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name: synthetic-data description: "Generate synthetic DAGs, tabular samples, dynamic time-series samples, and feature-mapping helpers for CausalNex experiments." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Synthetic Data

Use this sub-skill when you need toy DAGs, synthetic tabular data, dynamic time-series samples, or helpers that reshape categorical and lagged inputs for causal workflows.

Route here when

• The task names generate_structure, sem_generator, nonlinear_sem_generator, generate_continuous_dataframe, generate_binary_dataframe, generate_count_dataframe, generate_categorical_dataframe, generate_structure_dynamic, generate_dataframe_dynamic, gen_stationary_dyn_net_and_df, DynamicDataTransformer, or VariableFeatureMapper.
• You need synthetic benchmarks or fixtures for another CausalNex workflow.
• You need dynamic lagged matrices or categorical feature expansion.

Route elsewhere when

• You want to fit or query a Bayesian network -> ../bayesian-networks/SKILL.md.
• You want to learn a graph from generated data -> ../structure-learning/SKILL.md.
• You need to discretize a feature before using generated data -> ../discretization/SKILL.md.

Start fast

1. Read references/api-reference.md for the generator and transform signatures.
2. Read references/workflows.md for static DAGs, dynamic DAGs, lagged transforms, and schema helpers.
3. Run ../../scripts/smoke_synthetic_data.py when you need a quick end-to-end fixture check.
4. Read references/troubleshooting.md when a generator rejects the graph or the transformer rejects the input layout.