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feat(arch): document canonical execution shapes and validation models [OMN-933] - #220
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… [OMN-933] Define allowed execution shapes (Event→Orchestrator, Event→Reducer, Intent→Effect, Command→Orchestrator, Command→Effect) and forbidden patterns with rationale. - Add CANONICAL_EXECUTION_SHAPES.md with diagrams and decision matrix - Add MESSAGE_TOPIC_MAPPING.md for message category to topic routing - Create EnumMessageCategory and EnumExecutionShape enums - Create ModelExecutionShape, ModelExecutionShapeValidation, ModelShapeValidationResult - Create ModelTopicNaming for canonical topic naming - Update node building guides with execution shape references - Cross-reference from architecture docs
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WalkthroughAdds canonical execution-shape documentation and topic-naming guidance, new enums (EnumMessageCategory, EnumExecutionShape), Pydantic models for topic naming and shape validation, runtime envelope category inference and topic-alignment helpers, and comprehensive unit tests and exports across docs and src modules. Changes
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes
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docs/INDEX.md (1)
338-355: Update architecture progress text to match new totalsThe summary table correctly shows Architecture as
16docs and TOTAL as56, and the overall progress line reflects56/56. The “Priority Items” bullet still says “Architecture documentation (13/13 complete)”, which is now stale; consider updating that to16/16(or similar wording) to keep the counts consistent.
🧹 Nitpick comments (7)
docs/guides/node-building/06_ORCHESTRATOR_NODE_TUTORIAL.md (1)
18-29: Execution shape section is clear; consider aligning “Action → Effect” wording with canonical shapesThe new execution shape block correctly emphasizes:
- Event→Orchestrator and Command→Orchestrator as primary entry patterns.
- Orchestrators coordinating and emitting Actions rather than doing I/O directly.
- A pointer to
CANONICAL_EXECUTION_SHAPES.md, which is great.If the canonical shapes doc only names
Intent→Effect/Command→Effect, you might optionally clarify that “Action→Effect” here is the concrete ModelAction-based realization of those shapes (or rephrase to match those exact names) to avoid readers thinking it’s a separate canonical shape.src/omnibase_core/models/validation/model_shape_validation_result.py (1)
39-39: Consider addingfrom_attributes=Trueto ConfigDict.As per coding guidelines for models in
src/**/models/**/*.py, addfrom_attributes=Trueto ConfigDict for immutable value objects that may be used with pytest-xdist parallel execution.🔎 Suggested change
- model_config = ConfigDict(frozen=True, extra="forbid") + model_config = ConfigDict(frozen=True, extra="forbid", from_attributes=True)src/omnibase_core/models/validation/model_execution_shape_validation.py (2)
7-7: Reconsider use offrom __future__ import annotationsfor Pydantic models.Same concern as in
model_shape_validation_result.py- this may interfere with Pydantic's runtime type introspection. As per coding guidelines, avoid this import for Pydantic models.
42-42: Consider addingfrom_attributes=Trueto ConfigDict.Per coding guidelines for immutable value objects in
src/**/models/**/*.py.🔎 Suggested change
- model_config = ConfigDict(frozen=True, extra="forbid") + model_config = ConfigDict(frozen=True, extra="forbid", from_attributes=True)src/omnibase_core/models/validation/model_execution_shape.py (2)
7-7: Reconsider use offrom __future__ import annotationsfor Pydantic models.Same concern as in other validation models - this may interfere with Pydantic's runtime type introspection. As per coding guidelines, avoid this import for Pydantic models.
40-40: Consider addingfrom_attributes=Trueto ConfigDict.Per coding guidelines for immutable value objects in
src/**/models/**/*.py.🔎 Suggested change
- model_config = ConfigDict(frozen=True, extra="forbid") + model_config = ConfigDict(frozen=True, extra="forbid", from_attributes=True)src/omnibase_core/models/events/model_topic_naming.py (1)
81-86:VERSION_PATTERNis defined but never used.The
VERSION_PATTERNconstant is not referenced anywhere in the module. Theversionfield at line 111 uses the pattern string directly via Pydantic'spattern=argument. Consider removing this unused constant or using it in a custom validator for consistency with the other patterns.🔎 Proposed fix: Remove unused constant
# Validation patterns DOMAIN_PATTERN: ClassVar[re.Pattern[str]] = re.compile(r"^[a-z][a-z0-9\-]*$") - VERSION_PATTERN: ClassVar[re.Pattern[str]] = re.compile(r"^v\d+$") ENVIRONMENT_VALUES: ClassVar[frozenset[str]] = frozenset( {"dev", "staging", "prod", "test", "local"} )
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📒 Files selected for processing (18)
