---

name: config-authoring description: "Design, inspect, validate, and serialize DataDesigner configs: builders, columns, samplers, seeds, person data, validators, processors, model/tool configs, custom columns, skip/drop rules, and Jinja references." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Config Authoring

Use this sub-skill when the user needs to create, edit, inspect, validate, or serialize data_designer.config objects or a Python script that returns a DataDesignerConfigBuilder.

Route here for

• Builder API questions: DataDesignerConfigBuilder, shorthand add_column(...), from_config(...), build(), and write_config(...).
• Column design: sampler, LLM text/code/structured/judge, expression, validation, embedding, image, seed dataset, and custom column configs.
• Sampler parameters, sampler constraints, conditional sampler params, person/person-from-faker fields, and managed persona schema inspection.
• Seed source configs and seed/person Jinja references.
• Config-only model/tool declarations: ModelConfig, inference params, ModelProvider, ToolConfig, and MCP provider config objects.
• Troubleshooting Pydantic validation, discriminators, missing fields, bad Jinja syntax/references, drop/skip propagation, and serialization errors.

Do not handle here

• Running generation, preview, create, result artifacts, resume, or model readiness probes. Route to generation-runtime, especially for preview, create, and check_models.
• CLI state files, data-designer command syntax, agent context/types/state, or non-TTY behavior. Route to cli-and-agent-tools.
• Installing/discovering plugin packages or building package entry points. Route to plugins-and-extensions.
• Long recipe selection or adaptation narratives. Route to recipes-and-integrations.

For package topology and cross-cutting behavior, also use the root package overview. For install/import/global failures that are not config-specific, use root troubleshooting.

Operating workflow

1. Classify the request. Decide whether the user is asking to author a new config, inspect an existing builder/YAML/JSON/script, fix a config error, add seed/person data, add validators/processors, or serialize a config.
2. Load the right reference.
   • Public API and field sets: references/api-reference.md.
   • Columns, samplers, validators, skip/drop/Jinja, and custom columns: references/columns-and-samplers.md.
   • Seed sources and person data: references/seed-and-person-data.md.
   • Symptom-based fixes: references/troubleshooting.md.
3. Prefer public imports. Use import data_designer.config as dd and public dd.* exports unless a reference explicitly names a lower-level public module for inspection.
4. Author a builder, not an execution. Return or update a DataDesignerConfigBuilder; do not call preview, create, or check_models from this sub-skill.
5. Validate locally before handoff. At minimum instantiate the config objects and call builder.build() to trigger Pydantic validation. If the DataDesigner interface and non-model resources are available, DataDesigner().validate(builder) performs compile-time validation without running generation; generation/runtime failures still route to generation-runtime.
6. Be explicit about model aliases. The installed-package evidence found no usable model aliases by default in the agent context. Config-only sampler/expression/seed workflows can still build and validate without model calls. Model-backed columns need explicit ModelConfig aliases before runtime checks.
7. Serialize only serializable configs. builder.write_config("config.yaml") or .json is valid for normal seed sources; DataFrameSeedSource intentionally cannot be serialized, so convert it to LocalFileSeedSource first.
8. Use bundled inspectors when facts may be stale. Run python scripts/dump_config_type_catalog.py to refresh live installed field sets and python scripts/inspect_person_schema.py <locale> to inspect managed person fields when assets are present.

Output expectations

• For new scripts, expose load_config_builder() -> dd.DataDesignerConfigBuilder and keep dependencies explicit.
• For fixes, state the minimal field/key/discriminator change and the validation command or snippet used.
• For seed/person configs, name which columns remain in final output and which helper columns use drop=True.
• For validation handoff, distinguish config validation from runtime generation/model errors and link to generation-runtime when model calls or artifacts are involved.