refactor(github-review): replace confidence scoring with evidence-based validation #628
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…ed validation Removes confidence scores (0-100) from PR review system in favor of evidence-based validation. This addresses the root cause of false positives: reviews reporting issues they couldn't prove with actual code. Key changes: - Remove confidence field from PRReviewFinding and pydantic models - Add required 'evidence' field for code snippets proving issues exist - Deprecate confidence.py module with migration guidance - Update response_parsers.py to filter by evidence presence, not score - Add "NEVER ASSUME - ALWAYS VERIFY" sections to all reviewer prompts - Update finding-validator to use binary evidence verification - Update tests for new evidence-based validation model The new model is simple: either you can show the problematic code, or you don't report the finding. No more "80% confident" hedging. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <[email protected]>
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Note Other AI code review bot(s) detectedCodeRabbit has detected other AI code review bot(s) in this pull request and will avoid duplicating their findings in the review comments. This may lead to a less comprehensive review. 📝 WalkthroughWalkthroughMigration from confidence-score-based review to an evidence-first validation process: prompts, schemas, parsers, tests, and docs now require concrete code evidence and boolean verification flags; the confidence module is deprecated and some reviewer/orchestrator enums updated. Changes
Sequence Diagram(s)(No sequence diagrams generated — changes are primarily schema/prompts/tests and do not introduce a new multi-component control flow that requires visualization.) Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
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Summary of ChangesHello @AndyMik90, 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 fundamentally refactors the PR review process by moving from a subjective confidence scoring system to an objective, evidence-based validation approach. The core principle is that any reported issue must be verifiable with actual code evidence, eliminating speculative or hallucinated findings. This change aims to significantly reduce false positives and improve the reliability and trustworthiness of the review system by ensuring that all reported issues are grounded in concrete code examples. Highlights
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Customization To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a Limitations & Feedback Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here. You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension. Footnotes
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Updated the find_worktree function to first check the new worktree path at .auto-claude/worktrees/tasks/ for task directories. Added a fallback to check the legacy path at .worktrees/ for backwards compatibility, ensuring that existing workflows are not disrupted. This change improves the robustness of worktree handling in the project. 🤖 Generated with [Claude Code](https://claude.com/claude-code)
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Code Review
This pull request introduces a significant and well-executed refactoring, replacing the subjective confidence scoring system with a more robust, binary evidence-based validation approach. The changes are comprehensive, touching prompts, Pydantic models, data classes, response parsers, and tests to consistently enforce the new 'No evidence = No finding' principle. The deprecation of the old confidence module is handled excellently with clear warnings and documentation. The updated prompts provide strong, clear guidance to the AI agents. The tests have been thoroughly updated to reflect the new data models and logic. Overall, this is a high-quality change that will improve the reliability of review findings. I have one minor suggestion to improve the clarity of a field description in a Pydantic model, but otherwise, the implementation is outstanding.
| fixable: bool = Field(False, description="Whether this can be auto-fixed") | ||
| evidence: str | None = Field( | ||
| None, | ||
| description="Actual code snippet proving the issue exists. Required for validation.", |
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The description for the evidence field says "Required for validation", which could be slightly misleading since the field is optional in the model (str | None). To improve clarity and accurately reflect that the requirement is enforced by process rather than by this model's validation, consider rephrasing the description.
This change would apply to all models that inherit this field from BaseFinding, making the schema more self-documenting and clearer for developers.
| description="Actual code snippet proving the issue exists. Required for validation.", | |
| description="A code snippet that proves the issue exists. This is required by prompts and downstream validation; findings without it may be filtered.", |
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apps/backend/runners/github/confidence.py (1)
2-30: Excellent deprecation documentation.The updated docstring clearly communicates the deprecation, explains the new evidence-based approach, and provides concrete migration guidance. The structure is well-organized and actionable.
Consider adding a removal timeline.
While the deprecation is well-documented, consider adding a target version or timeline for removal (e.g., "will be removed in version 2.0" or "will be removed after Q2 2026") to help downstream code plan their migration.
