---
name: sonichi-answer-format
description: "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."
license: "MIT"
metadata:
  decimalai-skill-id: "914a7175-c952-4d67-8d3b-3cd467c54b40"
  decimalai-version-id: "318ce364-e831-4f32-8452-658824e3486e"
  decimalai-version: "1"
  decimalai-body-sha256: "4dc3643abeb53e5f345bc6e7f440cc7d3cfc6fc05865dba3a7431f2aed2395e7"
  decimalai-name: "sonichi/answer-format"
  decimalai-format: "1"
  decimalai-invocation: "model"
  decimalai-category: "ai-ml"
  decimalai-source: "https://app.decimal.ai/s/sonichi-answer-format@1/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**:
```bash
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:
```python
from normalize import normalize_answer
normalize_answer("100 million", kind="number")   # "100000000"
```

## What it does

| 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 |

## 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.