CLAUDE.md(1 hunks)docs/INDEX.md(2 hunks)docs/architecture/CANONICAL_EXECUTION_SHAPES.md(1 hunks)docs/architecture/MESSAGE_TOPIC_MAPPING.md(1 hunks)docs/architecture/NODE_PURITY_GUARANTEES.md(1 hunks)docs/architecture/ONEX_FOUR_NODE_ARCHITECTURE.md(1 hunks)docs/guides/node-building/03_COMPUTE_NODE_TUTORIAL.md(1 hunks)docs/guides/node-building/04_EFFECT_NODE_TUTORIAL.md(1 hunks)docs/guides/node-building/05_REDUCER_NODE_TUTORIAL.md(1 hunks)docs/guides/node-building/06_ORCHESTRATOR_NODE_TUTORIAL.md(1 hunks)src/omnibase_core/enums/__init__.py(2 hunks)src/omnibase_core/enums/enum_execution_shape.py(1 hunks)src/omnibase_core/models/events/__init__.py(1 hunks)src/omnibase_core/models/events/model_topic_naming.py(1 hunks)src/omnibase_core/models/validation/__init__.py(2 hunks)src/omnibase_core/models/validation/model_execution_shape.py(1 hunks)src/omnibase_core/models/validation/model_execution_shape_validation.py(1 hunks)src/omnibase_core/models/validation/model_shape_validation_result.py(1 hunks)
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📓 Path-based instructions (5)
**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
**/*.py: Always use Poetry for Python package management and task execution - usepoetry runfor all Python commands
Never use pip or direct python execution - use Poetry exclusively for dependency management and script execution
Files:
src/omnibase_core/models/validation/model_shape_validation_result.pysrc/omnibase_core/enums/__init__.pysrc/omnibase_core/enums/enum_execution_shape.pysrc/omnibase_core/models/validation/model_execution_shape_validation.pysrc/omnibase_core/models/validation/__init__.pysrc/omnibase_core/models/events/__init__.pysrc/omnibase_core/models/events/model_topic_naming.pysrc/omnibase_core/models/validation/model_execution_shape.py
src/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
src/**/*.py: Always use ModelOnexError with appropriate error codes instead of generic Exception for error handling
Use PEP 604 union syntax (str | None) instead of Optional and Union types
Use duck typing with protocols for service resolution - never use isinstance checks
Maintain minimum 60% code coverage and ensure 100% mypy strict mode compliance with 0 errors
Format code with Black and isort, then run pre-commit hooks before committing
Files:
src/omnibase_core/models/validation/model_shape_validation_result.pysrc/omnibase_core/enums/__init__.pysrc/omnibase_core/enums/enum_execution_shape.pysrc/omnibase_core/models/validation/model_execution_shape_validation.pysrc/omnibase_core/models/validation/__init__.pysrc/omnibase_core/models/events/__init__.pysrc/omnibase_core/models/events/model_topic_naming.pysrc/omnibase_core/models/validation/model_execution_shape.py
src/**/models/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
src/**/models/**/*.py: Add from_attributes=True to ConfigDict for immutable value objects nested in Pydantic models used with pytest-xdist parallel execution
Use thread-local instances or locks for components like ModelComputeCache and ModelCircuitBreaker that are not thread-safe
Do not use from future import annotations for Pydantic models and FastAPI endpoints that require runtime type introspection
Files:
src/omnibase_core/models/validation/model_shape_validation_result.pysrc/omnibase_core/models/validation/model_execution_shape_validation.pysrc/omnibase_core/models/validation/__init__.pysrc/omnibase_core/models/events/__init__.pysrc/omnibase_core/models/events/model_topic_naming.pysrc/omnibase_core/models/validation/model_execution_shape.py
src/**/enums/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
Distinguish between EnumNodeKind (architectural role: EFFECT, COMPUTE, REDUCER, ORCHESTRATOR) and EnumNodeType (implementation type: TRANSFORMER, AGGREGATOR, etc.)
Files:
src/omnibase_core/enums/__init__.pysrc/omnibase_core/enums/enum_execution_shape.py
src/**/events/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
Use structured ModelEventEnvelope for inter-service event communication
Files:
src/omnibase_core/models/events/__init__.pysrc/omnibase_core/models/events/model_topic_naming.py
🧠 Learnings (4)
📚 Learning: 2025-12-19T15:50:54.084Z
Learnt from: CR
Repo: OmniNode-ai/omnibase_core PR: 0
File: CLAUDE.md:0-0
Timestamp: 2025-12-19T15:50:54.084Z
Learning: Maintain strict data flow in ONEX four-node architecture: EFFECT → COMPUTE → REDUCER → ORCHESTRATOR with no backwards dependencies
Applied to files:
docs/architecture/NODE_PURITY_GUARANTEES.mddocs/guides/node-building/06_ORCHESTRATOR_NODE_TUTORIAL.mddocs/guides/node-building/03_COMPUTE_NODE_TUTORIAL.mdCLAUDE.mddocs/INDEX.mddocs/architecture/CANONICAL_EXECUTION_SHAPES.mddocs/architecture/ONEX_FOUR_NODE_ARCHITECTURE.mddocs/guides/node-building/05_REDUCER_NODE_TUTORIAL.mddocs/guides/node-building/04_EFFECT_NODE_TUTORIAL.md
📚 Learning: 2025-12-19T15:50:54.083Z
Learnt from: CR
Repo: OmniNode-ai/omnibase_core PR: 0
File: CLAUDE.md:0-0
Timestamp: 2025-12-19T15:50:54.083Z
Learning: Applies to **/node*.py : Inherit from appropriate base classes (NodeEffect, NodeCompute, NodeReducer, NodeOrchestrator) and implement required methods
Applied to files:
docs/guides/node-building/03_COMPUTE_NODE_TUTORIAL.md
📚 Learning: 2025-12-19T15:50:54.083Z
Learnt from: CR
Repo: OmniNode-ai/omnibase_core PR: 0
File: CLAUDE.md:0-0
Timestamp: 2025-12-19T15:50:54.083Z
Learning: Applies to src/**/enums/**/*.py : Distinguish between EnumNodeKind (architectural role: EFFECT, COMPUTE, REDUCER, ORCHESTRATOR) and EnumNodeType (implementation type: TRANSFORMER, AGGREGATOR, etc.)