Optional: Add removal timeline
""" -DEPRECATED: Review Confidence Scoring +DEPRECATED (removal planned for v2.0): Review Confidence Scoring ===================================== -This module is DEPRECATED and will be removed in a future version. +This module is DEPRECATED and will be removed in version 2.0.apps/backend/prompts/github/pr_followup_orchestrator.md (1)
178-194: JSON example still contains deprecatedconfidencefield.The example shows
"confidence": 0.92and"confidence": 0.88for finding validations, but the PR removes confidence scoring in favor ofevidence_verified_in_file. This example is inconsistent with the updatedFindingValidationResultschema inpydantic_models.py.🔎 Proposed fix
{ "finding_id": "SEC-001", "validation_status": "confirmed_valid", "code_evidence": "const query = `SELECT * FROM users WHERE id = ${userId}`;", "line_range": [45, 45], "explanation": "SQL injection is present - user input is concatenated...", - "confidence": 0.92 + "evidence_verified_in_file": true }, { "finding_id": "QUAL-002", "validation_status": "dismissed_false_positive", "code_evidence": "const sanitized = DOMPurify.sanitize(data);", "line_range": [23, 26], "explanation": "Original finding claimed XSS but code uses DOMPurify...", - "confidence": 0.88 + "evidence_verified_in_file": true }
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apps/backend/prompts/github/pr_finding_validator.mdapps/backend/prompts/github/pr_followup.mdapps/backend/prompts/github/pr_followup_newcode_agent.mdapps/backend/prompts/github/pr_followup_orchestrator.mdapps/backend/prompts/github/pr_followup_resolution_agent.mdapps/backend/prompts/github/pr_reviewer.mdapps/backend/runners/github/confidence.pyapps/backend/runners/github/models.pyapps/backend/runners/github/services/pydantic_models.pyapps/backend/runners/github/services/response_parsers.pytests/test_finding_validation.pytests/test_structured_outputs.py
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apps/backend/prompts/**/*.md
📄 CodeRabbit inference engine (CLAUDE.md)
Agent prompts should be stored as markdown files in
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apps/backend/prompts/github/pr_followup_orchestrator.mdapps/backend/prompts/github/pr_followup_resolution_agent.mdapps/backend/prompts/github/pr_followup_newcode_agent.mdapps/backend/prompts/github/pr_followup.mdapps/backend/prompts/github/pr_finding_validator.mdapps/backend/prompts/github/pr_reviewer.md
apps/backend/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
apps/backend/**/*.py: Always use the Claude Agent SDK (claude-agent-sdkpackage) for all AI interactions, never use the Anthropic API directly
Use thecreate_client()function fromapps/backend/core/client.pyto instantiate Claude SDK clients, not directClaudeSDKClientinitialization
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apps/backend/runners/github/models.pyapps/backend/runners/github/confidence.pyapps/backend/runners/github/services/response_parsers.pyapps/backend/runners/github/services/pydantic_models.py
tests/**
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tests/test_finding_validation.pytests/test_structured_outputs.py
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tests/test_finding_validation.py (1)
apps/backend/runners/github/services/pydantic_models.py (1)
FindingValidationResult(584-630)
tests/test_structured_outputs.py (1)
apps/backend/runners/github/services/pydantic_models.py (2)
OrchestratorFinding(296-322)DeepAnalysisFinding(74-87)
apps/backend/runners/github/services/response_parsers.py (2)
apps/backend/runners/github/models.py (1)
PRReviewFinding(204-274)apps/frontend/src/preload/api/modules/github-api.ts (1)
PRReviewFinding(295-306)
🪛 Gitleaks (8.30.0)
apps/backend/prompts/github/pr_followup.md
[high] 146-146: Detected a Generic API Key, potentially exposing access to various services and sensitive operations.
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tests/test_structured_outputs.py
[high] 271-271: Detected a Generic API Key, potentially exposing access to various services and sensitive operations.
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🔇 Additional comments (39)
apps/backend/prompts/github/pr_reviewer.md (6)
17-25: Evidence requirements section is clear and well-structured.This section effectively establishes the core principle of evidence-based validation with unambiguous language and actionable requirements. The inclusion of the decision test ("Can I prove this with actual code from the file?") provides reviewers with a clear decision framework.
27-49: "NEVER ASSUME - ALWAYS VERIFY" section is comprehensive and practical.This section effectively translates the evidence-based principle into procedural guidance with concrete rules, verification steps, and real-world false positive examples. The inclusion of specific mistakes (hallucination on line numbers, missing validation context, framework misidentification) demonstrates understanding of actual review challenges. The structure—rules → verification steps → common mistakes—provides clear decision-making guidance.
236-282: JSON output examples properly demonstrate evidence field requirements.All three example findings include concrete evidence fields with actual code snippets, effectively demonstrating the evidence-based validation approach. The evidence varies appropriately by finding type (SQL injection query, endpoint handler, function signature) and supports the described issues. The examples serve as clear implementation guidance for parsers and validators.
285-308: Evidence field correctly marked as REQUIRED with clear definition.The evidence field is properly categorized as required (line 301) with explicit definition emphasizing copy-pasted code. This consistent reinforcement across multiple sections (Evidence Requirements, Field Definitions, Example Finding) effectively communicates the non-negotiable nature of evidence for all findings.
310-321: Guidelines reinforce evidence requirements prominently.Guideline 6 correctly positions evidence as a critical quality criterion, using consistent language ("no code, no finding") with the core principle. The placement among the top quality guidelines appropriately signals importance to reviewers.
323-352: Example finding and important notes effectively reinforce evidence-based approach.The JWT secret hardcoding example (lines 332-352) effectively demonstrates evidence-based finding with realistic code, proportionate fix guidance, and proper field population. The example serves as a capstone to earlier guidance, tying together all requirements in a complete, actionable finding.
apps/backend/prompts/github/pr_followup_resolution_agent.md (2)
51-71: Evidence and verification sections align well with PR objectives.The new "Provide Evidence" (lines 51–56), "NEVER ASSUME - ALWAYS VERIFY" (lines 57–71), and "Evidence Requirements" (lines 102–107) sections clearly establish the shift from confidence-based to evidence-based validation. The explicit directives ("Read the actual code," "Verify the pattern still exists," etc.) and the principle "No evidence = No finding" are well-expressed and actionable.
113-134: JSON examples correctly reflect evidence-based schema.The output examples show the evidence field (e.g., line 118:
"evidence": "cursor.execute(...)") and resolution_notes replacing the previous confidence field. The code snippets in evidence (lines 118, 124, 130–131) are concrete and verifiable, consistent with the PR's core principle.apps/backend/prompts/github/pr_followup_newcode_agent.md (3)
94-117: Evidence and verification requirements are clear and consistent across prompts.The "NEVER ASSUME - ALWAYS VERIFY" section (lines 94–108) and "Evidence Requirements" (lines 109–116) enforce the same evidence-first principle as pr_followup_resolution_agent.md. The statement "No evidence = No finding" (line 116) is a strong, clear mandate that aligns with the PR's core objective.
122-154: JSON output format correctly uses evidence field.The examples at lines 122–154 properly demonstrate the evidence field with actual code snippets (e.g., lines 133, 147) and include context (file, line number, category, severity). The format is consistent with the schema changes described in the PR objectives.
