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name: cot_answer_extraction description: Extract and normalize final answers from chain-of-thought outputs without scoring intermediate reasoning text.

Chain-of-Thought Answer Extraction

Use this skill when a model or recovery harness emits reasoning followed by a final answer and you need the shortest canonical answer span for scoring. Do not use it to build prompts, judge rationale quality, or run an external calculator.

Inputs

• Raw output text.
• Task type: numeric, multiple_choice, yes_no, date, symbolic, or plan.
• Optional final-answer markers and numeric tolerance settings.

Outputs

• Raw text.
• Extracted final answer.
• Normalized answer.
• Diagnostics with marker and fallback information.

Workflow

1. Search from the end of the output for markers such as The answer is, So the answer is, or Answer:.
2. Extract the following short span and ignore earlier intermediate numbers.
3. If no marker appears, fall back to a task-specific extraction from the end of the text.
4. Normalize according to task type.
5. Preserve diagnostics so recovery can distinguish explicit marker extraction from fallback behavior.

Validation

Run:

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

Tests cover numeric traces, multiple-choice letters, yes/no outputs, dates, and symbolic answers.

Limitations

The extractor intentionally keeps normalization conservative. It does not infer answers from unsupported world knowledge and it does not decide whether the reasoning path is faithful.