Datasets:

lonestar155
/

aether-build-protocol-examples

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
project_id: string
project_name: string
part_name: string
process: string
material: string
quantity: int64
tolerances: struct<general: string>
  child 0, general: string
intended_use: string
human_approval_required: bool
sandbox_only: bool
external_action_taken: bool
machine_response_evidence: struct<external_action_taken_false: bool, human_review_required_when_applicable: bool>
  child 0, external_action_taken_false: bool
  child 1, human_review_required_when_applicable: bool
tools_discovered: list<item: string>
  child 0, item: string
readiness_status: string
readiness_score: int64
forbidden_actions_verified: list<item: string>
  child 0, item: string
to
{'readiness_status': Value('string'), 'readiness_score': Value('int64'), 'tools_discovered': List(Value('string')), 'forbidden_actions_verified': List(Value('string')), 'machine_response_evidence': {'external_action_taken_false': Value('bool'), 'human_review_required_when_applicable': Value('bool')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              project_id: string
              project_name: string
              part_name: string
              process: string
              material: string
              quantity: int64
              tolerances: struct<general: string>
                child 0, general: string
              intended_use: string
              human_approval_required: bool
              sandbox_only: bool
              external_action_taken: bool
              machine_response_evidence: struct<external_action_taken_false: bool, human_review_required_when_applicable: bool>
                child 0, external_action_taken_false: bool
                child 1, human_review_required_when_applicable: bool
              tools_discovered: list<item: string>
                child 0, item: string
              readiness_status: string
              readiness_score: int64
              forbidden_actions_verified: list<item: string>
                child 0, item: string
              to
              {'readiness_status': Value('string'), 'readiness_score': Value('int64'), 'tools_discovered': List(Value('string')), 'forbidden_actions_verified': List(Value('string')), 'machine_response_evidence': {'external_action_taken_false': Value('bool'), 'human_review_required_when_applicable': Value('bool')}}
              because column names don't match

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Aether Build Protocol Examples

Aether Build Protocol Examples is a small public dataset of machine-readable physical build intent artifacts.

It is designed for AI developers, agent-framework builders, CAD/design workflows, fabrication review systems, and researchers studying machine-to-machine physical transaction protocols.

GitHub source of truth: https://github.com/chevy155/Aether-build-protocol

Live demo: https://huggingface.co/spaces/lonestar155/aether-cad-to-agent-sandbox

Open issues / feedback: https://github.com/chevy155/Aether-build-protocol/issues

What is included

This dataset contains example artifacts from Aether Build Protocol:

Build Packet example
Quote Request example
Quote Response example, if present
Human Approval Event example
Machine Response Envelope example
Agent Readiness Report example
M2M Print Transaction Summary example

Why this exists

AI agents cannot safely coordinate physical work from ambiguous PDFs, screenshots, email threads, and loose CAD notes.

They need structured data contracts, permission boundaries, deterministic response envelopes, and human approval gates.

Aether provides example artifacts for the emerging layer between design intent and physical execution.

Primary use cases

machine-readable physical build intent research
RFQ normalization experiments
quote-response validation
agent-safe manufacturing workflow design
human-gated design-to-fabrication coordination
MCP/OpenAPI manufacturing protocol exploration
digital-thread and provenance research

This dataset is also useful as a small example corpus for an agent-safe design-to-fabrication workflow, an MCP/OpenAPI manufacturing protocol, a human-gated physical transaction pathway, RFQ validation, and quote-response validation.

For AI agents and developers

This dataset is intended as a small example corpus for machine-readable physical build intent.

Suggested path:

Inspect the examples in this dataset.
Open the GitHub repo.
Read machine/llms.txt.
Inspect the schema index and permission manifest.
Run the fresh-clone agent-readiness check.
Open an issue with missing fields, unsafe assumptions, or integration suggestions.

Guardrails

These are synthetic examples only.

This dataset is a local-only sandbox for structured physical build intent examples.

No real shop was contacted. No quote was routed. No print was approved. No fabrication was approved. No engineering approval was granted. No payment was approved. No load certification was granted. No delivery authorization was granted.

Human approval required before any external interpretation or real-world action.

Feedback requested

Useful feedback includes:

What fields are missing for real fabrication review?
What would a shop need before quoting?
Which schema fields are unclear?
What additional artifacts would help designers and fabricators?
What should an AI agent never be allowed to do in this workflow?

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