146-146: Gitleaks warning is a false positive; example is intentional.The static analysis tool flagged line 146 as exposing an API key. This is expected—the line intentionally demonstrates a vulnerable pattern that the agent should catch and flag. No action needed.
apps/backend/prompts/github/pr_followup.md (3)
74-79: NEVER ASSUME directive is present but less detailed than in other follow-up prompts.Lines 74–79 introduce the NEVER ASSUME - ALWAYS VERIFY principle but with fewer explicit checks than pr_followup_resolution_agent.md and pr_followup_newcode_agent.md. Consider aligning the level of detail: this is the orchestrating follow-up prompt that spawns specialized agents, so the brevity is acceptable, but ensure it adequately emphasizes reading code and verifying exact lines before reporting.
Consider whether the directive needs more procedural detail (e.g., "Actually READ the code at the file/line you cite" and "Verify there's no validation in surrounding code"), similar to the specialized agent prompts, to maintain consistency.
146-146: Evidence field documentation and examples are consistent with schema changes.Line 184 explicitly marks evidence as REQUIRED in the new_findings schema, and the example at line 146 shows the evidence field populated with actual code (
const API_KEY = 'sk-prod-abc123xyz789';). This aligns with the PR's evidence-based validation objective.Also applies to: 184-184
146-146: Gitleaks warning is a false positive; example is intentional.The static analysis tool flagged line 146 as exposing an API key. This is expected—the line intentionally demonstrates a hardcoded secret that the reviewer should identify and flag. No action needed.
apps/backend/prompts/github/pr_finding_validator.md (3)
3-5: Clear articulation of the evidence-based validation principle.The core principle "Evidence, not confidence scores" and the binary validation model are well-defined. This provides clear guidance for the AI agent to produce consistent, verifiable findings.
48-70: Strong anti-hallucination guidance.The "NEVER ASSUME" block with explicit verification questions and the list of behaviors to avoid is excellent for reducing false positives. This directly addresses the PR objective of preventing unsubstantiated findings.
152-167: Well-structured evidence guidelines table.The decision rules clearly map evidence scenarios to validation statuses. The binary logic (evidence proves/disproves, or escalate) aligns with the PR's goal of eliminating ambiguous confidence scores.
apps/backend/prompts/github/pr_followup_orchestrator.md (1)
208-226: Consistent verification guidance across prompts.The "NEVER ASSUME - ALWAYS VERIFY" block mirrors the guidance in pr_finding_validator.md, ensuring consistent behavior across the agent system.
apps/backend/runners/github/services/pydantic_models.py (5)
49-52: Evidence field added to BaseFinding.The optional
evidencefield with a clear description aligns with the PR's evidence-based validation approach. This field propagates to all finding subclasses.
319-322: Consistent evidence field across orchestrator findings.Both
OrchestratorFindingandParallelOrchestratorFindingnow use theevidencefield instead ofconfidence, maintaining consistency across the finding models.Also applies to: 395-398
461-464: ResolutionVerification requires non-empty evidence.The
min_length=1constraint ensures evidence is always provided, which is appropriate since resolution verification should always include proof of the resolution status.
584-630: FindingValidationResult schema properly implements evidence-based validation.The model includes:
evidence_verified_in_file: boolreplacing confidence scorescode_evidencewithmin_length=1ensuring non-empty proofexplanationwithmin_length=20for meaningful context- Clear docstring explaining the binary validation principle
This is well-designed for the PR's objectives.
540-548: Forward reference to FindingValidationResult is valid.The
from __future__ import annotationsimport (line 25) enables forward references, so usingFindingValidationResultat line 542 before its definition at line 584 is valid in Python 3.12+.tests/test_structured_outputs.py (4)
225-240: Test updated to verify evidence field.The test properly validates the new evidence field with assertions checking for non-null value and expected content.
242-252: Evidence optionality test replaces confidence normalization.Good test coverage for the optional nature of the evidence field, ensuring the model accepts findings without evidence when appropriate.
268-272: Gitleaks false positive - intentional test data.The static analysis flagged
API_KEY = 'sk-prod-12345abcdef'as a potential API key leak. This is intentional test data demonstrating a "hardcoded secret" security finding scenario - not an actual leaked credential. The test correctly exercises the security finding model.
381-394: DeepAnalysisFinding evidence test.Test correctly validates that evidence field works on DeepAnalysisFinding (which inherits from BaseFinding). The test data demonstrates a concrete code evidence pattern.
apps/backend/runners/github/models.py (4)
217-222: Evidence field replaces confidence in PRReviewFinding.The evidence field with clear documentation ("Actual code snippet showing the issue") properly implements the evidence-based validation model. The removal of the confidence field aligns with the PR objectives.
243-246: Serialization includes evidence field.The
to_dictmethod correctly serializes the new evidence field while maintaining all existing validation fields.
266-268: Deserialization loads evidence field.The
from_dictmethod correctly loads the evidence field usingdata.get("evidence"), maintaining backward compatibility with existing data that may not have this field.
44-45: New review categories for verification and redundancy.The
VERIFICATION_FAILEDandREDUNDANCYcategories support the enhanced evidence-based review workflow where unverifiable findings and duplicate code detection are distinct categories.apps/backend/runners/github/services/response_parsers.py (4)
36-38: Evidence length threshold defined.
MIN_EVIDENCE_LENGTH = 20provides a reasonable threshold for valid evidence. This matches themin_length=20constraint on the explanation field inFindingValidationResult, maintaining consistency.
68-75: Evidence-based parsing with clear documentation.The
require_evidenceparameter with defaultTrueenforces evidence validation by default while allowing flexibility. The docstring clearly explains the evidence-based validation approach.
