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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.
Passed 19 of 21 test cases with the skill, and 7 of 21 without it.
and 11 more in the full report
13 fixed7 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
The model returned a string wrapped in double quotes with extra padding: ` "Paris" `. Please execute the answer formatting tool to strip the external quotes and trim surrounding whitespace for final grading.
| Checked | Without skill | With skill |
|---|---|---|
| The response executes the answer formatting tool command using python3 skills/answer-format/scripts/normalize.py. | Missed | Passed |
The instructions your model reads when the skill loads.
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"
| kind | transforms (all lossless or pattern-gated) |
|---|---|
number | worded magnitude → digits (3.5 billion→3500000000); strip thousands separators (1,234→1234); strip a single leading currency ($/€/£/¥) or trailing % when the rest is numeric |
string | trim whitespace; strip one layer of surrounding quotes; collapse internal whitespace; optionally drop a leading the/a/an (--drop-article) — interior words and capitalization untouched |
list | comma-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 |
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 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.
test cases passed, vs 7 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.