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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 8 new columns ({'rps', 'p95_ms', 'date', 'benchmark_id', 'p50_ms', 'error_rate_pct', 'endpoint', 'p99_ms'}) and 8 missing columns ({'path', 'response_size_bytes', 'status_code', 'request_id', 'request_size_bytes', 'timestamp', 'response_time_ms', 'user_agent'}).
This happened while the csv dataset builder was generating data using
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/tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_04.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_04.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_05.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_05.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_06.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_06.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_07.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_07.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_08.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_08.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_09.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_09.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_10.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_10.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_11.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_11.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_12.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_12.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_13.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_13.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_14.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_14.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_15.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_15.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_16.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_16.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_17.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_17.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_18.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_18.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_19.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/moe-training/ml_training_csv_19.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_00.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_00.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_01.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_01.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_02.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_02.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_03.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_03.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_04.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_04.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_05.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_05.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_06.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_06.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_07.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_07.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_08.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_08.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_09.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_09.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_10.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_10.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_11.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_11.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_12.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_12.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_13.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_13.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_14.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_14.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_15.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_15.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_16.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_16.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_17.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_17.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_18.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_18.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_19.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/quantum_csv_19.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/sae_activations_1.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/sae_activations_1.csv), /tmp/hf-datasets-cache/medium/datasets/83650276318908-config-parquet-and-info-JiaaqiLiu-SkillArena-data-fe210123/hub/datasets--JiaaqiLiu--SkillArena-datasets/snapshots/d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/sae_activations_2.csv (origin=hf://datasets/JiaaqiLiu/SkillArena-datasets@d58b95393ed488d394644dbcd196a566c484ee1f/skills/sparse-autoencoder-training/sae_activations_2.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
benchmark_id: string
endpoint: string
method: string
p50_ms: double
p95_ms: double
p99_ms: double
rps: double
error_rate_pct: double
date: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1296
to
{'request_id': Value('string'), 'timestamp': Value('string'), 'method': Value('string'), 'path': Value('string'), 'status_code': Value('int64'), 'response_time_ms': Value('float64'), 'request_size_bytes': Value('int64'), 'response_size_bytes': Value('int64'), 'user_agent': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 8 new columns ({'rps', 'p95_ms', 'date', 'benchmark_id', 'p50_ms', 'error_rate_pct', 'endpoint', 'p99_ms'}) and 8 missing columns ({'path', 'response_size_bytes', 'status_code', 'request_id', 'request_size_bytes', 'timestamp', 'response_time_ms', 'user_agent'}).
This happened while the csv dataset builder was generating data using
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Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
request_id string | timestamp string | method string | path string | status_code int64 | response_time_ms float64 | request_size_bytes int64 | response_size_bytes int64 | user_agent string |