85-95: Evidence extraction and filtering logic.The code correctly:
- Extracts evidence from either
evidenceorcode_snippetfields (backward compatibility)- Filters findings with insufficient evidence when
require_evidence=True- Logs dropped findings with clear reason (character count vs threshold)
113-116: PRReviewFinding constructed with evidence field.The evidence field is correctly populated (or set to
Noneif empty), replacing the previous confidence field. This completes the parser's transition to evidence-based validation.tests/test_finding_validation.py (4)
7-10: Clear documentation of the evidence-based validation change.The updated module docstring explains the shift from confidence scores to
evidence_verified_in_file, helping future maintainers understand the validation model.
95-106: New test case for hallucinated findings.The
test_hallucinated_finding_not_verifiedtest is crucial for validating the scenario where original findings referenced non-existent code. This ensures theevidence_verified_in_file=Falsecase is properly handled.
136-148: Required field validation test.The test correctly verifies that
evidence_verified_in_fileis a required field and that omitting it raises aValidationError. This ensures the schema enforces evidence verification.
400-412: All validation status values tested.The test iterates through all valid status values (
confirmed_valid,dismissed_false_positive,needs_human_review) and verifies each can be used with the new evidence-based model.
| ### `confirmed_valid` | ||
| Use when you verify the issue IS real: | ||
| Use when your code evidence PROVES the issue IS real: | ||
| - The problematic code pattern exists exactly as described | ||
| - The vulnerability/bug is present and exploitable | ||
| - You can point to the specific lines showing the vulnerability/bug | ||
| - The code quality issue genuinely impacts the codebase | ||
| - **Key question**: Does your code_evidence field contain the actual problematic code? | ||
|
|
||
| ### `dismissed_false_positive` | ||
| Use when you verify the issue does NOT exist: | ||
| - The described code pattern is not actually present | ||
| - The original finding misunderstood the code | ||
| - There is mitigating code that prevents the issue (e.g., input validation elsewhere) | ||
| - The finding was based on incorrect assumptions | ||
| Use when your code evidence PROVES the issue does NOT exist: | ||
| - The described code pattern is not actually present (code_evidence shows different code) | ||
| - There is mitigating code that prevents the issue (code_evidence shows the mitigation) | ||
| - The finding was based on incorrect assumptions (code_evidence shows reality) | ||
| - The line number doesn't exist or contains different code than claimed | ||
| - **Key question**: Does your code_evidence field show code that disproves the original finding? | ||
|
|
||
| ### `needs_human_review` | ||
| Use when you cannot determine with confidence: | ||
| - The issue requires runtime analysis to verify | ||
| Use when you CANNOT find definitive evidence either way: | ||
| - The issue requires runtime analysis to verify (static code doesn't prove/disprove) | ||
| - The code is too complex to analyze statically | ||
| - You have conflicting evidence | ||
| - Your confidence is below 0.70 | ||
| - You found the code but can't determine if it's actually a problem | ||
| - **Key question**: Is your code_evidence inconclusive? |
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🧹 Nitpick | 🔵 Trivial
Minor markdown formatting: missing blank lines before headings.
The static analysis tool flagged missing blank lines before headings at lines 82, 89, and 97. This is a minor formatting issue.
🔎 Proposed fix
### `confirmed_valid`
+
Use when your code evidence PROVES the issue IS real:
- The problematic code pattern exists exactly as described
...
### `dismissed_false_positive`
+
Use when your code evidence PROVES the issue does NOT exist:
...
### `needs_human_review`
+
Use when you CANNOT find definitive evidence either way:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ### `confirmed_valid` | |
| Use when you verify the issue IS real: | |
| Use when your code evidence PROVES the issue IS real: | |
| - The problematic code pattern exists exactly as described | |
| - The vulnerability/bug is present and exploitable | |
| - You can point to the specific lines showing the vulnerability/bug | |
| - The code quality issue genuinely impacts the codebase | |
| - **Key question**: Does your code_evidence field contain the actual problematic code? | |
| ### `dismissed_false_positive` | |
| Use when you verify the issue does NOT exist: | |
| - The described code pattern is not actually present | |
| - The original finding misunderstood the code | |
| - There is mitigating code that prevents the issue (e.g., input validation elsewhere) | |
| - The finding was based on incorrect assumptions | |
| Use when your code evidence PROVES the issue does NOT exist: | |
| - The described code pattern is not actually present (code_evidence shows different code) | |
| - There is mitigating code that prevents the issue (code_evidence shows the mitigation) | |
| - The finding was based on incorrect assumptions (code_evidence shows reality) | |
| - The line number doesn't exist or contains different code than claimed | |
| - **Key question**: Does your code_evidence field show code that disproves the original finding? | |
| ### `needs_human_review` | |
| Use when you cannot determine with confidence: | |
| - The issue requires runtime analysis to verify | |
| Use when you CANNOT find definitive evidence either way: | |
| - The issue requires runtime analysis to verify (static code doesn't prove/disprove) | |
| - The code is too complex to analyze statically | |
| - You have conflicting evidence | |
| - Your confidence is below 0.70 | |
| - You found the code but can't determine if it's actually a problem | |
| - **Key question**: Is your code_evidence inconclusive? | |
| ### `confirmed_valid` | |
| Use when your code evidence PROVES the issue IS real: | |
| - The problematic code pattern exists exactly as described | |
| - You can point to the specific lines showing the vulnerability/bug | |
| - The code quality issue genuinely impacts the codebase | |
| - **Key question**: Does your code_evidence field contain the actual problematic code? | |
| ### `dismissed_false_positive` | |
| Use when your code evidence PROVES the issue does NOT exist: | |
| - The described code pattern is not actually present (code_evidence shows different code) | |
| - There is mitigating code that prevents the issue (code_evidence shows the mitigation) | |
| - The finding was based on incorrect assumptions (code_evidence shows reality) | |
| - The line number doesn't exist or contains different code than claimed | |
| - **Key question**: Does your code_evidence field show code that disproves the original finding? | |
| ### `needs_human_review` | |
| Use when you CANNOT find definitive evidence either way: | |
| - The issue requires runtime analysis to verify (static code doesn't prove/disprove) | |
| - The code is too complex to analyze statically | |
| - You found the code but can't determine if it's actually a problem | |
| - **Key question**: Is your code_evidence inconclusive? |
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(MD022, blanks-around-headings)
🤖 Prompt for AI Agents
In apps/backend/prompts/github/pr_finding_validator.md around lines 82 to 102,
there are headings at lines 82, 89, and 97 missing the required blank lines
before them; fix by inserting a single blank line immediately before each of
those heading lines so each heading is separated from the preceding paragraph or
list item, then save and optionally run your markdown linter (e.g.,
markdownlint) to confirm formatting.