Applied to files:
src/omnibase_core/enums/enum_execution_shape.py
📚 Learning: 2025-12-19T15:50:54.083Z
Learnt from: CR
Repo: OmniNode-ai/omnibase_core PR: 0
File: CLAUDE.md:0-0
Timestamp: 2025-12-19T15:50:54.083Z
Learning: Applies to src/**/events/**/*.py : Use structured ModelEventEnvelope for inter-service event communication
Applied to files:
src/omnibase_core/models/events/model_topic_naming.py
🧬 Code graph analysis (8)
src/omnibase_core/models/validation/model_shape_validation_result.py (1)
src/omnibase_core/models/validation/model_execution_shape_validation.py (1)
ModelExecutionShapeValidation(22-105)
src/omnibase_core/enums/__init__.py (1)
src/omnibase_core/enums/enum_execution_shape.py (2)
EnumExecutionShape(180-362)EnumMessageCategory(14-176)
src/omnibase_core/enums/enum_execution_shape.py (2)
src/omnibase_core/protocols/event_bus/protocol_event_message.py (2)
value(33-35)topic(23-25)src/omnibase_core/models/events/model_topic_naming.py (1)
topic_suffix(160-167)
src/omnibase_core/models/validation/model_execution_shape_validation.py (2)
src/omnibase_core/enums/enum_execution_shape.py (6)
EnumExecutionShape(180-362)EnumMessageCategory(14-176)get_source_category(233-250)get_target_node_kind(253-272)get_description(161-176)get_description(345-362)src/omnibase_core/enums/enum_node_kind.py (1)
EnumNodeKind(13-118)
src/omnibase_core/models/validation/__init__.py (3)
src/omnibase_core/models/validation/model_execution_shape.py (1)
ModelExecutionShape(22-87)src/omnibase_core/models/validation/model_execution_shape_validation.py (1)
ModelExecutionShapeValidation(22-105)src/omnibase_core/models/validation/model_shape_validation_result.py (1)
ModelShapeValidationResult(20-95)
src/omnibase_core/models/events/__init__.py (1)
src/omnibase_core/models/events/model_topic_naming.py (3)
ModelTopicNaming(34-296)get_topic_category(329-345)validate_topic_matches_category(299-326)
src/omnibase_core/models/events/model_topic_naming.py (1)
src/omnibase_core/enums/enum_execution_shape.py (3)
EnumMessageCategory(14-176)topic_suffix(70-83)from_topic(86-119)
src/omnibase_core/models/validation/model_execution_shape.py (2)
src/omnibase_core/enums/enum_execution_shape.py (6)
EnumExecutionShape(180-362)EnumMessageCategory(14-176)get_target_node_kind(253-272)get_source_category(233-250)get_description(161-176)get_description(345-362)src/omnibase_core/enums/enum_node_kind.py (1)
EnumNodeKind(13-118)
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🔇 Additional comments (24)
docs/architecture/NODE_PURITY_GUARANTEES.md (1)
316-318: Cross-link to canonical execution shapes looks goodThe added link to
CANONICAL_EXECUTION_SHAPES.mdnicely ties purity guarantees to the allowed dataflow patterns and reinforces the EFFECT→COMPUTE→REDUCER→ORCHESTRATOR discipline. Based on learnings, this is consistent with the enforced unidirectional flow.CLAUDE.md (1)
148-153: Good top-level pointer to execution shapesThe new “Execution Shapes” line, with examples like
Event→ReducerandIntent→Effect, is consistent with the four-node architecture and gives contributors a clear entry point into the new canonical shapes doc.docs/architecture/ONEX_FOUR_NODE_ARCHITECTURE.md (1)
1840-1842: Canonical execution shapes link fits well in related docsAdding
CANONICAL_EXECUTION_SHAPES.mdto the related documentation list is appropriate and keeps architecture readers one click away from the allowed/forbidden shape constraints.docs/INDEX.md (1)
95-109: New architecture entries are well placed and clearly labeledThe three new rows—Canonical Execution Shapes, Message Topic Mapping, and Node Purity Guarantees—are correctly grouped under Architecture, marked as NEW/Complete where appropriate, and align with the rest of the index structure.
docs/guides/node-building/03_COMPUTE_NODE_TUTORIAL.md (1)
25-34: Execution shape section for COMPUTE nodes matches purity guaranteesThe new section correctly states that COMPUTE nodes are pure transformations with no I/O, sit after EFFECT nodes, and feed REDUCER/ORCHESTRATOR, and it links to both canonical execution shapes and node purity guarantees. This is aligned with the EFFECT→COMPUTE→REDUCER→ORCHESTRATOR flow and the purity rules.
docs/guides/node-building/05_REDUCER_NODE_TUTORIAL.md (1)
178-188: Reducer execution shape section is aligned with pure FSM + intent patternThe new execution shape block accurately captures the canonical “Event→Reducer” flow, explicitly calls out that REDUCER nodes must not perform I/O, and points to both canonical shapes and node purity guarantees. That matches the pure FSM + Intent emission model described later in the tutorial.
src/omnibase_core/enums/__init__.py (1)
72-76: Import path for EnumMessageCategory is correctThe import is properly structured.
EnumMessageCategoryis defined inenum_execution_shape.py(line 14) and explicitly exported in its__all__list, making the combined import statement valid:from .enum_execution_shape import EnumExecutionShape, EnumMessageCategoryThis will work without issues. The
__all__additions for both names in the public API are correct.docs/guides/node-building/04_EFFECT_NODE_TUTORIAL.md (1)
35-41: LGTM!The new "Execution Shape" section provides clear, concise documentation of EFFECT node behavior with a proper cross-reference to the canonical shapes document. The description accurately reflects the ONEX architectural pattern where EFFECT nodes receive Intents from REDUCERs or Actions from ORCHESTRATORs. Based on learnings, this aligns with the strict data flow in the ONEX four-node architecture.
src/omnibase_core/models/validation/model_shape_validation_result.py (1)
70-95: LGTM!The
from_validationsfactory method correctly partitions validations, computes counts, and derivesis_fully_compliant. The logic is clear and well-documented.docs/architecture/MESSAGE_TOPIC_MAPPING.md (1)
1-426: LGTM!Comprehensive documentation covering message categories, topic naming conventions, enforcement rules, and integration patterns. The examples align with the implementation and provide clear guidance for developers.
src/omnibase_core/models/events/__init__.py (1)
35-49: LGTM!The new topic naming utilities are properly imported and exported with clear section grouping in
__all__. The public API surface is appropriately expanded.src/omnibase_core/models/validation/__init__.py (2)
15-20: LGTM!New execution shape validation models are properly imported with alphabetical ordering maintained.
39-42: LGTM!The new exports are properly grouped under a clear section comment with ticket reference (OMN-933).
src/omnibase_core/models/validation/model_execution_shape_validation.py (1)
65-105: LGTM!The
validate_shapeclassmethod correctly implements the matching logic by iterating through all canonical shapes and comparing source category and target node kind. The string comparison withtarget_node_kind.valueis the correct approach given thatget_target_node_kindreturns a string to avoid circular imports.src/omnibase_core/enums/enum_execution_shape.py (2)
13-177: LGTM!Well-structured
EnumMessageCategoryenum with comprehensive utilities for topic suffix generation, category detection from topics, and semantic checks (fact-based, action-oriented, goal-oriented). Thefrom_topicimplementation correctly handles both simple and versioned topic patterns.
179-363: LGTM!The
EnumExecutionShapeenum correctly defines canonical execution shapes with comprehensive utilities. The design choice to return strings fromget_target_node_kindto avoid circular imports withEnumNodeKindis well-documented. Based on learnings, the distinction betweenEnumNodeKind(architectural role) andEnumNodeType(implementation type) is properly maintained.src/omnibase_core/models/validation/model_execution_shape.py (1)
70-71: No action required. The string values returned byget_target_node_kind("orchestrator", "reducer", "effect") match the correspondingEnumNodeKindenum values exactly, soEnumNodeKind(target_kind_str)construction is safe and will not raiseValueErrorfor these strings.docs/architecture/CANONICAL_EXECUTION_SHAPES.md (2)
1-77: Well-structured architecture documentation that correctly enforces unidirectional data flow.The document accurately captures the ONEX Four-Node Architecture principles. The left-to-right data flow (EFFECT → COMPUTE → REDUCER → ORCHESTRATOR) aligns with the established architectural constraints. The ASCII diagrams are clear and the distinction between direct data flow and event bus coordination is well illustrated.