|---|---|---|---|---|---|---|---|---|
req-62ab52375b46 | 2021-08-20T13:13:37Z | POST | /api/v1/products/97c230d8 | 201 | 3,352.4 | 1,066 | 110,057 | axios/1.6 |
req-0af7eb36b3c2 | 2020-12-03T08:23:02Z | PATCH | /api/v1/products/1ade7bbb | 204 | 4,839.8 | 11,244 | 34,258 | curl/7.88 |
req-854a43e5d51c | 2020-06-25T17:13:52Z | DELETE | /api/v1/orders/2b31c87a | 409 | 4,993.5 | 18,719 | 246,096 | okhttp/4.12 |
req-26f75a3bace5 | 2025-07-14T01:11:18Z | POST | /api/v1/orders/af0f1ecc | 503 | 3,517.9 | 25,729 | 81,070 | Mozilla/5.0 |
req-71a85e24d3de | 2021-04-11T11:41:30Z | DELETE | /api/v1/search/9e235f4a | 400 | 3,213 | 24,279 | 358,332 | axios/1.6 |
req-e898f261c582 | 2021-06-15T11:46:58Z | DELETE | /api/v1/products/71323020 | 200 | 3,655.3 | 17,461 | 488,164 | Mozilla/5.0 |
req-e4f8eddde630 | 2025-01-20T01:10:01Z | GET | /api/v1/auth/e63500e4 | 500 | 934.2 | 15,173 | 496,362 | python-requests/2.31 |
req-8d0cdf9e3556 | 2024-12-23T01:30:22Z | PUT | /api/v1/products/14f9c044 | 200 | 4,933.9 | 11,407 | 220,590 | Mozilla/5.0 |
req-dd354e2d5591 | 2025-03-04T02:13:57Z | DELETE | /api/v1/search/b8861acf | 400 | 2,458.2 | 33,487 | 276,043 | curl/7.88 |
req-a89cd20d8d67 | 2020-06-07T06:12:01Z | DELETE | /api/v1/users/fe51a0fd | 200 | 1,385.7 | 1,037 | 59,902 | Mozilla/5.0 |
req-cf9943f78d4e | 2025-08-10T09:21:05Z | DELETE | /api/v1/users/71bfd879 | 503 | 1,681.3 | 31,907 | 44,189 | axios/1.6 |
req-cbeeb7ed14be | 2020-05-01T13:20:03Z | PUT | /api/v1/auth/9aeff4fd | 201 | 2,877.5 | 23,358 | 128,589 | curl/7.88 |
req-1b4e605ec2be | 2025-07-23T00:11:18Z | DELETE | /api/v1/orders/ea322f60 | 201 | 333.8 | 42,878 | 200,636 | axios/1.6 |
req-6931ea278e53 | 2022-03-20T00:21:18Z | PUT | /api/v1/users/188df6a6 | 400 | 1,561.1 | 10,893 | 362,938 | okhttp/4.12 |
req-d99b8d85060e | 2020-12-26T05:34:58Z | PUT | /api/v1/orders/84320172 | 404 | 4,468.4 | 30,449 | 385,672 | python-requests/2.31 |
req-93b08a7a8b7a | 2025-06-21T04:00:42Z | DELETE | /api/v1/orders/d31482fa | 502 | 1,665.6 | 17,281 | 279,582 | axios/1.6 |
req-236c8aa746ab | 2022-10-23T08:19:25Z | POST | /api/v1/orders/ad64cf67 | 503 | 3,855.7 | 14,052 | 257,663 | Mozilla/5.0 |
req-94d85d9d1d74 | 2020-07-12T00:30:39Z | GET | /api/v1/products/dcca778c | 409 | 4,804.3 | 19,580 | 85,236 | okhttp/4.12 |
req-50fbf722f3f4 | 2025-03-07T08:39:45Z | PUT | /api/v1/users/fc4241a9 | 200 | 1,380.3 | 15,399 | 319,529 | axios/1.6 |
req-f263af90d416 | 2023-11-22T22:15:53Z | DELETE | /api/v1/auth/ec747899 | 404 | 731 | 6,687 | 12,020 | okhttp/4.12 |
req-7fcd8ac37119 | 2022-01-03T19:29:34Z | DELETE | /api/v1/products/6eca77da | 400 | 3,063.6 | 24,253 | 60,467 | axios/1.6 |
req-6a4338c4afee | 2024-08-09T10:59:36Z | PATCH | /api/v1/auth/22f5352c | 502 | 156.2 | 5,183 | 67,554 | axios/1.6 |
req-98496a3c83c7 | 2020-06-07T08:15:00Z | PUT | /api/v1/search/0f157e67 | 403 | 2,643.8 | 7,806 | 406,598 | Mozilla/5.0 |
req-960a47a6a509 | 2023-02-26T15:36:08Z | PUT | /api/v1/users/a19d5e94 | 404 | 4,406.5 | 22,337 | 401,943 | Mozilla/5.0 |
req-57033657f09e | 2024-03-16T00:30:29Z | PUT | /api/v1/products/cc50e5e2 | 404 | 608.3 | 13,320 | 347,788 | curl/7.88 |