| - **Show what changed** - before vs after | ||
| - **Explain WHY** this proves resolution/non-resolution | ||
|
|
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Fix markdown formatting: headings require blank lines.
Headings at lines 54, 60, and 66 are missing blank lines above them, violating MD022 style rules. Add a blank line before each heading.
🔎 Proposed fix
-### 4. Provide Evidence
+
+### 4. Provide Evidence-## NEVER ASSUME - ALWAYS VERIFY
+
+## NEVER ASSUME - ALWAYS VERIFY-**You MUST:**
+
+**You MUST:**Also applies to: 60-64, 66-70
🧰 Tools
🪛 markdownlint-cli2 (0.18.1)
54-54: Headings should be surrounded by blank lines
Expected: 1; Actual: 0; Below
(MD022, blanks-around-headings)
🤖 Prompt for AI Agents
In apps/backend/prompts/github/pr_followup_resolution_agent.md around lines
54-56 (and similarly for ranges 60-64 and 66-70), several Markdown headings are
missing a blank line above them (violating MD022); fix by inserting a single
blank line immediately before each heading at those line ranges so every heading
is separated from the preceding content by an empty line.
| ## Evidence Requirements | ||
|
|
||
| **CRITICAL: Quality over quantity** | ||
| **CRITICAL: No evidence = No finding** |
There was a problem hiding this comment.
Fix markdown formatting: use heading syntax instead of emphasis.
Line 19 uses bold emphasis (**CRITICAL: No evidence = No finding**) where a proper heading would be more appropriate and comply with markdown linting rules.
🔎 Proposed fix
-**CRITICAL: No evidence = No finding**
+### CRITICAL: No evidence = No finding📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| **CRITICAL: No evidence = No finding** | |
| ### CRITICAL: No evidence = No finding |
🧰 Tools
🪛 markdownlint-cli2 (0.18.1)
19-19: Emphasis used instead of a heading
(MD036, no-emphasis-as-heading)
🤖 Prompt for AI Agents
In apps/backend/prompts/github/pr_reviewer.md around line 19, the text currently
uses bold emphasis ("**CRITICAL: No evidence = No finding**") but should be a
Markdown heading; replace the bold markup with an appropriate heading level
(e.g., prepend one or more '#' characters) so the line becomes a proper heading
(for example "# CRITICAL: No evidence = No finding" or "## CRITICAL: No evidence
= No finding") to satisfy markdown linting and improve document structure.
| import warnings | ||
|
|
||
| warnings.warn( | ||
| "The confidence module is deprecated. Use evidence-based validation instead. " | ||
| "See models.py 'evidence' field and pr_finding_validator.md for the new approach.", | ||
| DeprecationWarning, | ||
| stacklevel=2, | ||
| ) |
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Fix deprecation warning message: referenced documentation and fields do not exist.
The warning implementation uses correct Python deprecation mechanics (warnings.warn(), DeprecationWarning, stacklevel=2), but the warning message directs developers to non-existent resources:
- pr_finding_validator.md - This documentation file does not exist in the repository
- models.py 'evidence' field - No 'evidence' field found in the referenced model files
Update the warning message to either:
- Reference actual documentation and fields that exist in the codebase, or
- Remove specific file/field references and provide more general guidance on the migration path
Additionally, since no imports of this module were found in active code (only internal class definitions), the transition period may already be complete or the module is unused. Consider verifying whether this deprecation warning is still necessary.
🤖 Prompt for AI Agents
In apps/backend/runners/github/confidence.py around lines 34 to 41, the
DeprecationWarning message points to non-existent resources; update the warning
to reference actual, existing migration guidance (or remove specific file/field
names). Replace the current message with either a concise general migration note
(e.g., "This module is deprecated; migrate to the evidence-based validation used
elsewhere — see <actual-doc-or-path> or contact the backend team for migration
steps.") or point to the correct existing doc/field if one exists, and verify
whether the module is imported anywhere; if it is unused, consider removing the
warning entirely. Ensure the warning still uses warnings.warn(...,
DeprecationWarning, stacklevel=2).
