The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ParserError
Message: Error tokenizing data. C error: Expected 7 fields in line 20, saw 9
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
for batch_idx, df in enumerate(csv_file_reader):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
return self.get_chunk()
~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
return self.read(nrows=size)
~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
) = self._engine.read( # type: ignore[attr-defined]
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
nrows
^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
chunks = self._reader.read_low_memory(nrows)
File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
pandas.errors.ParserError: Error tokenizing data. C error: Expected 7 fields in line 20, saw 9Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Brick public skill tables
Public skill vectors consumed by the Brick router. The Hugging Face dataset
regolo/brick-skill-tables contains one CSV file, skill_vectors.csv, with one
row per model and six capability values in [0,1]. Brick uses these values as
cold-start priors, so users do not need to measure a model that is already listed.
The CLI also ships richer JSON copies under this folder for offline initialization. The Hugging Face dataset is intentionally CSV-only; provenance and detailed measurement metadata remain in the Brick repository and generated records.
CSV schema
model,coding,creative_synthesis,instruction_following,math_reasoning,planning_agentic,world_knowledge
claude-haiku-4-5,0.60,0.60,0.68,0.62,0.58,0.70
qwen3.5-9b,0.58,0.45,0.72,0.75,0.62,0.76
The vector order is fixed and must never be changed:
[coding, creative_synthesis, instruction_following, math_reasoning, planning_agentic, world_knowledge]
Values are normalized scores, not raw benchmark percentages. They describe
capability priors only; cost and routing preferences belong in Brick
configuration. creative_synthesis is generally low-confidence because there
is no reliable public cross-model evaluation for it.
The bundled JSON records may include provenance fields such as source,
confidence, support, and sources. The public CSV deliberately contains
only the model id and vector so it stays easy to inspect and consume.
How Brick uses the table
At startup, Brick first checks its bundled cards. When a fresh card is requested,
the resolver can fetch skill_vectors.csv from Hugging Face, find the matching
model row, and cache the result locally. A measured profile from Brick can
supersede a benchmark prior locally.
Contributing
Add a model by pull request
- Fork this dataset and create a branch.
- Add one row to
skill_vectors.csv. - Keep the exact header and capability order; use a stable model id and values
between
0and1. - Sort rows by
modeland open a pull request againstmain. - In the PR description, explain the sources or evaluation method used for the vector. Public benchmark links and measured Brick probe results are preferred.
Example row:
my-model,0.70,0.45,0.72,0.80,0.60,0.75
For a measured profile, the recommended local command is:
brick skills extract my-model --publish
This asks for consent and updates the matching CSV row. Manual PRs are welcome when the model cannot be measured through Brick or when submitting a benchmark prior for review.
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