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name: cot_prompt_templates description: Build standard, chain-of-thought, and ablation few-shot prompts with auditable prompt structure metadata.

Chain-of-Thought Prompt Templates

Use this skill when you need to construct prompts for the chain-of-thought prompting paper or for a recovery experiment that compares direct few-shot answers against reasoning-before-answer exemplars. Do not use it to run an LLM, compute a task answer, or score a prediction.

Inputs

• A JSON list of exemplars. Every exemplar needs question and answer; chain-of-thought and ablation modes also need reasoning.
• A target question string.
• A mode: standard, chain_of_thought, equation_only, variable_compute_only, or reasoning_after_answer.

Outputs

• Prompt text containing rendered exemplars and the target question.
• Metadata describing mode, exemplar count, reasoning placement, and whether final-answer markers are present.

Workflow

1. Select the prompt mode that matches the comparison being run.
2. Validate exemplar fields before rendering; reject missing reasoning in chain-of-thought modes.
3. Render exemplars as Q: and A: blocks.
4. For chain_of_thought, place reasoning before The answer is ....
5. For standard, omit reasoning and include only the direct answer.
6. For ablations, preserve the structural difference being tested: equations only, useless variable-compute padding, or reasoning after the answer.
7. Append the target question and A: prefix without including the target answer.

Validation

Run:

python scripts/cot_prompt_templates.py --self-test
python -m unittest discover -s tests

The tests verify that standard prompts omit reasoning, chain-of-thought prompts include reasoning before the final answer, and ablation modes are structurally distinct.

Limitations

This skill does not infer missing reasoning, call a model, or canonicalize answers. Pair it with cot_answer_extraction and cot_equation_calculator for evaluation or recovery work.