Based on learnings, the "no backwards dependencies" rule is correctly enforced throughout.
464-661: Forbidden patterns are comprehensive and well-justified.The six forbidden patterns cover the key architectural violations:
- Bypassing orchestration (Command → Reducer)
- Purity violations (Reducer → I/O, Compute → I/O)
- Data flow direction violations (Effect → Reducer)
- Circular dependencies
- Orchestrator coupling to I/O
Each pattern includes clear rationale and the correct alternative, making this section valuable for developers onboarding to the architecture.
src/omnibase_core/models/events/model_topic_naming.py (5)
74-79: Model configuration follows best practices.The
ConfigDictcorrectly includesfrom_attributes=Trueas required for immutable value objects used with pytest-xdist parallel execution. Thefrozen=Truesetting ensures immutability, andextra="forbid"prevents typos in field names from silently passing.
190-221: Verify intended behavior for topics with multi-segment domains.The
parse_topicmethod can construct multi-segment domains via".".join()(lines 203, 206), butDOMAIN_PATTERNat line 82 only allows single-segment domains (^[a-z][a-z0-9\-]*$— no dots). For example:
- Input:
"dev.foo.bar.events.v1"- Parsed domain:
"foo.bar"- Result:
None(fails validation)If topics with dot-separated domains are valid (e.g.,
dev.foo.bar.events.v1), the pattern or parsing logic needs adjustment. If single-segment domains are enforced, the current behavior is correct but could benefit from a comment explaining this constraint.
114-136: Validators correctly use ValueError for Pydantic compatibility.Using
ValueErrorin Pydantic field validators is the standard approach — Pydantic catches these and converts them toValidationError. This is appropriate even though the coding guidelines preferModelOnexErrorfor general error handling, as this exception applies specifically to Pydantic's validation flow.
223-296: Factory methods provide good ergonomics.The
for_events,for_commands, andfor_intentsclass methods offer clean convenience constructors that reduce boilerplate for common use cases. Sensible defaults (environment="dev",version="v1") align with the documented topic naming convention.
299-345: Helper functions provide clean public API surface.The standalone
validate_topic_matches_categoryandget_topic_categoryfunctions offer a simple API for topic validation without requiring model instantiation. The docstrings correctly document that no exceptions are raised (returnsFalseorNonefor invalid topics).
PR Review: Canonical Execution Shapes Documentation & Validation ModelsSummaryThis PR adds comprehensive documentation and validation infrastructure for ONEX execution shapes. Overall, this is excellent architectural work that significantly strengthens the framework. ✅ Strengths1. Outstanding Documentation (906 lines!)
2. Clean Enum Design
3. Well-Designed Validation ModelsModelExecutionShapeValidation.validate_shape() provides clean API 4. Comprehensive Topic Naming Model
5. Proper Integration
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Addresses PR #220 review feedback: - Add 161 unit tests for execution shape models and enums - test_enum_execution_shape.py (39 tests): EnumMessageCategory, EnumExecutionShape - test_model_execution_shapes.py (56 tests): validation models - test_model_topic_naming.py (66 tests): topic naming model - Fix code quality issues per review: - Remove `from __future__ import annotations` from Pydantic models - Add `from_attributes=True` to ConfigDict for validation models - Remove unused VERSION_PATTERN from model_topic_naming.py - Use string annotations for forward references All tests pass, mypy strict and ruff checks pass.
PR Review: Canonical Execution Shapes Documentation [OMN-933]SummaryThis PR introduces comprehensive documentation and validation infrastructure for ONEX canonical execution shapes. The implementation is excellent with thorough documentation, well-designed models, and comprehensive test coverage (161 tests added). ✅ Strengths1. Exceptional Documentation Quality
2. Well-Designed Models
3. Comprehensive Enum Design
4. Excellent Test Coverage
5. Adherence to Project Standards✅ Proper use of Poetry (all project conventions followed) 🔍 Code Quality Issues1. Minor: Unused Import in enum_execution_shape.py (Line 8)from __future__ import annotationsIssue: This import is present but not strictly necessary since the file uses string annotations where needed. 2. Potential Enhancement: Type Safety in get_target_node_kind()Location: Current implementation returns @classmethod
def get_target_node_kind(cls, shape: EnumExecutionShape) -> str:
"""Returns string to avoid circular imports with EnumNodeKind."""