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apps/backend/merge/git_utils.py (1)
20-28: Update docstring to document the two-path fallback behavior.The docstring should explain that the function checks the new location (
.auto-claude/worktrees/tasks/) first and falls back to the legacy location (.worktrees/) for backward compatibility.🔎 Proposed docstring enhancement
def find_worktree(project_dir: Path, task_id: str) -> Path | None: """ Find the worktree path for a task. + + Searches for the task worktree in two locations: + 1. New location: .auto-claude/worktrees/tasks/ + 2. Legacy location: .worktrees/ (for backward compatibility) Args: project_dir: The project root directory task_id: The task identifier Returns: Path to the worktree, or None if not found """
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apps/backend/merge/git_utils.pyapps/frontend/src/renderer/components/terminal/usePtyProcess.ts
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apps/backend/**/*.py
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apps/backend/**/*.py: Always use the Claude Agent SDK (claude-agent-sdkpackage) for all AI interactions, never use the Anthropic API directly
Use thecreate_client()function fromapps/backend/core/client.pyto instantiate Claude SDK clients, not directClaudeSDKClientinitialization
Files:
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Check for proper error handling and security considerations.
Verify compatibility with Python 3.12+.
Files:
apps/backend/merge/git_utils.py
apps/frontend/src/**/*.{ts,tsx,jsx}
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Always use i18n translation keys for all user-facing text in the frontend instead of hardcoded strings
Files:
apps/frontend/src/renderer/components/terminal/usePtyProcess.ts
apps/frontend/src/**/*.{ts,tsx}
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Use
useTranslation()hook with namespace prefixes (e.g., 'navigation:items.key') for accessing translation strings in React components
Files:
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apps/frontend/**/*.{ts,tsx}
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🔇 Additional comments (2)
apps/frontend/src/renderer/components/terminal/usePtyProcess.ts (2)
114-123: Lifecycle helpers provide clean API for controlled recreation.The
prepareForRecreateandresetForRecreatefunctions give external callers explicit control over the recreation sequence. The naming is clear, and the implementation correctly sets/clears the ref guards.Note: Callers must ensure
resetForRecreate()is always called afterprepareForRecreate(), even in error paths. Otherwise, the terminal will remain stuck in the "creating" state. Consider documenting this requirement in JSDoc comments or the component's documentation.
32-40: Guard correctly prevents race condition during terminal recreation.The condition
if (isCreatedRef.current && !isCreatingRef.current)properly gates the reset logic, ensuring thatisCreatedRefis only cleared when a terminal exists and no external recreation is in progress. ThecurrentCwdRef.current = cwdassignment outside the guard correctly tracks the latest working directory regardless of recreation state.External callers in Terminal.tsx follow the required sequence correctly:
prepareForRecreate()is called before updating the cwd, andresetForRecreate()is called after destroying the terminal. This prevents the creation effect from running until the old terminal is fully destroyed.
| # Check new path first | ||
| new_worktrees_dir = project_dir / ".auto-claude" / "worktrees" / "tasks" | ||
| if new_worktrees_dir.exists(): | ||
| for entry in new_worktrees_dir.iterdir(): | ||
| if entry.is_dir() and task_id in entry.name: | ||
| return entry | ||
|
|
||
| # Legacy fallback for backwards compatibility | ||
| legacy_worktrees_dir = project_dir / ".worktrees" | ||
| if legacy_worktrees_dir.exists(): | ||
| for entry in legacy_worktrees_dir.iterdir(): | ||
| if entry.is_dir() and task_id in entry.name: | ||
| return entry |
There was a problem hiding this comment.
Add error handling to ensure legacy fallback works even if new path is inaccessible.
If new_worktrees_dir.iterdir() raises a filesystem error (e.g., PermissionError, OSError), the function will crash without checking the legacy path, breaking the backward compatibility guarantee.
🔎 Proposed fix to wrap each path check in try-except
- # Check new path first
- new_worktrees_dir = project_dir / ".auto-claude" / "worktrees" / "tasks"
- if new_worktrees_dir.exists():
- for entry in new_worktrees_dir.iterdir():
- if entry.is_dir() and task_id in entry.name:
- return entry
-
- # Legacy fallback for backwards compatibility
- legacy_worktrees_dir = project_dir / ".worktrees"
- if legacy_worktrees_dir.exists():
- for entry in legacy_worktrees_dir.iterdir():
- if entry.is_dir() and task_id in entry.name:
- return entry
+ # Check new path first
+ new_worktrees_dir = project_dir / ".auto-claude" / "worktrees" / "tasks"
+ if new_worktrees_dir.exists():
+ try:
+ for entry in new_worktrees_dir.iterdir():
+ if entry.is_dir() and task_id in entry.name:
+ return entry
+ except (PermissionError, OSError):
+ # If new path is inaccessible, continue to legacy fallback
+ pass
+
+ # Legacy fallback for backwards compatibility
+ legacy_worktrees_dir = project_dir / ".worktrees"
+ if legacy_worktrees_dir.exists():
+ try:
+ for entry in legacy_worktrees_dir.iterdir():
+ if entry.is_dir() and task_id in entry.name:
+ return entry
+ except (PermissionError, OSError):
+ # If legacy path is also inaccessible, return None
+ pass🤖 Prompt for AI Agents
In apps/backend/merge/git_utils.py around lines 30 to 42, the current iteration
over new_worktrees_dir can raise filesystem errors and prevent the legacy
fallback from running; wrap the check/iteration over the new path in a
try/except that catches OSError and PermissionError (or a general OSError) and
on exception silently skip to the legacy path, then likewise wrap the legacy
directory's exists/iterdir in a try/except to handle filesystem errors and
return None if both checks fail; ensure you do not swallow unexpected exceptions
beyond OSError family and keep behavior of returning the matching entry when
found.
| new_worktrees_dir = project_dir / ".auto-claude" / "worktrees" / "tasks" | ||
| if new_worktrees_dir.exists(): | ||
| for entry in new_worktrees_dir.iterdir(): | ||
| if entry.is_dir() and task_id in entry.name: |
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# Test the actual substring matching concern - create a scenario
python3 << 'EOF'
# Simulate the find_worktree function behavior
def test_substring_matching():
# Example directories that could exist
dirs = ["001-auth", "0010-database", "100-setup", "10-legacy"]
# Test cases
test_cases = [
("001", "001-auth"), # Should match 001-auth (exact prefix)
("001", "0010-database"), # BUG: matches unintended!