req-f8168b539966 | 2020-12-03T14:30:30Z | PATCH | /api/v1/users/a95c45bd | 204 | 1,858.7 | 39,833 | 237,844 | okhttp/4.12 |
req-a0bfb1ba7657 | 2021-09-20T02:06:04Z | DELETE | /api/v1/auth/4e4ca92c | 301 | 279.7 | 11,194 | 468,933 | okhttp/4.12 |
req-13b38628643f | 2023-07-16T03:50:37Z | DELETE | /api/v1/orders/832ec181 | 200 | 2,916.2 | 41,555 | 366,923 | axios/1.6 |
req-378154fe6397 | 2022-07-02T20:54:02Z | DELETE | /api/v1/auth/99196372 | 404 | 3,374.7 | 13,594 | 71,518 | axios/1.6 |
req-76434e72b265 | 2024-03-15T01:41:11Z | PATCH | /api/v1/orders/547a22de | 200 | 1,149.6 | 38,220 | 469,532 | python-requests/2.31 |
req-b159a13aa4f0 | 2022-08-24T04:20:53Z | POST | /api/v1/search/74da2951 | 500 | 3,605.9 | 1,041 | 152,612 | axios/1.6 |
req-f4674882e108 | 2020-04-21T04:50:20Z | POST | /api/v1/search/7c784f7f | 401 | 2,368.7 | 21,862 | 186,384 | curl/7.88 |
req-91c48171db56 | 2021-02-22T14:30:08Z | DELETE | /api/v1/users/a2ffd17d | 301 | 3,288.4 | 9,372 | 224,161 | okhttp/4.12 |
req-570633eadeee | 2025-05-24T16:13:10Z | DELETE | /api/v1/auth/d2cd966b | 502 | 891.6 | 27,573 | 176,321 | okhttp/4.12 |
req-c0a788c97f1b | 2020-07-05T02:17:18Z | DELETE | /api/v1/products/8e08b831 | 201 | 4,595.1 | 46,108 | 329,150 | python-requests/2.31 |
req-f97e46c60a84 | 2025-10-27T15:14:57Z | GET | /api/v1/auth/9103e025 | 409 | 3,693.7 | 24,245 | 61,271 | Mozilla/5.0 |
req-5dc8aab77ff7 | 2021-02-06T20:27:45Z | PATCH | /api/v1/auth/8ff6e073 | 403 | 2,287.9 | 43,013 | 216,224 | python-requests/2.31 |
req-49c79c7fb65f | 2020-02-18T01:53:44Z | GET | /api/v1/orders/cf6188fc | 301 | 2,825.1 | 48,420 | 163,914 | curl/7.88 |
req-e86f86269bb4 | 2024-07-01T05:19:55Z | POST | /api/v1/orders/ad4eac38 | 503 | 589.2 | 46,997 | 490,076 | python-requests/2.31 |
req-457d14f1b606 | 2022-08-12T00:52:58Z | POST | /api/v1/auth/acaec942 | 301 | 1,606 | 46,081 | 369,779 | axios/1.6 |
req-0ac783aa4ee2 | 2020-12-13T07:40:09Z | POST | /api/v1/products/088fd382 | 404 | 309.1 | 40,365 | 216,997 | python-requests/2.31 |
req-e7889401a64d | 2022-08-18T15:14:08Z | GET | /api/v1/users/58715d00 | 200 | 1,199.1 | 5,843 | 55,245 | curl/7.88 |
req-76ef6203d27c | 2024-08-16T03:35:31Z | GET | /api/v1/products/319fc754 | 400 | 4,891.4 | 14,492 | 195,466 | Mozilla/5.0 |
req-f017d6958146 | 2020-07-02T22:07:26Z | GET | /api/v1/users/9f6d3a33 | 201 | 3,689.6 | 34,079 | 419,189 | curl/7.88 |
req-0891399082f9 | 2024-05-09T16:32:42Z | PUT | /api/v1/search/c808b090 | 401 | 4,411.8 | 40,224 | 56,418 | python-requests/2.31 |
req-e645603a5dc1 | 2022-12-02T07:04:46Z | GET | /api/v1/search/5d672cfd | 502 | 1,040.5 | 19,860 | 209,717 | axios/1.6 |
req-41eda6036aa6 | 2023-07-10T08:50:38Z | PUT | /api/v1/auth/eb125c39 | 401 | 1,979.3 | 40,112 | 464,063 | axios/1.6 |
req-2d5be50b77c5 | 2025-12-20T18:06:17Z | POST | /api/v1/users/b284615f | 404 | 4,342.9 | 29,684 | 172,667 | Mozilla/5.0 |
req-c1ed1c37ef6d | 2025-02-23T18:35:24Z | DELETE | /api/v1/users/b8777ea9 | 200 | 796.7 | 23,165 | 211,407 | python-requests/2.31 |
req-5bfdbcac4fb7 | 2024-06-11T12:59:10Z | PATCH | /api/v1/auth/7f6700e5 | 204 | 4,464.4 | 35,701 | 315,376 | curl/7.88 |
req-f2539c012099 | 2025-12-25T17:57:10Z | DELETE | /api/v1/search/bb14952d | 400 | 37.4 | 49,643 | 408,083 | curl/7.88 |