target_mapping = {
cls.EVENT_TO_ORCHESTRATOR: "orchestrator",
cls.EVENT_TO_REDUCER: "reducer",
cls.INTENT_TO_EFFECT: "effect",
cls.COMMAND_TO_ORCHESTRATOR: "orchestrator",
cls.COMMAND_TO_EFFECT: "effect",
}
return target_mapping[shape]Observation: The circular import is resolved in target_kind_str = EnumExecutionShape.get_target_node_kind(shape)
target_kind = EnumNodeKind(target_kind_str) # String -> Enum conversionRecommendation: This is a reasonable design decision to avoid circular imports. However, consider using from typing import TYPE_CHECKING
if TYPE_CHECKING:
from omnibase_core.enums.enum_node_kind import EnumNodeKindImpact: Low (current approach is acceptable) 🚀 Performance ConsiderationsNo Issues Found
🔒 Security ConcernsNo Issues Found
📊 Test Coverage AssessmentExcellent Coverage (161 tests)test_enum_execution_shape.py (39 tests):
test_model_execution_shapes.py (56 tests):
test_model_topic_naming.py (66 tests):
Potential Gap: Integration tests for end-to-end topic routing validation
🎯 Architectural AlignmentPerfectly Aligned with ONEX Principles✅ Four-Node Architecture: Execution shapes enforce EFFECT → COMPUTE → REDUCER → ORCHESTRATOR flow 🐛 Potential BugsNo Critical Bugs FoundMinor Edge Case: Location: # Handle format: <env>.<domain>.<suffix>.<version>
if parts[-1].startswith("v") and parts[-1][1:].isdigit():
version = parts[-1]
suffix = parts[-2]
domain = ".".join(parts[1:-2]) if len(parts) > 3 else parts[1]
else:
suffix = parts[-1]
domain = ".".join(parts[1:-1]) if len(parts) > 2 else parts[1]Test Coverage: This logic is well-tested ( Verdict: No bug, just flagging for awareness 📋 Recommendations1. CI Status Check (Priority: High)
Action: Wait for CI to pass before merging 2. Documentation Cross-References (Priority: Low)The PR adds cross-references to node building guides, but consider adding links from:
Verdict: Already complete! 3. Future Enhancement: Runtime ValidationConsider adding runtime enforcement in future PR: # In event bus publish logic
def publish(message: ModelEventEnvelope, topic: str):
category = EnumMessageCategory.from_topic(topic)
if category != infer_category_from_message(message):
raise ModelOnexError(
message="Message category mismatch with topic",
error_code=EnumCoreErrorCode.VALIDATION_ERROR,
context={"topic": topic, "message_type": type(message).__name__}
)Impact: Would catch routing misconfigurations at runtime ✅ Final VerdictAPPROVE with Minor Observations This is outstanding work that significantly improves the architectural clarity of the ONEX framework. The execution shapes documentation will be invaluable for developers building ONEX applications. Merge Criteria
Post-Merge Recommendations
Great work, @jonahgabriel! 🚀 This PR establishes a solid foundation for execution shape validation and will prevent architectural violations. The comprehensive documentation and test coverage demonstrate excellent engineering discipline. cc: @OmniNode-ai/omnibase_core |
…nment [OMN-933] Add runtime validation to ensure messages are published to topics with matching category (events to .events topics, commands to .commands, intents to .intents). Changes: - ModelEventEnvelope: Add infer_category() method and message_category property to determine category from metadata tags or payload type name - model_topic_naming: Add validate_message_topic_alignment() function that raises ModelOnexError when message category doesn't match topic - MixinEventBus: Add _validate_topic_alignment() helper method with TODO markers for integration when topic-based publishing is implemented Tests: - 18 tests for infer_category() covering metadata, payload type inference, defaults - 18 tests for validate_message_topic_alignment() covering all category combinations
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Actionable comments posted: 0
🧹 Nitpick comments (4)
src/omnibase_core/models/events/model_event_envelope.py (1)
324-371: Simplify redundant substring checks.The
endswithcheck is redundant since"Command" in payload_type_namealready covers suffix cases. The same applies to the Intent check.🔎 Suggested simplification
# Infer from payload type name payload_type_name = type(self.payload).__name__ - if payload_type_name.endswith("Command") or "Command" in payload_type_name: + if "Command" in payload_type_name: return EnumMessageCategory.COMMAND - if payload_type_name.endswith("Intent") or "Intent" in payload_type_name: + if "Intent" in payload_type_name: return EnumMessageCategory.INTENTsrc/omnibase_core/mixins/mixin_event_bus.py (1)
227-228: Remove unused inline import.The inline import of
ModelEventEnvelopeis not used in this method. The validation uses duck typing withhasattr(envelope, "message_category")instead of type checking.🔎 Suggested fix
- # Import here to avoid circular imports at module level - from omnibase_core.models.events.model_event_envelope import ModelEventEnvelope - # Only validate if envelope has message_category property if not hasattr(envelope, "message_category"):tests/unit/models/events/test_model_topic_naming.py (1)
717-722: Empty test method should be implemented or removed.
test_max_length_environmentcontains only a comment and no assertions. Since the comment explains that environment values are constrained to a specific set rather than a length limit, this test should either be removed or converted to a meaningful test.🔎 Suggested options
Option 1 - Remove the empty test:
- def test_max_length_environment(self): - """Test environment at maximum length boundary.""" - # Environment max_length is 20, but only specific values are allowed - # So this test is more about the validator rejecting invalid values - # even if they're within length limit - # Valid environments are all well under 20 charsOption 2 - Add a meaningful test that documents the constraint:
def test_max_length_environment(self): - """Test environment at maximum length boundary.""" - # Environment max_length is 20, but only specific values are allowed - # So this test is more about the validator rejecting invalid values - # even if they're within length limit - # Valid environments are all well under 20 chars + """Test that environment values are constrained to allowed set, not just length.""" + # Verify that all valid environments are well under max_length + valid_envs = ["dev", "staging", "prod", "test", "local"] + for env in valid_envs: + assert len(env) <= 20, f"Valid env {env} exceeds max_length"tests/unit/models/validation/test_model_execution_shapes.py (1)
26-742: Consider adding pytest markers for test organization.The test suite is comprehensive and well-structured with excellent coverage of all models, factory methods, validation logic, and integration scenarios.
However, per the coding guidelines, tests should include appropriate pytest markers:
@pytest.mark.unit class TestModelExecutionShapeCreation: ...This helps with selective test execution and CI/CD pipeline organization.
Suggested markers for test classes
Add
@pytest.mark.unitdecorator to all test classes since these are unit tests:+@pytest.mark.unit class TestModelExecutionShapeCreation: """Test ModelExecutionShape instantiation and fields."""Apply the same decorator to all other test classes in this file.
