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Get Started Free →datadrivenconstruction/parquet-converter
Convert construction data to/from Parquet format. Optimize storage, enable fast queries, and integrate with data lakehouses.
Passed 17 of 22 test cases with the skill, and 4 of 22 without it.
and 14 more in the full report
13 fixed4 passed either way5 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.
When defining custom schema configurations using the standard schema dataclass, what default `row_group_size` is assigned?
| Checked | Without skill | With skill |
|---|---|---|
| States that the default row_group_size is 100000 | Missed | Passed |
The instructions your model reads when the skill loads.
Data storage and processing challenges:
Convert construction data to Parquet format for efficient columnar storage, faster queries, and compatibility with data lakehouses.
import pandas as pd
from typing import Dict, Any, List, Optional, Union
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
import json
class CompressionType:
SNAPPY = "snappy"
GZIP = "gzip"
BROTLI = "brotli"
ZSTD = "zstd"
NONE = None
@dataclass
class ParquetSchema:
columns: Dict[str, str] # column_name: dtype
partitions: List[str] = field(default_factory=list)
row_group_size: int = 100000
test cases passed, vs 4 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.