req-31eac22d6c18 | 2021-02-27T10:27:02Z | GET | /api/v1/orders/c4683c73 | 404 | 4,647.7 | 23,547 | 275,253 | axios/1.6 |
req-42f5b2e1bac0 | 2022-11-16T05:34:55Z | POST | /api/v1/users/947ef385 | 500 | 2,476.7 | 9,741 | 339,206 | curl/7.88 |
req-736219d10139 | 2025-09-21T13:54:03Z | GET | /api/v1/auth/d4125f97 | 201 | 2,502.6 | 38,219 | 277,232 | python-requests/2.31 |
req-9557db9cad39 | 2023-10-23T09:33:21Z | PATCH | /api/v1/users/bd098248 | 401 | 2,884.3 | 2,275 | 130,148 | python-requests/2.31 |
req-50c7ff855a06 | 2021-12-25T07:00:37Z | PUT | /api/v1/auth/b0565dbc | 301 | 3,254 | 28,620 | 270,338 | curl/7.88 |
req-eb0ff20d823e | 2025-12-22T20:33:57Z | PUT | /api/v1/users/ad473ead | 502 | 2,910.5 | 44,875 | 457,580 | curl/7.88 |
req-c584a8b792a6 | 2025-10-26T23:00:17Z | POST | /api/v1/auth/ab2d39d2 | 503 | 3,897.3 | 34,112 | 43,432 | okhttp/4.12 |
req-3d2d1c7799e0 | 2020-12-25T07:59:14Z | GET | /api/v1/orders/282a4318 | 201 | 1,876.5 | 25,078 | 354,313 | python-requests/2.31 |
req-933a9e4fabda | 2021-03-12T12:38:39Z | PATCH | /api/v1/products/2a255bb8 | 400 | 1,390 | 43,983 | 199,560 | curl/7.88 |
req-5f043d735529 | 2025-05-17T22:19:26Z | POST | /api/v1/auth/45fc626f | 403 | 2,235.3 | 41,464 | 313,648 | axios/1.6 |
req-25565a3aa03d | 2021-11-01T19:16:27Z | GET | /api/v1/orders/9e93cae9 | 301 | 4,056.4 | 11,550 | 53,129 | python-requests/2.31 |
req-354b2ea51770 | 2021-12-03T10:25:53Z | PATCH | /api/v1/search/46b55bc5 | 502 | 4,174.7 | 19,521 | 208,900 | python-requests/2.31 |
req-ed029bb965b4 | 2020-06-28T15:57:35Z | PATCH | /api/v1/orders/f72a0105 | 403 | 2,702.5 | 44,086 | 283,712 | axios/1.6 |
req-6355071e695d | 2025-05-28T12:26:58Z | POST | /api/v1/orders/408a3246 | 404 | 767.5 | 26 | 456,534 | python-requests/2.31 |
req-4df5a1290783 | 2022-05-22T18:11:30Z | POST | /api/v1/search/485787a9 | 204 | 685.4 | 15,297 | 121,689 | Mozilla/5.0 |
req-bd38bd1aff48 | 2022-05-23T05:52:47Z | PATCH | /api/v1/users/c855794a | 204 | 4,637.7 | 41,443 | 320,785 | axios/1.6 |
req-7fae6856a5e7 | 2020-03-27T13:17:31Z | PATCH | /api/v1/products/0a64151c | 409 | 4,816.6 | 40,535 | 280,078 | Mozilla/5.0 |
req-2b539a8ca144 | 2020-05-19T09:26:40Z | POST | /api/v1/users/b125365c | 502 | 1,161.2 | 39,534 | 252,958 | okhttp/4.12 |
req-d6e621bcb8e4 | 2024-06-10T03:48:28Z | GET | /api/v1/orders/04827cb7 | 200 | 1,870.7 | 34,091 | 156,983 | axios/1.6 |
req-8282496eb723 | 2021-05-28T22:57:53Z | POST | /api/v1/orders/35bdda00 | 403 | 3,255.3 | 7,842 | 121,596 | Mozilla/5.0 |
req-0295728963e5 | 2021-09-27T11:15:41Z | PUT | /api/v1/orders/a2458ff1 | 400 | 1,106.1 | 22,041 | 228,982 | Mozilla/5.0 |
req-8387f2d04eaf | 2023-04-11T07:23:02Z | GET | /api/v1/orders/f14e5f46 | 404 | 3,721.3 | 38,728 | 264,648 | curl/7.88 |
req-822cd1de6331 | 2020-07-26T06:04:18Z | GET | /api/v1/products/08fcf992 | 200 | 3,329.2 | 13,742 | 409,327 | Mozilla/5.0 |
req-f81b347b6dd2 | 2020-07-02T13:31:26Z | GET | /api/v1/search/bdd16721 | 200 | 474.8 | 47,025 | 410,834 | axios/1.6 |
req-8a132bc383c2 | 2021-12-17T23:36:13Z | POST | /api/v1/search/08b2ee8d | 502 | 3,742.4 | 30,072 | 111,571 | python-requests/2.31 |