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📒 Files selected for processing (10)
src/omnibase_core/mixins/mixin_event_bus.py(5 hunks)src/omnibase_core/models/events/model_event_envelope.py(2 hunks)src/omnibase_core/models/events/model_topic_naming.py(1 hunks)src/omnibase_core/models/validation/model_execution_shape.py(1 hunks)src/omnibase_core/models/validation/model_execution_shape_validation.py(1 hunks)src/omnibase_core/models/validation/model_shape_validation_result.py(1 hunks)tests/unit/enums/test_enum_execution_shape.py(1 hunks)tests/unit/models/events/test_model_event_envelope.py(2 hunks)tests/unit/models/events/test_model_topic_naming.py(1 hunks)tests/unit/models/validation/test_model_execution_shapes.py(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- src/omnibase_core/models/validation/model_execution_shape_validation.py
- src/omnibase_core/models/validation/model_shape_validation_result.py
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📓 Path-based instructions (5)
**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
**/*.py: Always use Poetry for Python package management and task execution - usepoetry runfor all Python commands
Never use pip or direct python execution - use Poetry exclusively for dependency management and script execution
Files:
src/omnibase_core/mixins/mixin_event_bus.pytests/unit/models/events/test_model_event_envelope.pytests/unit/models/validation/test_model_execution_shapes.pysrc/omnibase_core/models/events/model_event_envelope.pytests/unit/models/events/test_model_topic_naming.pysrc/omnibase_core/models/events/model_topic_naming.pytests/unit/enums/test_enum_execution_shape.pysrc/omnibase_core/models/validation/model_execution_shape.py
src/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
src/**/*.py: Always use ModelOnexError with appropriate error codes instead of generic Exception for error handling
Use PEP 604 union syntax (str | None) instead of Optional and Union types
Use duck typing with protocols for service resolution - never use isinstance checks
Maintain minimum 60% code coverage and ensure 100% mypy strict mode compliance with 0 errors
Format code with Black and isort, then run pre-commit hooks before committing
Files:
src/omnibase_core/mixins/mixin_event_bus.pysrc/omnibase_core/models/events/model_event_envelope.pysrc/omnibase_core/models/events/model_topic_naming.pysrc/omnibase_core/models/validation/model_execution_shape.py
tests/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
tests/**/*.py: Run pytest with Poetry: poetry run pytest tests/ with appropriate markers (@pytest.mark.unit, @pytest.mark.integration, @pytest.mark.slow)
Set test timeout protection to 60 seconds per test to prevent hangs in CI/CD
Use loadscope distribution strategy with 4 worker processes for local parallel testing
Files:
tests/unit/models/events/test_model_event_envelope.pytests/unit/models/validation/test_model_execution_shapes.pytests/unit/models/events/test_model_topic_naming.pytests/unit/enums/test_enum_execution_shape.py
src/**/models/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
src/**/models/**/*.py: Add from_attributes=True to ConfigDict for immutable value objects nested in Pydantic models used with pytest-xdist parallel execution
Use thread-local instances or locks for components like ModelComputeCache and ModelCircuitBreaker that are not thread-safe
Do not use from future import annotations for Pydantic models and FastAPI endpoints that require runtime type introspection
Files:
src/omnibase_core/models/events/model_event_envelope.pysrc/omnibase_core/models/events/model_topic_naming.pysrc/omnibase_core/models/validation/model_execution_shape.py
src/**/events/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
Use structured ModelEventEnvelope for inter-service event communication
Files:
src/omnibase_core/models/events/model_event_envelope.pysrc/omnibase_core/models/events/model_topic_naming.py
🧠 Learnings (1)
📚 Learning: 2025-12-19T15:50:54.083Z
Learnt from: CR
Repo: OmniNode-ai/omnibase_core PR: 0
File: CLAUDE.md:0-0
Timestamp: 2025-12-19T15:50:54.083Z
Learning: Applies to src/**/events/**/*.py : Use structured ModelEventEnvelope for inter-service event communication
Applied to files:
src/omnibase_core/mixins/mixin_event_bus.pytests/unit/models/events/test_model_event_envelope.pysrc/omnibase_core/models/events/model_event_envelope.pysrc/omnibase_core/models/events/model_topic_naming.py
🧬 Code graph analysis (5)
src/omnibase_core/mixins/mixin_event_bus.py (3)
src/omnibase_core/enums/enum_execution_shape.py (1)
EnumMessageCategory(14-176)src/omnibase_core/models/events/model_topic_naming.py (1)
validate_message_topic_alignment(349-405)src/omnibase_core/models/events/model_event_envelope.py (2)
ModelEventEnvelope(33-517)message_category(374-390)
tests/unit/models/validation/test_model_execution_shapes.py (5)
src/omnibase_core/enums/enum_execution_shape.py (2)
EnumExecutionShape(180-362)EnumMessageCategory(14-176)src/omnibase_core/models/validation/model_execution_shape.py (3)
ModelExecutionShape(20-85)from_shape(58-75)get_all_shapes(78-85)src/omnibase_core/models/validation/model_execution_shape_validation.py (2)
ModelExecutionShapeValidation(20-103)validate_shape(64-103)src/omnibase_core/models/validation/model_shape_validation_result.py (2)
ModelShapeValidationResult(18-93)from_validations(69-93)src/omnibase_core/protocols/event_bus/protocol_event_message.py (1)
value(33-35)
src/omnibase_core/models/events/model_event_envelope.py (1)
src/omnibase_core/enums/enum_execution_shape.py (1)
EnumMessageCategory(14-176)
tests/unit/enums/test_enum_execution_shape.py (1)
src/omnibase_core/enums/enum_execution_shape.py (13)
EnumExecutionShape(180-362)EnumMessageCategory(14-176)from_topic(86-119)is_fact_based(122-132)is_action_oriented(135-145)is_goal_oriented(148-158)get_source_category(233-250)get_target_node_kind(253-272)targets_coordinator(275-285)targets_effect(288-298)targets_reducer(301-311)get_shapes_for_category(314-327)get_shapes_for_target(330-342)
src/omnibase_core/models/validation/model_execution_shape.py (2)
src/omnibase_core/enums/enum_execution_shape.py (6)
EnumExecutionShape(180-362)EnumMessageCategory(14-176)get_target_node_kind(253-272)get_source_category(233-250)get_description(161-176)get_description(345-362)src/omnibase_core/enums/enum_node_kind.py (1)
EnumNodeKind(13-118)
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🔇 Additional comments (17)
src/omnibase_core/models/events/model_event_envelope.py (2)
22-22: LGTM!Import of
EnumMessageCategoryis correct and necessary for the new category inference functionality.
373-390: LGTM!The
message_categoryproperty correctly delegates toinfer_category()providing a clean accessor pattern. Documentation is comprehensive with clear examples.src/omnibase_core/mixins/mixin_event_bus.py (3)
29-35: LGTM!Imports for
EnumMessageCategoryandvalidate_message_topic_alignmentare correctly added to support the new topic alignment validation.
203-244: Well-structured validation helper with good defensive coding.The method correctly:
- Uses duck typing to gracefully handle envelopes without
message_category- Logs a warning instead of failing when the property is missing
- Delegates to the centralized
validate_message_topic_alignmentfunction
290-293: TODO comments document future integration points.The commented-out validation calls and TODO markers clearly document where topic validation should be integrated once topic-based publishing is implemented. This is appropriate for staged rollout.
tests/unit/models/events/test_model_topic_naming.py (2)
1-29: LGTM!Comprehensive test module header with clear documentation of test coverage areas. Imports are correctly structured.
