[New Skill]: data_engineering/unit_normalizer — structured unit normalization for agent pipelines

[rosspeili]

Skill Name

data_engineering/unit_normalizer

What should this skill do?

Parent discussion: supersede the basic utilities/unit_converter proposal in PR #166.

Problem: LLMs handle one-off unit math fine, but agents fail when normalizing mixed units across structured records (JSON rows, CSV columns, telemetry batches) or parsing compound/ambiguous unit strings under tool constraints.

Proposed capability (v1):

• action: normalize — convert a single value with explicit from/to units (subset of PR feat: add utilities/unit_converter skill #166, kept minimal)
• action: normalize_batch — convert a list of records or a column map; return per-row results + errors
• action: parse_quantity — parse compound strings (e.g. 5 ft 11 in, 100 km/h) into canonical SI value + unit metadata
• Structured errors only; no network; deterministic factors

Out of scope for v1:

• Live FX/crypto rates (separate finance skill if needed)
• New top-level category utilities/ — use data_engineering/

Success criteria:

• Solves something agents reliably get wrong (batch + parse + audit trail), not grade-school single conversions alone
• Full Skillware bundle: manifest, instructions, card.json, test_skill.py, init.py files, docs/skills page, README catalog row, CHANGELOG
• Issue approved before PR opened

Ideal Inputs & Outputs

Input (normalize_batch):
{
"action": "normalize_batch",
"records": [
{"id": "a", "value": 100, "from_unit": "km", "to_unit": "miles"},
{"id": "b", "value": 70, "from_unit": "kg", "to_unit": "lbs"}
]
}

Output:
{
"status": "ready",
"results": [
{"id": "a", "converted_value": 62.137119, "from_unit": "km", "to_unit": "miles"},
{"id": "b", "converted_value": 154.323584, "from_unit": "kg", "to_unit": "lbs"}
],
"errors": []
}

Input (parse_quantity):
{"action": "parse_quantity", "text": "5 ft 11 in", "target_unit": "m"}

Output:
{"status": "ready", "original_text": "5 ft 11 in", "value": 1.8034, "unit": "m"}

Targeted Models (if applicable)

Model Agnostic (All)

[rosspeili]

Hey @rizzoMartin any thoughts or tips on this? The idea is to introduce a skill that would make sense as a standalone module, and not basic unit converters. Would like to hear your opinion, and feel free to pick up if the original PR author is not willing to scale this to Skillware standards. <3