req-d77e861b6f8f | 2021-08-17T09:51:01Z | DELETE | /api/v1/products/597d8331 | 503 | 48.1 | 19,849 | 267,414 | Mozilla/5.0 |
req-2cce1689f8a9 | 2022-12-23T10:46:59Z | PATCH | /api/v1/orders/13b1cf1e | 201 | 1,236.2 | 28,708 | 44,188 | python-requests/2.31 |
req-dac9fcdd6fcc | 2022-03-26T10:05:45Z | PUT | /api/v1/users/5a5b5623 | 502 | 4,898.6 | 24,574 | 103,398 | Mozilla/5.0 |
req-8a9d13361dee | 2025-07-07T21:57:29Z | GET | /api/v1/orders/083d9d37 | 400 | 3,791.6 | 19,693 | 387,523 | Mozilla/5.0 |
req-c87e4c7961d5 | 2021-09-04T11:49:00Z | DELETE | /api/v1/search/7f224eb8 | 200 | 2,057.8 | 24,045 | 284,298 | axios/1.6 |
req-85657c4f2913 | 2020-11-18T17:59:10Z | DELETE | /api/v1/auth/bb2ac486 | 401 | 598.1 | 16,548 | 192,549 | okhttp/4.12 |
req-f5320c5934d2 | 2025-04-05T20:42:22Z | PUT | /api/v1/users/d41bfb81 | 502 | 1,325.4 | 27,288 | 25,116 | Mozilla/5.0 |
req-6b7e9ebc3336 | 2021-10-21T01:49:48Z | GET | /api/v1/search/6109160e | 403 | 174.7 | 32,709 | 483,758 | python-requests/2.31 |
req-6e5776da1194 | 2020-02-21T01:38:37Z | POST | /api/v1/search/ed97c3a3 | 400 | 1,730 | 40,872 | 472,221 | Mozilla/5.0 |
req-8a623a4b27ae | 2020-02-27T13:54:50Z | PUT | /api/v1/orders/9256e2c0 | 403 | 2,488.3 | 35,900 | 121,058 | Mozilla/5.0 |
req-3c987ce1e9c2 | 2022-09-02T17:55:55Z | DELETE | /api/v1/search/a74e3af8 | 403 | 1,994.8 | 11,398 | 497,449 | curl/7.88 |
req-005c079ee112 | 2024-01-18T01:57:00Z | PATCH | /api/v1/search/c2398046 | 503 | 3,400.8 | 26,449 | 497,155 | curl/7.88 |
req-8970ac3e9b73 | 2025-03-05T12:24:01Z | PUT | /api/v1/products/9a5e17d4 | 409 | 2,274.5 | 21,521 | 273,338 | axios/1.6 |
req-9731d2d06c21 | 2022-09-10T22:09:34Z | PUT | /api/v1/auth/341216aa | 500 | 3,738.9 | 30,135 | 38,412 | axios/1.6 |
req-32c39d574b77 | 2021-04-27T20:53:11Z | PUT | /api/v1/search/a6461bec | 200 | 4,434.1 | 21,614 | 135,815 | Mozilla/5.0 |
req-e8053c2fb0b6 | 2025-01-19T01:42:08Z | POST | /api/v1/products/d3dafba2 | 502 | 1,235.1 | 8,977 | 80,583 | python-requests/2.31 |
req-ee6cd62e00ed | 2021-06-26T10:10:52Z | POST | /api/v1/orders/ba31e979 | 301 | 1,730.5 | 40,381 | 315,455 | python-requests/2.31 |
req-03c94f786e9e | 2024-07-11T02:39:22Z | PUT | /api/v1/users/d9e101a3 | 200 | 935.9 | 45,516 | 243,037 | okhttp/4.12 |
req-b75f5a52b6cb | 2020-02-17T11:59:26Z | DELETE | /api/v1/search/d0939061 | 201 | 3,130.6 | 20,667 | 200,048 | python-requests/2.31 |
req-812cdf11c56c | 2023-06-04T04:06:39Z | PATCH | /api/v1/orders/2c165949 | 400 | 1,164.1 | 43,156 | 200,079 | okhttp/4.12 |
req-acb635a14e91 | 2022-04-03T04:49:28Z | PATCH | /api/v1/auth/200f159e | 403 | 3,778.7 | 24,983 | 22,927 | python-requests/2.31 |
req-fd8c116f6a5e | 2020-01-04T21:26:55Z | PATCH | /api/v1/users/4e122273 | 200 | 3,944.6 | 7,209 | 166,637 | Mozilla/5.0 |
req-e054ec3dca6c | 2020-01-11T17:32:36Z | POST | /api/v1/orders/2d6e7bdf | 401 | 4,876.9 | 15,558 | 70,904 | Mozilla/5.0 |
req-1621f5ac28fd | 2025-09-28T14:00:56Z | PATCH | /api/v1/users/76c6e278 | 409 | 3,821.1 | 14,364 | 435,554 | okhttp/4.12 |
End of preview.
SkillArena Offline Datasets
Offline evaluation data for SkillArena — a validated automatic benchmark generation framework for AI agent skills, targeting NeurIPS 2026 Datasets & Benchmarks Track.