798-993: Thorough validation tests for message-topic alignment.The
TestValidateMessageTopicAlignmentclass provides excellent coverage:
- All valid category-topic combinations
- All mismatch scenarios
- Error context verification
- Case-insensitive handling
- Edge cases (empty topics, unknown suffixes)
tests/unit/models/events/test_model_event_envelope.py (2)
24-24: LGTM!Import of
EnumMessageCategoryis correctly added to support the new category inference tests.
909-1097: Comprehensive test coverage for category inference.The
TestModelEventEnvelopeInferCategoryclass provides excellent coverage of:
- Default category inference (EVENT for generic payloads)
- Payload type name-based inference (Command/Intent patterns)
- Metadata tag precedence over payload type
- Case-insensitive metadata handling
- Invalid metadata fallback to payload inference
- Consistency between
message_categoryproperty andinfer_category()methodThe use of inline class definitions (e.g.,
ProcessOrderCommand,CreateUserIntent) is a clean pattern for testing type name-based inference.tests/unit/enums/test_enum_execution_shape.py (4)
1-19: LGTM!Well-structured test module with clear documentation of test coverage. Import from the combined enum module is correct.
21-176: Thorough test coverage for EnumMessageCategory.The test class comprehensively covers:
- Enum value existence and string representation
topic_suffixpropertyfrom_topicparsing (simple, versioned, complex, case-insensitive, invalid)- Classification methods (
is_fact_based,is_action_oriented,is_goal_oriented)get_descriptionmethod- Enum equality, membership, and iteration
178-493: Comprehensive test coverage for EnumExecutionShape.The test class thoroughly validates:
- All five execution shape values
- Source category mappings (
get_source_category)- Target node kind mappings (
get_target_node_kind)- Target helper methods (
targets_coordinator,targets_effect,targets_reducer)- Category-to-shape and target-to-shape lookups
- Shape descriptions
- Consistency verification that all shapes have valid source/target mappings
495-542: Good integration tests ensuring consistency across enums.The integration test class verifies important cross-cutting concerns:
- Every message category has at least one execution shape
- Topic-to-shape workflow functions correctly
- All target node kinds have associated shapes
- Descriptions are non-empty for both categories and shapes
src/omnibase_core/models/validation/model_execution_shape.py (1)
20-85: LGTM! Clean factory pattern implementation.The ModelExecutionShape implementation is well-structured with:
- Proper immutability via
frozen=True- Factory methods that delegate to EnumExecutionShape helpers
- Clear field definitions with appropriate defaults
The
from_shapeclassmethod correctly converts the string target kind to EnumNodeKind enum, andget_all_shapesprovides convenient access to all canonical shapes.src/omnibase_core/models/events/model_topic_naming.py (3)
76-137: LGTM! Robust validation with proper patterns.The model configuration and field validators are well-implemented:
- Immutability enforced via
frozen=True- Environment validation against known values with case normalization
- Domain validation with regex pattern ensuring lowercase, hyphen-separated naming
- Proper use of
ClassVarfor shared constantsThe validators return lowercase normalized values, ensuring consistency across the system.
170-223: parse_topic logic correctly handles multi-part domains and versions.The parsing logic properly handles:
- Versioned topics:
dev.user.events.v1- Non-versioned topics:
dev.user.events- Multi-part domains:
dev.foo.bar.events.v1→ domain =foo.barLine 210 uses
f"x.{suffix}"as a workaround forEnumMessageCategory.from_topic(), which works correctly since that method checks for patterns like.eventsanywhere in the topic string.
349-405: Excellent runtime validation with informative error context.The
validate_message_topic_alignmentfunction provides critical runtime enforcement:
- Validates topic-category alignment before message publication
- Raises
ModelOnexErrorwith appropriate error code per coding guidelines- Includes rich context (topic, categories, message type) for debugging
This aligns with the coding guideline: "Always use ModelOnexError with appropriate error codes instead of generic Exception for error handling."
PR Review: Canonical Execution Shapes [OMN-933]OverviewThis PR introduces comprehensive architectural documentation and validation models for canonical execution shapes in the ONEX framework. Strengths1. Excellent Documentation Quality
2. Strong Type Safety
3. Comprehensive Test Coverage
4. Excellent Adherence to Project Standards
Areas for Improvement1. Topic Parsing Case SensitivityLocation: src/omnibase_core/enums/enum_execution_shape.py:111-119 Issue: from_topic() uses case-insensitive matching which could mask topic naming errors. Kafka topics are case-sensitive. Recommendation: Consider making topic parsing case-sensitive or emit warnings for case mismatches. 2. Circular Import PatternLocation: src/omnibase_core/mixins/mixin_event_bus.py:227 Issue: Imports ModelEventEnvelope inside function to avoid circular imports. Recommendation: Consider moving _validate_topic_alignment to utility module or use protocol/interface. 3. Validation Method CoverageLocation: src/omnibase_core/models/validation/model_execution_shape_validation.py:64-103 Issue: validate_shape() does not validate forbidden patterns explicitly. Recommendation: Add explicit validation against forbidden patterns with specific error messages aligned with documentation. 4. Domain Validation PatternLocation: src/omnibase_core/models/events/model_topic_naming.py:84 Issue: Domain pattern allows consecutive hyphens. Recommendation: Reject patterns like user--service, -user, user-. 5. Null Check HandlingLocation: src/omnibase_core/enums/enum_execution_shape.py:86-119 Issue: from_topic() returns None for invalid topics. Recommendation: Consider raising exception or provide try_from_topic() variant. Security ConsiderationsNo Critical Issues Found
Minor Enhancement: Add length limits to topic names to prevent DoS. Performance Considerations
TestingExcellent Coverage:
Suggested additions:
Code Quality Checklist
Recommendations SummaryMust Fix (Blocking): None Should Fix (High Priority):
Nice to Have (Low Priority):
Final AssessmentOverall Quality: 5/5 stars This is excellent work that significantly improves ONEX framework architectural clarity and runtime safety. Documentation is outstanding, code follows best practices, and test coverage is comprehensive. Recommendation: APPROVE with minor suggested improvements The suggestions are non-blocking enhancements. Core architecture is sound, implementation is clean, and documentation will be invaluable for developers. Great job! Related Future Work
Reviewed by: Claude Code |
…933] - Add try_from_topic() method for safe topic parsing with error messages - Add property-based tests using hypothesis for enum invariants - Enhance docstrings with examples, Args, Returns, and See Also sections - Add pytest.mark.unit markers to all test classes - Document case-insensitive topic parsing behavior - Clarify canonical vs runtime execution shapes in documentation - Verify all validation models have from_attributes=True in ConfigDict Adds 81 new tests (118 total), all passing with mypy strict compliance.
