Datasets:

Jules-OC
/

flowzap-sequence-workflows

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type string to null
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2315, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2121, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature.feature)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2155, in cast_array_to_feature
                  return array_cast(
                      array,
                  ...<2 lines>...
                      allow_decimal_to_str=allow_decimal_to_str,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
                  raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
              TypeError: Couldn't cast array of type string to null
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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template_id string	workflow_id string	title string	kind string	source_domain string	primary_use_case string	secondary_use_cases list	use_cases list	complexity int64	num_nodes int64	num_lanes int64	tags list	description string	source_path string	repo_path string
training-enrollment-workflow	training-enrollment	Training Enrollment Workflow	workflow	hr	compliance	[]	[ "compliance" ]	2	7	2	[ "training-enrollment", "learning-management", "lms", "professional-development", "compliance-training", "certification" ]	Training enrollment workflow with course catalog browsing, manager approval, LMS registration, completion tracking, and certification management.	hr/training-enrollment	compliance/hr/training-enrollment-workflow

sequence-workflows

A synchronized FlowZap template corpus with 356 canonical templates, mirrored from the FlowZap template library and organized by primary Use Case.

Organization Model

Top-level folders are primary Use Cases from the FlowZap Templates dropdown.
Second-level folders preserve the original source domain from the FlowZap app index.
Each template keeps all matched Use Cases in metadata.json and the generated JSON/CSV indexes.
Templates that do not match any current dropdown Use Case are placed under uncategorized/.

Top-Level Folders

handoff/ — Handoff (22)
approval/ — Approval (21)
incident/ — Incident (12)
onboarding/ — Onboarding (8)
offboarding/ — Offboarding (1)
billing/ — Billing & Payments (11)
security/ — Security & Access (17)
compliance/ — Compliance (13)
delivery/ — Delivery & DevOps (23)
support/ — Support (1)
agentic/ — Agentic Teams (19)
ai-orchestration/ — AI Orchestration (7)
mcp/ — MCP Architecture (11)
context-management/ — Context Management (7)
commerce-automation/ — Commerce Automation (4)
process-mapping/ — Process Mapping (2)
agentic-architecture/ — Agentic Architecture (10)
agentic-security/ — Agentic Security (9)
agent-simulation/ — Agent Simulation (2)
llm-operations/ — LLM Operations (1)
diagram-authoring/ — Diagram Authoring (4)
platform-reliability/ — Platform Reliability (1)
mindmap/ — Mind Map (2)
syntax-primitives/ — Syntax Primitives (18)
edge-cases/ — Edge Cases (23)
architecture/ — Reference Architecture (22)
patterns/ — Patterns (40)
uncategorized/ — Unmatched canonical templates (45)

Generated Indexes

templates.index.json — Full machine-readable template index
use-cases.index.json — Use Case summary with counts and template paths
metadata.csv — Flat CSV export of core template metadata

Sync Source

Template library (source of truth): src/templateLibrary/templatesIndex.json in the FlowZap app
Template file source: public/template-files/*.fz in the FlowZap app
Public listing check: https://flowzap.xyz/sitemap-templates.xml — a template missing from the sitemap is still mirrored, this list only reports the gap

What this is

356 hand-authored FlowZap Code (.fz) diagrams: a plain-text DSL that renders as four views of the same source - workflow, sequence, architecture and mind map. Each file is valid against https://flowzap.xyz/api/validate and ships its own metadata (source domain, complexity, node and lane counts, tags).

Use it as reference examples before generating new diagrams, as a fine-tuning corpus, or as a regression set for a flowzap-code generator.