Overview
This dataset provides domain-specific task input data for 289 AI agent skills across 13 domains. Each skill has 50 curated data files designed as meaningful agent task inputs — files an agent could receive and act upon (analyze, transform, validate, or generate from). The data is used by SkillArena's 10-stage evaluation pipeline to automatically generate validated evaluation tasks, graders, and pilot tests.
| Metric | Value |
|---|---|
| Total skills | 289 (16 original + 73 community + 200 new community) |
| Total files | 14,450 (skills) + 160 (original-skills) |
| Files per skill | 50 |
| Dataset size | ~126 MB |
| File formats | 5 types: CSV, JSON, YAML, Markdown, Plain Text |
| Generation method | Deterministic, domain-aware, seed-reproducible |
Dataset Structure
skillarena-datasets/
├── skills/ # 289 skills × 50 files = 14,450 files
│ ├── scikit-learn/ # ML training domain
│ │ ├── ml_training_csv_00.csv # Classification dataset
│ │ ├── ml_training_csv_01.csv # Regression dataset
│ │ ├── ... # 18 more CSV files
│ │ ├── ml_training_json_00.json # Model config
│ │ ├── ... # 11 more JSON files
│ │ ├── ml_training_yaml_00.yaml # Training pipeline config
│ │ ├── ... # 9 more YAML files
│ │ ├── ml_training_md_00.md # Model card
│ │ ├── ... # 4 more Markdown files
│ │ ├── ml_training_txt_00.txt # Training log
│ │ └── ... # 2 more text files
│ ├── langchain/ # NLP/LLM domain
│ ├── grafana-dashboards/ # DevOps/Infra domain
│ ├── rdkit/ # Chemistry domain
│ └── ... # 285 more skill directories
├── original-skills/ # 16 core skills (hand-curated, 10 files each)
│ ├── pdf/ # PDFs, extraction configs
│ ├── xlsx/ # Spreadsheets, CSV data
│ ├── frontend-design/ # HTML, CSS, JS, design specs
│ └── ... # 13 more original skills
└── index.json # Dataset index with file metadata
File Distribution Per Skill
Every skill receives exactly 50 files with a fixed distribution:
| Count | Format | Purpose |
|---|---|---|
| 20 | .csv |
Tabular data for analysis and transformation tasks |
| 12 | .json |
Structured data, configs, and specifications |
| 10 | .yaml |
Configuration and specification files |
| 5 | .md |
Documents for analysis and generation tasks |
| 3 | .txt |
Plain text for processing tasks |
Total across all skills: 5,780 CSV + 3,468 JSON + 2,890 YAML + 1,445 MD + 867 TXT = 14,450 files
Domain Classification
Skills are classified into 13 domains, each with a specialized data generator:
| Domain | Skills | Example Skills |
|---|---|---|
| NLP / LLM | 82 | langchain, llamaindex, huggingface-tokenizers, dspy, outlines |
| Frontend / Web | 53 | react-state-management, tailwind-design-system, nextjs-app-router-patterns |
| Bioinformatics | 27 | biopython, scanpy, anndata, pysam, clinical-decision-support |
| Generic | 23 | file-organizer, contract-analyzer, financial-calc, create-plan |
| Backend | 21 | kafka-producer-consumer, redis-cache-manager, database-schema-designer |
| DevOps / Infra | 18 | k8s-manifest-generator, grafana-dashboards, prometheus-configuration |
| Data Analysis | 16 | pandas (dask, geopandas), seaborn, matplotlib, statsmodels |
| ML Training | 15 | scikit-learn, pytorch-lightning, deepspeed, weights-and-biases |
| Documentation | 12 | changelog-generator, architecture-diagram-creator, cli-demo-generator |
| Chemistry | 9 | rdkit, deepchem, matchms, molfeat, torchdrug |
| Quantum | 5 | qiskit, cirq, pennylane, qutip, sparse-autoencoder-training |
| Security | 5 | secret-scanner, jwt-token-validator, sast-configuration |
| Testing | 3 | bats-testing-patterns, e2e-testing-patterns, temporal-python-testing |
Data Content Examples
CSV — ML Training (scikit-learn)