- docs/INDEX.md: keep 56 document count (includes new execution shapes docs) - models/events/__init__.py: include both topic_config/manifest and topic_naming exports
Pull Request Review: Canonical Execution Shapes Documentation [OMN-933]SummaryThis PR introduces comprehensive documentation and validation infrastructure for ONEX canonical execution shapes. The implementation is excellent with high-quality documentation, robust validation models, and comprehensive test coverage. Overall Assessment: ✅ APPROVED (with minor suggestions) 🎯 Strengths1. Exceptional Documentation Quality
2. Clean Enum Design# src/omnibase_core/enums/enum_execution_shape.py
3. Robust Validation Models# src/omnibase_core/models/validation/
4. Comprehensive Test Coverage
5. Thread Safety Consideration
🔍 Code Quality AssessmentType Safety ✅
Error Handling ✅
Performance ✅
Maintainability ✅
🐛 Potential Issues1. Minor: Topic Parsing Edge CaseFile: def parse_topic(cls, topic: str) -> "ModelTopicNaming | None":
parts = topic.lower().split(".")
if len(parts) < 3:
return None
# Handle format: <env>.<domain>.<suffix>.<version>
# or: <env>.<domain>.<suffix>
environment = parts[0]
version = "v1"Issue: If Suggestion: Consider using 2. Minor: Validation PerformanceFile: @classmethod
def validate_shape(cls, source_category, target_node_kind):
# Find matching canonical shape
for shape in EnumExecutionShape:
shape_source = EnumExecutionShape.get_source_category(shape)
shape_target_str = EnumExecutionShape.get_target_node_kind(shape)
if (shape_source == source_category and
shape_target_str == target_node_kind.value):
return cls(...)Observation: Linear search through 5 shapes is fine, but could use a cached mapping dict if this becomes a hot path. Not Critical: Current implementation is clear and only 5 iterations. 💡 Suggestions for Improvement1. Add Runtime Enforcement Hook (Future Enhancement)Consider adding a decorator or middleware to validate execution shapes at runtime: # Future enhancement idea (not blocking)
@enforce_execution_shape
class NodeMyReducer(NodeReducer):
async def process(self, input_data: ModelReducerInput):
# Runtime validates Event→Reducer is allowed
pass2. Documentation Cross-ReferenceAdd a section in
3. Validation ToolingConsider adding a CLI tool for developers: poetry run onex validate-shape EVENT ORCHESTRATOR # ✅ Allowed
poetry run onex validate-shape COMMAND REDUCER # ❌ Forbidden🔐 Security Considerations✅ No Security Issues Found
📊 Test Coverage AnalysisCoverage Summary
Property-Based Testing ⭐Excellent use of Hypothesis for invariant testing:
This catches edge cases that example-based tests might miss. ✅ Checklist ComplianceBased on CLAUDE.md requirements:
🎯 Architectural ComplianceONEX Four-Node Architecture ✅
Integration with Existing Codebase ✅
📝 Documentation Updates Needed✅ Already Done
💡 Future Enhancements (Not Blocking)
🚀 Performance Considerations✅ No Performance Concerns
🎓 Best Practices Demonstrated
Final Recommendation✅ APPROVE AND MERGE This PR demonstrates exceptional engineering quality:
Minor TODOs (Non-Blocking)
CI Status⏳ Awaiting CI test results. Based on code review:
Great work on this PR! This establishes a solid foundation for enforcing ONEX architectural patterns across the ecosystem. cc: @jonahgabriel |
…mples [OMN-933] - Add @enforce_execution_shape decorator for runtime validation of message handlers against canonical execution shapes - Create EXECUTION_SHAPE_EXAMPLES.md with practical code examples for all 5 canonical shapes (Event→Orchestrator, Event→Reducer, Intent→Effect, Command→Orchestrator, Command→Effect) - Add decision tree and quick reference for shape selection - Update INDEX.md and CANONICAL_EXECUTION_SHAPES.md with references New files: - src/omnibase_core/decorators/decorator_enforce_execution_shape.py - tests/unit/decorators/test_decorator_enforce_execution_shape.py (45 tests) - docs/architecture/EXECUTION_SHAPE_EXAMPLES.md Related: OMN-960 (CLI validation tool tracked separately)
Code Review: Canonical Execution Shapes ImplementationThis PR introduces a comprehensive architectural specification and validation framework for ONEX execution shapes. The implementation is excellent with well-thought-out design patterns, thorough documentation, and extensive test coverage. StrengthsOutstanding Documentation Quality
Clean Type-Safe Implementation
Comprehensive Test Coverage
Runtime Enforcement Decorator
Minor Observations
Architecture & Design: ExcellentThe allowed/forbidden patterns are architecturally sound and enforce:
Security & Performance
Final Verdict: APPROVEThis PR represents high-quality architectural work with:
Impact: This establishes a solid foundation for enforcing architectural constraints across the ONEX ecosystem. Confidence: Very High - The combination of extensive tests, clear documentation, and adherence to project conventions makes this a low-risk, high-value contribution. This is a model PR for architectural specifications. The execution shape constraints will prevent entire classes of architectural violations. Recommended next steps:
Review conducted using CLAUDE.md project conventions and ONEX architectural standards. |
Summary
Test plan
Files Added (7)
docs/architecture/CANONICAL_EXECUTION_SHAPES.mddocs/architecture/MESSAGE_TOPIC_MAPPING.mdsrc/omnibase_core/enums/enum_execution_shape.pysrc/omnibase_core/models/validation/model_execution_shape.pysrc/omnibase_core/models/validation/model_execution_shape_validation.pysrc/omnibase_core/models/validation/model_shape_validation_result.pysrc/omnibase_core/models/events/model_topic_naming.pyLinear
Closes OMN-933
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