("100", "100-setup"), # Should match 100-setup
("100", "1000-other"), # BUG: matches unintended!
("10", "10-legacy"), # Should match 10-legacy
("10", "100-setup"), # BUG: matches unintended!
]
for task_id, dirname in test_cases:
matches = task_id in dirname
print(f"task_id='{task_id}' in '{dirname}': {matches}")
test_substring_matching()
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python3 << 'EOF'
# Test the actual substring matching behavior with realistic spec names
def simulate_find_worktree():
"""Simulate the current find_worktree logic"""
# Realistic directory names that would exist
worktree_dirs = [
"001-auth",
"0010-database",
"002-api",
"100-setup",
"10-legacy"
]
# Test cases with different task_id formats
test_cases = [
("001", "Should match only 001-auth"), # 001 in entry.name
("0010", "Should match only 0010-database"), # 0010 in entry.name
("001-auth", "Should match only 001-auth"), # Full spec name
("task-001", "Should match only if stripped"), # With task- prefix
]
for task_id, expected in test_cases:
matches = [d for d in worktree_dirs if task_id in d]
print(f"task_id='{task_id}': Found {matches}")
print(f" Expected: {expected}")
if len(matches) > 1:
print(f" ⚠️ ISSUE: Substring matching found multiple results!")
print()
simulate_find_worktree()
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Substring matching for task_id can match unintended directories.
The condition task_id in entry.name is problematic. For example, with worktree directories like "001-auth" and "0010-database", searching for task_id="001" would match both instead of just "001-auth". Consider using more precise matching such as checking if task_id matches as an exact prefix followed by a dash: entry.name.startswith(f"{task_id}-") or entry.name == task_id.
Note: This function is only called as a fallback when worktree_path is not provided in TaskMergeRequest (which has both fields as required), so the practical impact is limited. However, it's still a code quality issue worth fixing for robustness.
🤖 Prompt for AI Agents
In apps/backend/merge/git_utils.py around line 34, the condition uses substring
matching (`task_id in entry.name`) which can incorrectly match names like
"0010-database" when task_id="001"; change the check to be more precise by
verifying either exact equality or a prefix followed by a dash (e.g., entry.name
== task_id or entry.name.startswith(f"{task_id}-")) so only the intended
worktree directory is matched.
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✅ Auto Claude PR Review - APPROVEDMerge Verdict: ✅ READY TO MERGENo blocking issues found Risk Assessment
Generated by Auto Claude PR Review This approval was generated by Auto Claude. |
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✅ Auto Claude PR Review - APPROVEDMerge Verdict: ✅ READY TO MERGENo blocking issues found Risk Assessment
Generated by Auto Claude PR Review This approval was generated by Auto Claude. |
…untime errors The evidence-based validation migration missed updating: - parallel_followup_reviewer.py:647 - accessed non-existent validation.confidence - parallel_followup_reviewer.py:663 - passed validation_confidence to PRReviewFinding - parallel_orchestrator_reviewer.py:589,972 - passed confidence to PRReviewFinding PRReviewFinding no longer has confidence field (replaced with evidence). FindingValidationResult no longer has confidence field (replaced with evidence_verified_in_file). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <[email protected]>
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⚠️ Outside diff range comments (1)
apps/backend/runners/github/services/parallel_orchestrator_reviewer.py (1)
998-1002: Remove unused_normalize_confidencemethod.This method is no longer used after migrating from confidence scoring to evidence-based validation. Removing dead code improves maintainability and reduces confusion.
🔎 Verify no other usages exist before removal
#!/bin/bash # Description: Verify that _normalize_confidence is not used elsewhere in the codebase # Search for any calls to _normalize_confidence rg -nP '\b_normalize_confidence\b' apps/backend/
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🔇 Additional comments (2)
apps/backend/runners/github/services/parallel_orchestrator_reviewer.py (2)
972-972: No action required — the code is correct.The
evidencefield inPRReviewFindingis explicitly defined asstr | None = None(line 218 ofapps/backend/runners/github/models.py), which meansNoneis not only acceptable but is the intended default value. The changeevidence=f_data.get("evidence")correctly passesNonewhen the key is missing, which aligns with the model's design.Likely an incorrect or invalid review comment.
589-589: Remove the unused_normalize_confidencemethod (lines 998-1002).This method is dead code from the deprecated confidence-based validation approach. Since the evidence field is optional in the PRReviewFinding model, both line 589's direct attribute access and line 972's dictionary
.get()will work correctly—missing evidence will default to None without raising an error. The_normalize_confidencemethod is no longer needed and should be removed.
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🤖 Auto Claude PR Review
✅ Follow-up Review: Ready To Merge
Resolution Status
- ✅ Resolved: 0 previous findings addressed
- ❌ Unresolved: 0 previous findings remain
- 🆕 New Issues: 4 new findings in recent changes
Verdict
The confidence-to-evidence refactoring is complete and correct. All 4 findings are LOW severity quality suggestions (dead code cleanup) that do not block merge. The core migration in pydantic_models.py, models.py, and response_parsers.py properly replaces confidence/validation_confidence with evidence-based validation. The PTY race condition fix in usePtyProcess.ts is correctly implemented. No critical, high, or medium issues found. Previous review was already READY_TO_MERGE with no findings, and this commit successfully addresses the runtime errors mentioned in its commit message.
