity. Vary the libraries used (pandas, numpy, scipy, sklearn, matplotlib, seaborn, etc.) "
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"and the operations required to match the difficulty level."
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),
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prompt=(
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"Write a single, self-contained Python coding instruction for a {{ difficulty }}-level data science task "
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"in the subtopic of '{{ subtopic }}'.\n\n"
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"Requirements:\n"
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"- The task should be realistic and something a data scientist would actually encounter.\n"
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"- For beginner tasks, focus on simple operations with clear steps.\n"
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"- For intermediate tasks, combine multiple operations or require domain knowledge.\n"
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"- For advanced tasks, involve complex logic, performance considerations, or sophisticated techniques.\n"
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"- The instruction should describe what the code should do, including any expected input/output behavior.\n"
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"- Do NOT include the solution — only the task description.\n\n"
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"Return only the instruction text, nothing else."
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),
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)
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)
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# 4. Python solution
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config_builder.add_column(
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dd.LLMCodeColumnConfig(
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name="solution",
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model_alias="nvidia-text",
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code_lang="python",
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system_prompt=(
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"You are an expert Python data scientist. Write clean, well-structured, and correct Python code. "
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"The code must be completely self-contained — include all necessary imports, sample data creation, "
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"and function definitions. Do not rely on external files or databases. "
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"Use realistic variable names and follow PEP 8 conventions."
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),
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prompt=(
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"Write a complete, self-contained Python solution for the following {{ difficulty }}-level "
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"data science task in the area of '{{ subtopic }}'.\n\n"
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"Task:\n{{ instruction }}\n\n"
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"Requirements:\n"
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"- Include all necessary imports at the top.\n"
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"- Create any sample/mock data needed so the code runs independently.\n"
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"- The code must be syntactically valid and runnable as-is.\n"
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"- Match the complexity to the {{ difficulty }} level.\n"
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"- Add brief inline comments explaining key steps.\n\n"
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"Return only the Python code."
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),
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)
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)
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# 5. Code validation
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config_builder.add_column(
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dd.ValidationColumnConfig(
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name="code_validation",
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target_columns=["solution"],
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validator_type="code",
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validator_params=dd.CodeValidatorParams(code_lang="python"),
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)
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)
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# 6. LLM-as-a-judge quality scoring
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config_builder.add_column(
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dd.LLMJudgeColumnConfig(
|
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name="quality_score",
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model_alias="nvidia-text",
|
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prompt=(
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"Evaluate the following Python data science coding task and its solution.\n\n"
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"Difficulty: {{ difficulty }}\n"
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"Subtopic: {{ subtopic }}\n\n"
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"Instruction:\n{{ instruction }}\n\n"
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"Solution:\n{{ solution }}\n\n"
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"Score each dimension below on a 1-5 scale using the provided rubrics."
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),
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scores=[
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dd.Score(
|
|
name="correctness",
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description=(
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"Does the solution correctly implement what the instruction asks for? "
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"Consider whether the code logic is sound, handles edge cases appropriately, "
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"and would produce the expected output."
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),
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options={
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1: "Completely incorrect or does not address the instruction.",
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2: "Major logical errors; partially addresses the instruction.",
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3: "Mostly correct with minor bugs or missing edge cases.",
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4: "Correct implementation with only trivial issues.",
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5: "Fully correct, handles edge cases, and produces expected output.",
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},
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),
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dd.Score(
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name="code_quality",
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description=(
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"Assess the code quality including readability, proper use of libraries, "
|
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"PEP 8 compliance, appropriate variable naming, and idiomatic Python/pandas usage."
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),
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options={
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|
1: "Very poor quality: unreadable, no structure, bad naming.",
|
|
2: "Below average: some structure but many style issues.",
|
|
3: "Acceptable: readable but could improve in idiom or style.",
|
|
4: "Good: clean, well-structured, follows conventions.",
|
|
5: "Excellent: idiomatic, elegant, and highly readable.",
|
|
},
|
|
),
|
|
dd.Score(
|
|
name="instruction_clarity",
|
|
description=(
|
|
"How clear and unambiguous is the instruction? Could a competent developer "
|
|
"implement the solution from the instruction alone without guessing?"
|
|
),
|
|
options={
|
|
1: "Extremely vague or incomprehensible.",
|
|
2: "Unclear with significant ambiguity.",
|
|
3: "Understandable but some details are missing or ambiguous.",
|
|
4: "Clear with only minor room for interpretation.",
|
|
5: "Crystal clear, specific, and unambiguous.",
|
|
},
|
|
),
|
|
],
|
|
)
|
|
)
|
|
|
|
return config_builder
|
|
</div></details><div>File created successfully at: ./text_to_python.py</div>
|
|
</span>
|
|
</div>
|
|
<div class="st-step">
|
|
<span class="st-tool msg">Agent</span>
|
|
<span class="st-desc"><em>Now let me validate the config.</em></span>
|
|
</div>
|
|
<div class="st-step ok">
|
|
<span class="st-num">9</span>
|
|
<span class="st-tool bash">Bash</span>
|
|
<span class="st-desc">
|
|
<code>data-designer validate text_to_python.py 2>&1</code><div> ────────────────────────── Data Designer Validate ──────────────────────────
|
|
|
|
Config: text_to_python.py
|
|
|
|
[22:15:26] [INFO] ✅ Validation passed
|
|
✅ Configuration is valid</div>
|
|
</span>
|
|
</div>
|
|
<div class="st-step">
|
|
<span class="st-tool msg">Agent</span>
|
|
<span class="st-desc">
|
|
<details class="st-expand"><summary><span class="st-arrow">▶</span> message (1,025 chars)</summary><div>Validation passed. Here's a summary of the dataset pipeline:
|
|
|
|
| Column | Type | Description |
|
|
|---|---|---|
|
|
| `difficulty` | Category sampler | beginner, intermediate, advanced (uniform) |
|
|
| `subtopic` | Category sampler | 7 data science subtopics (data cleaning, exploratory analysis, aggregation/groupby, visualization, statistical testing, feature engineering, messy/missing data) |
|
|
| `instruction` | LLM text | Natural language task description conditioned on difficulty + subtopic |
|
|
| `solution` | LLM code (Python) | Complete self-contained Python solution for the instruction |
|
|
| `code_validation` | Code validator | Checks the solution for syntactic correctness |
|
|
| `quality_score` | LLM judge | Scores correctness, code_quality, and instruction_clarity (1-5 scale with structured reasoning) |
|
|
|
|
To generate the dataset, run:
|
|
|
|
```
|
|
data-designer create text_to_python.py --num-records <N> --dataset-name text-to-python
|
|
```
|
|
|
|
Note: generation can take a long time for large record counts (50+), so choose `N` accordingly.</div></details>
|
|
</span>
|
|
</div>
|
|
</div>
|
|
</details>
|