Datasets:

rmems
/

incident-response-oncall-trajectories

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
goal: string
plan: string
true_root_cause: string
red_herring: string
forbidden_diagnosis: string
required_evidence: list<item: string>
  child 0, item: string
steps: list<item: string>
  child 0, item: string
outcome: string
reward: struct<success: bool, red_herring_dismissed: int64, forbidden_diagnosis_avoided: bool, replica_lag_s (... 241 chars omitted)
  child 0, success: bool
  child 1, red_herring_dismissed: int64
  child 2, forbidden_diagnosis_avoided: bool
  child 3, replica_lag_s_before: int64
  child 4, replica_lag_s_after: double
  child 5, p99_s_before: double
  child 6, p99_s_after: double
  child 7, analytics_select_killed: bool
  child 8, cost_steps: int64
  child 9, wrong_mitigation_rollback: int64
  child 10, handoff_dba: bool
  child 11, writer_dns_updated: bool
  child 12, five_xx_rps_end: double
meta: struct<factory: string, round: int64, generator: string, opensre_seed: string>
  child 0, factory: string
  child 1, round: int64
  child 2, generator: string
  child 3, opensre_seed: string
false_lead: struct<claim: string, survived_steps: list<item: int64>, falsified_at: int64>
  child 0, claim: string
  child 1, survived_steps: list<item: int64>
      child 0, item: int64
  child 2, falsified_at: int64
kind: string
remediate: string
rca: string
to
{'id': Value('string'), 'goal': Value('string'), 'plan': Value('string'), 'kind': Value('string'), 'steps': List(Json(decode=True)), 'outcome': Value('string'), 'reward': {'success': Value('bool'), 'steps': Value('int64'), 'false_lead_steps': Value('int64'), 'http_retries': Value('int64')}, 'false_lead': {'claim': Value('string'), 'survived_steps': List(Value('int64')), 'falsified_at': Value('int64')}, 'rca': Value('string'), 'remediate': Value('string'), 'meta': {'factory': Value('string'), 'round': Value('int64'), 'generator': Value('string'), 'plant': Value('string'), 'alert_source': Value('string'), 'ticket': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in 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/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              goal: string
              plan: string
              true_root_cause: string
              red_herring: string
              forbidden_diagnosis: string
              required_evidence: list<item: string>
                child 0, item: string
              steps: list<item: string>
                child 0, item: string
              outcome: string
              reward: struct<success: bool, red_herring_dismissed: int64, forbidden_diagnosis_avoided: bool, replica_lag_s (... 241 chars omitted)
                child 0, success: bool
                child 1, red_herring_dismissed: int64
                child 2, forbidden_diagnosis_avoided: bool
                child 3, replica_lag_s_before: int64
                child 4, replica_lag_s_after: double
                child 5, p99_s_before: double
                child 6, p99_s_after: double
                child 7, analytics_select_killed: bool
                child 8, cost_steps: int64
                child 9, wrong_mitigation_rollback: int64
                child 10, handoff_dba: bool
                child 11, writer_dns_updated: bool
                child 12, five_xx_rps_end: double
              meta: struct<factory: string, round: int64, generator: string, opensre_seed: string>
                child 0, factory: string
                child 1, round: int64
                child 2, generator: string
                child 3, opensre_seed: string
              false_lead: struct<claim: string, survived_steps: list<item: int64>, falsified_at: int64>
                child 0, claim: string
                child 1, survived_steps: list<item: int64>
                    child 0, item: int64
                child 2, falsified_at: int64
              kind: string
              remediate: string
              rca: string
              to
              {'id': Value('string'), 'goal': Value('string'), 'plan': Value('string'), 'kind': Value('string'), 'steps': List(Json(decode=True)), 'outcome': Value('string'), 'reward': {'success': Value('bool'), 'steps': Value('int64'), 'false_lead_steps': Value('int64'), 'http_retries': Value('int64')}, 'false_lead': {'claim': Value('string'), 'survived_steps': List(Value('int64')), 'falsified_at': Value('int64')}, 'rca': Value('string'), 'remediate': Value('string'), 'meta': {'factory': Value('string'), 'round': Value('int64'), 'generator': Value('string'), 'plant': Value('string'), 'alert_source': Value('string'), 'ticket': Value('string')}}
              because column names don't match

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Incident Response Oncall Trajectories

Rights & intended use: legacy public research corpus / portfolio artifact. Hosted frontier-model outputs are research-only inputs under project policy (synthetic-factory#161): intended_use: research_only, project_training_policy: blocked. Not training data for any model-weight update. Machine-readable record: rights.json.

Release status: The raw, uncurated payload is now published under data/raw/. It is available for inspection and reproducibility, but it is not training-ready.

Visibility: public raw-data repository.

Rights and intended use

This is a legacy public research corpus and portfolio artifact, not training data.

Under the Synthetic Factory project-policy decision recorded in rmems/synthetic-factory#161, outputs of hosted frontier models (here: Grok 4.6 (xAI)) are research-only inputs:

intended_use: research_only
project_training_policy: blocked

These records must not enter SFT, DPO, RL, distillation, continued pretraining, or any other model-weight update in this project. training_ready: false is a data-quality statement; even a future curated release would not make this corpus eligible for weight updates under project policy. That policy value does not change merely because a provider document later permits training — changing it would require a separate recorded project decision.

Research retention, evaluation, and redistribution rights are tracked separately per provider/channel/date and fail closed while unresolved (research_retention_status, research_evaluation_status, redistribution_status: unresolved). The machine-readable record is rights.json. Full contributor/role breakdown is preserved in ATTRIBUTION.md.

Historical release provenance

This dataset was originally published under Apache-2.0 during the 2026-08 Synthetic Data Factory runs. That historical grant is documented as released — this update reframes intended use going forward; it does not rewrite, backdate, or delete the release history or the payload.

On-call leftover-signal RCA trajectories.

Intended model target

This is a general agentic research dataset in the Grok 4.6 agentic factory. It is independent of the Fable 5 collection and is not labeled as Spikenaut training data.

Generation attribution

The underlying synthetic data is generated by Grok 4.6 through the Synthetic Data Factory agentic lane. Factory slug: incident-response-oncall-factory. Source tree: outputs/raw/2026-08-19-agentic/incident-response-oncall-factory/.

Published raw payload

The release contains 10052 raw records across data/raw/batch-r01.jsonl through data/raw/batch-r5026.jsonl (~83590 KB), snapshotted from outputs/raw/2026-08-19-agentic/incident-response-oncall-factory/. Supporting notes are under data/metadata/NOTES-*.md. The factory source remains the write destination; this Hub copy is a public evidence snapshot, not the curated training export. Public visibility is not a training-readiness claim.

Planned curated release

Curated training publication remains blocked until a later audit and export pass. Do not treat this repository as a training corpus.

Execution and validation environment

All local curation, validation, dataset processing, packaging, and release engineering for this dataset were performed exclusively on the Ship of Theseus AI/HPC research workstation.

Ship of Theseus was the sole local execution environment for this project. It was not the hosted model-generation channel: the synthetic records were generated through the provider surface identified in rights.json and the dataset provenance metadata.

License

This public raw release is licensed under the Apache License 2.0. That license grants reuse permissions; it does not make the records training-ready or factual real-world measurements.