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name: prompt_template_rendering description: Render structured dataset examples into PromptSource-style natural-language source and target pairs for prompted multitask recovery.

Prompt Template Rendering

Use this skill when a recovery or implementation needs to convert structured NLP examples into the text-to-text prompted format used by T0/P3-style multitask training. Do not use it to choose train/evaluation splits or to score model outputs; those are separate contracts.

Inputs

• JSON examples containing fields referenced by prompt templates.
• Template specifications with dataset_id, template_id, input_format, target_field, and optional answer_choices.

Outputs

• Rendered records containing dataset_id, task_family, example_id, template_id, source, target, and label.

Workflow

1. Validate that every placeholder in input_format exists in the raw example.
2. Format the source string with Python-style named placeholders.
3. Resolve the target from target_field; if answer_choices is supplied, map numeric or symbolic labels to target text.
4. Preserve metadata for downstream leakage checks and prompt robustness evaluation.

Validation

Run python tests/test_prompt_template_rendering.py or validate the whole tree with the Distiller skill validator.

Limitations

This skill implements deterministic rendering only; it does not provide the original PromptSource package or full dataset loading.