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This is PR for following task.

2. Same violations reported multiple times
4. Error handling states (pass / fail / error)
5. Abstract classes using `pass` instead of explicit errors
7. AND / OR rule composition
9. Caching strategy (clarity + configurability)

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Summary of Changes

Hello @MT-superdev, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the Watchflow system's robustness, configurability, and expressiveness. It addresses critical feedback points by preventing redundant violation notifications, clarifying processing outcomes with explicit states, enforcing stricter implementation of abstract methods, and enabling the creation of highly complex and flexible rules. Additionally, it introduces a configurable caching mechanism to optimize performance and resource management.

Highlights

  • Violation Deduplication: Implemented a ViolationTracker to prevent the same violations from being reported multiple times, reducing notification spam for developers. This tracker fingerprints violations based on their content and context, and filters out duplicates before they are posted to GitHub.
  • Enhanced Error Handling States: Introduced a new ProcessingState enum (PASS, FAIL, ERROR) to clearly distinguish between successful processing, detected violations, and system errors. The ProcessingResult model and all event processors have been updated to use this more granular state, improving clarity and debugging capabilities.
  • Strict Abstract Method Enforcement: Modified abstract methods across various base classes (e.g., BaseAgent, BaseEventProcessor, BaseProvider) to raise NotImplementedError instead of using pass. This ensures that subclasses explicitly implement required methods, preventing silent failures and improving code quality.
  • Advanced Rule Logic (AND/OR/NOT): Added support for complex boolean logic (AND, OR, NOT) in rule conditions through ConditionExpression and ConditionEvaluator. This allows for the creation of more sophisticated and flexible rules, enabling precise control over when rules are triggered.
  • Configurable Caching Strategy: Introduced a dedicated CacheConfig and updated the caching utility to be fully configurable via environment variables. This includes settings for global and default cache sizes, TTLs, and a master switch to enable/disable caching, providing better control over performance and resource usage.

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Code Review

This is an excellent pull request that addresses several important architectural and functional improvements as outlined in the task description. The introduction of violation deduplication, configurable caching, explicit pass/fail/error processing states, and complex condition logic (AND/OR/NOT) are all well-implemented and significantly enhance the system's robustness and maintainability. The replacement of pass with NotImplementedError in abstract classes is a great improvement for developer experience. The addition of comprehensive test suites for each of these new features is particularly commendable and ensures the changes are reliable. I have two minor suggestions for improving code clarity and semantic correctness in the event processors, but overall, this is a very strong contribution.

Comment on lines 71 to 77
class FeasibilityResult:
def __init__(self, is_feasible: bool, yaml_content: str, feedback: str):
self.is_feasible = is_feasible
self.yaml_content = yaml_content
self.feedback = feedback

feasibility_result = FeasibilityResult(is_feasible, yaml_content, feedback)

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medium

Defining a class inside a method is generally discouraged as it can lead to code that is harder to understand, test, and maintain. For a simple data structure like this, consider using collections.namedtuple or a Pydantic BaseModel defined at the module level. This would make the code more idiomatic and improve readability.

Comment on lines 296 to 302
# Determine state: PASS if valid acknowledgment, FAIL if violations require fixes, ERROR if invalid
if evaluation_result["valid"]:
state = ProcessingState.PASS
elif evaluation_result.get("require_fixes"):
state = ProcessingState.FAIL
else:
state = ProcessingState.ERROR

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medium

The use of ProcessingState.ERROR here for an invalid user acknowledgment might be misleading. The ERROR state is typically reserved for unexpected system failures (e.g., exceptions, timeouts), whereas an invalid acknowledgment is a form of user input error. Consider using ProcessingState.FAIL instead to indicate that the process completed but the user's action was not successful. This would provide a clearer semantic distinction between system errors and validation failures.

            # If the acknowledgment is valid, the process passes. Otherwise, it fails.
            if evaluation_result["valid"]:
                state = ProcessingState.PASS
            else:
                state = ProcessingState.FAIL

@dkargatzis
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dkargatzis commented Jan 5, 2026

@MT-superdev could you please address the issues shown in PR checks?

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codecov-commenter commented Jan 6, 2026

⚠️ Please install the 'codecov app svg image' to ensure uploads and comments are reliably processed by Codecov.

Codecov Report

❌ Patch coverage is 21.46341% with 322 lines in your changes missing coverage. Please review.

❌ Your patch status has failed because the patch coverage (21.4%) is below the target coverage (80.0%). You can increase the patch coverage or adjust the target coverage.
❌ Your project status has failed because the head coverage (31.9%) is below the target coverage (80.0%). You can increase the head coverage or adjust the target coverage.
❗ Your organization needs to install the Codecov GitHub app to enable full functionality.

@@           Coverage Diff           @@
##            main     #37     +/-   ##
=======================================
- Coverage   32.8%   31.9%   -1.0%     
=======================================
  Files         85      89      +4     
  Lines       5162    5530    +368     
=======================================
+ Hits        1698    1766     +68     
- Misses      3464    3764    +300     

Continue to review full report in Codecov by Sentry.

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/gemini review

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Code Review

This pull request is a substantial and high-quality contribution that addresses several key architectural and functional issues outlined in Improvement.md. The introduction of a ProcessingState enum for clearer error handling, replacing pass with NotImplementedError in abstract classes, implementing a violation deduplication mechanism, adding a configurable caching strategy, and supporting complex AND/OR logic for rules are all excellent improvements. The code is well-structured, and the inclusion of comprehensive test suites for each new feature is commendable. My review includes a few suggestions for improving maintainability and consistency.

@@ -0,0 +1,405 @@
# Watchflow Improvements

## I THINK these are ISSUES (Must Fix Soon)

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medium

For consistency and professionalism, consider standardizing the capitalization of headings. Using Title Case for all main headings would improve readability.

For example:

  • ## I THINK these are ISSUES (Must Fix Soon) could become ## High-Priority Issues
  • ## TECHNICAL DEBT (Code Quality Issues) could become ## Technical Debt
  • ## PRIORITY SUMMARY could become ## Priority Summary

Comment on lines +149 to +159
pr_data = task.payload.get("pull_request", {})
pr_number = pr_data.get("number")
context = (
{
"pr_number": pr_number,
"commit_sha": pr_data.get("head", {}).get("sha"),
"branch": pr_data.get("head", {}).get("ref"),
}
if pr_number
else {}
)

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medium

This logic for building the context dictionary for violation tracking is duplicated in PushProcessor and ViolationAcknowledgmentProcessor. To improve maintainability and reduce code duplication, consider extracting this into a shared helper function. For example, you could create a function build_context_from_task(task: Task) -> dict that inspects the task's event type and payload to construct the appropriate context.

try:
condition_expr = ConditionExpression.from_dict(rule_data["condition"])
logger.debug(f"Parsed condition expression for rule: {rule_data['description']}")
except Exception as e:

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medium

Catching a broad Exception can mask unexpected bugs. It's better to catch more specific exceptions that you anticipate from parsing the condition dictionary, such as ValueError, KeyError, or Pydantic's ValidationError. This makes the error handling more robust.

Suggested change
except Exception as e:
except (ValueError, KeyError, TypeError) as e:

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3 participants