feature_0,feature_1,feature_2,feature_3,target,split
0.83,0.17,-0.45,1.02,1,train
-0.22,0.91,0.33,-0.67,0,train
JSON — NLP/LLM (langchain)
{
"chain_name": "qa_retrieval_chain",
"chain_type": "stuff",
"retriever": {
"type": "vectorstore",
"search_type": "similarity",
"search_kwargs": {"k": 4}
}
}
YAML — DevOps (k8s-manifest-generator)
apiVersion: apps/v1
kind: Deployment
metadata:
name: api-gateway
namespace: production
spec:
replicas: 3
strategy:
type: RollingUpdate
How It's Used
SkillArena's pipeline uses these files in the Data Acquisition stage:
SKILL.md → Analyze → Plan → Synthesize → [Data Acquisition] → Grade → Validate → Meta-Eval → Pilot → Promote
↑
This dataset provides
input data for tasks
The OfflineDataProvider is the first source in an 8-provider chain:
Offline Dataset (this) → GitHub → Web Search → Kaggle → HuggingFace → Programmatic → Binary Renderer → LLM Synthesis
When a task is generated, the provider:
- Looks up
skills/{skill_name}/directory - Picks the first unused file (sorted alphabetically)
- Copies it to
task_dir/input/input.{ext}as the agent's input
Original Skills (16)
These hand-curated skills have been validated through the full SkillArena pipeline:
| Skill | Domain | Quality Score | Files |
|---|---|---|---|
| frontend-design | Frontend/UI | 9.95/10 | 10 |
| theme-factory | Design Systems | 9.95/10 | 10 |
| web-artifacts-builder | Web Development | 9.95/10 | 10 |
| webapp-testing | Testing | 9.84/10 | 10 |
| mcp-builder | MCP Protocol | 9.44/10 | 10 |
| algorithmic-art | Creative Coding | 9.43/10 | 10 |
| canvas-design | Visual Design | 9.42/10 | 10 |
| internal-comms | Communication | 9.41/10 | 10 |
| slack-gif-creator | Animation | 9.41/10 | 10 |
| brand-guidelines | Brand Design | 9.41/10 | 10 |
| doc-coauthoring | Documentation | 9.38/10 | 10 |
| skill-creator | Meta-Skills | 9.37/10 | 10 |
| pptx | Presentations | 8.60/10 | 10 |
| docx | Documents | 7.86/10 | 10 |
| PDF Processing | 7.36/10 | 10 | |
| xlsx | Spreadsheets | 7.17/10 | 10 |
Average quality score: 9.12/10 | Validation pass rate: 100% (162/162 tasks)
Generation & Validation
All 14,450 files are deterministically generated using domain-specific generators with per-skill random seeds (hash(skill_name) & 0xFFFFFFFF) for full reproducibility.
Validation checks (all passing):
- File count: exactly 50 per skill (20 CSV + 12 JSON + 10 YAML + 5 MD + 3 TXT)
- Minimum file size: >= 500 bytes
- Format validity: CSV parseable with consistent column counts, JSON/YAML parseable
- CSV integrity: header + data rows, no empty columns, consistent column count
- Markdown: has headers and substantial content
- No source code leakage: no Python/Java/Go/JS imports in data files
- Filename convention:
{domain}_{ext}_{NN}.{ext}pattern
Regeneration
# Generate all skills
python -m scripts.generate_task_inputs.main --force
# Single skill
python -m scripts.generate_task_inputs.main --skill scikit-learn --force
# Validate only
python -m scripts.generate_task_inputs.main --validate-only
# Deep validation
python scripts/generate_task_inputs/deep_validate.py
Usage
from skillarena.pipeline.orchestrator import PipelineOrchestrator
orchestrator = PipelineOrchestrator(
output_dir="pipeline_output",
offline_data_dir="skillarena-datasets", # Point to this dataset
seed=42
)
state = await orchestrator.run(skill_path="skills/pdf/SKILL.md")
Citation
@inproceedings{liu2026skillarena,
title={SkillArena: Validated Automatic Benchmark Generation for AI Agent Skills},
author={Liu, Jiaqi},
booktitle={NeurIPS 2026 Datasets and Benchmarks Track},
year={2026}
}
License
MIT License — see the SkillArena repository for details.
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