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sonichisonichi/sutando/skills/answer-format/SKILL.md

answer-format

Skill396 starsChanged 6 months agoMITView on GitHub

Deterministic final-answer normalizer — a last-step pass for any task that ends in a precise answer (a number, a short string, a comma-list). Applies the formatting conventions graders and downstream consumers expect without changing meaning. Or import it: Conservative. A normalizer that mangles a correct answer is worse than none, so every transform is either lossless or gated on a confident pattern; ambiguous input passes through unchanged. Sorting and article-dropping are opt-in because graders rarely want them. Precise answers routinely…

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SKILL.md

answer-format

Deterministic final-answer normalizer — a last-step pass for any task that ends in a precise answer (a number, a short string, a comma-list). Applies the formatting conventions graders and downstream consumers expect without changing meaning.

Usage:

python3 skills/answer-format/scripts/normalize.py --kind number -- "100 million"   # -> 100000000
python3 skills/answer-format/scripts/normalize.py --kind list --sort -- "b, a, c"   # -> a, b, c
echo "  \"Paris\"  " | python3 skills/answer-format/scripts/normalize.py --kind string  # -> Paris

Or import it:

from normalize import normalize_answer
normalize_answer("100 million", kind="number")   # "100000000"

What it does

kindtransforms (all lossless or pattern-gated)
numberworded magnitude → digits (3.5 billion→3500000000); strip thousands separators (1,234→1234); strip a single leading currency ($/€/£/¥) or trailing % when the rest is numeric
stringtrim whitespace; strip one layer of surrounding quotes; collapse internal whitespace; optionally drop a leading the/a/an (--drop-article) — interior words and capitalization untouched
listcomma-separated, one space after each comma; per-element trim; optional case-insensitive sort (--sort) and per-element numeric normalization (--number-items)
auto (default)infer kind from shape, then apply the above

Design principle

Conservative. A normalizer that mangles a correct answer is worse than none, so every transform is either lossless or gated on a confident pattern; ambiguous input passes through unchanged. Sorting and article-dropping are opt-in because graders rarely want them.

Why

Precise answers routinely fail grading or downstream matching on format alone — "100 million" vs "100000000", "1,234" vs "1234", '"Paris"' vs Paris. This is a general capability (any task ending in an exact answer benefits); it also implements the final-format step of the agent-eval solver contract, where a single format miss cost a correct L3 answer.

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