Review Process
Agents invoked: new-code-reviewer
This is an AI-generated follow-up review using parallel specialist analysis with finding validation.
Findings (4 selected of 4 total)
🔵 [LOW] Unused _normalize_confidence method left in refactoring
📁 apps/backend/runners/github/services/parallel_orchestrator_reviewer.py:998
The _normalize_confidence method was left in the codebase but is never called after the confidence-to-evidence refactoring. This is dead code that should have been removed as part of the cleanup.
Suggested fix:
Remove the _normalize_confidence method as it is no longer used after the evidence-based validation refactoring.
🔵 [LOW] IssueTriageResponse still uses confidence field (intentional)
📁 apps/backend/runners/github/services/pydantic_models.py:266
The IssueTriageResponse model still contains a confidence field. This is intentional because issue triage is a separate feature from PR review finding validation - confidence scoring is valid for categorization decisions. Documented for completeness.
Suggested fix:
No fix needed - IssueTriageResponse is for issue categorization, not PR finding validation.
🔵 [LOW] IssueBatch still uses validation_confidence (intentional)
📁 apps/backend/runners/github/batch_issues.py:306
The IssueBatch dataclass still uses validation_confidence for batch grouping validation, which is a separate concept from PR finding validation. This is intentional and not a bug.
Suggested fix:
No fix needed - batch validation is a different concept from PR finding validation.
🔵 [LOW] Dead code branch checking for confidence attribute
📁 apps/backend/runners/github/output_validator.py:412
The output_validator.py checks for hasattr(finding, 'confidence'). Since PRReviewFinding no longer has a confidence field, this branch is effectively dead code. The code works correctly due to defensive programming.
Suggested fix:
Consider removing the confidence attribute checks since PRReviewFinding no longer has a confidence field.
This review was generated by Auto Claude.
Auto Claude Review - APPROVEDStatus: Ready to Merge Summary: ## ✅ Follow-up Review: Ready To Merge Resolution Status
VerdictThe confidence-to-evidence refactoring is complete and correct. All 4 findings are LOW severity quality suggestions (dead code cleanup) that do not block merge. The core migration in pydantic_models.py, models.py, and response_parsers.py properly replaces confidence/validation_confidence with evidence-based validation. The PTY race condition fix in usePtyProcess.ts is correctly implemented. No critical, high, or medium issues found. Previous review was already READY_TO_MERGE with no findings, and this commit successfully addresses the runtime errors mentioned in its commit message. Review ProcessAgents invoked: new-code-reviewer This is an AI-generated follow-up review using parallel specialist analysis with finding validation. Review Details:
This automated review found no blocking issues. The PR can be safely merged. Generated by Auto Claude |
| fixable=original.fixable, | ||
| validation_status=validation_status, | ||
| validation_evidence=validation_evidence, | ||
| validation_confidence=validation_confidence, |
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Evidence field not passed for new followup findings
The PR adds an evidence field to ParallelFollowupFinding and updates parallel_orchestrator_reviewer.py to pass evidence=finding_data.evidence when creating PRReviewFinding objects. However, the equivalent code in parallel_followup_reviewer.py that processes new_findings and comment_findings does not pass the evidence field. This means evidence collected by the AI for new findings during followup reviews is silently lost, defeating the purpose of the evidence-based validation system. The evidence=nf.evidence and evidence=cf.evidence parameters need to be added to the PRReviewFinding constructor calls.
Additional Locations (1)
Merged upstream changes: - fix(ci): include update manifests for architecture-specific auto-updates (AndyMik90#611) - fix: security hook cwd extraction and PATH issues (AndyMik90#555, AndyMik90#556) (AndyMik90#587) - fix(frontend): filter empty env vars to prevent OAuth token override (AndyMik90#520) - refactor(github-review): replace confidence scoring with evidence-based validation (AndyMik90#628) - feat(terminal): add worktree support for terminals (AndyMik90#625) - fix: human_review status persistence bug (worktree plan path fix) (AndyMik90#605) - fix(frontend): resolve PATH and PYTHONPATH issues in insights and changelog services (AndyMik90#558) (AndyMik90#610) - fix: pass electron version explicitly to electron-rebuild on Windows (AndyMik90#622) - fix(kanban): complete refresh button implementation (AndyMik90#584) Conflict resolution: - insights-executor.ts: Combined local diagnostics with upstream error message improvements 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <[email protected]>
Merged commits: - fix: check .claude.json for OAuth auth in profile scorer (AndyMik90#652) - fix(mcp): use shell mode for Windows command spawning (AndyMik90#572) - fix(ui): update TaskCard description truncation (AndyMik90#637) - fix: change hardcoded Opus defaults to Sonnet (fix AndyMik90#433) (AndyMik90#633) - Fix/small fixes 2.7.3 (AndyMik90#631) - fix(ci): include update manifests for architecture-specific auto-updates (AndyMik90#611) - fix: security hook cwd extraction and PATH issues (AndyMik90#555, AndyMik90#556) (AndyMik90#587) - fix(frontend): filter empty env vars to prevent OAuth token override (AndyMik90#520) - refactor(github-review): replace confidence scoring with evidence-based validation (AndyMik90#628) - feat(terminal): add worktree support for terminals (AndyMik90#625) Resolved conflicts: - Model defaults: Accepted upstream change from opus to sonnet (fix AndyMik90#433) - i18n namespaces: Combined both branches (analytics + terminal) 🤖 Generated with [Claude Code](https://claude.com/claude-code)
Summary
evidence_verified_in_fileboolean instead of confidence floatsChanges
Prompt Updates
Code Updates
evidence_verified_in_fileboolean instead of confidenceTest Updates
evidence_verified_in_fileevidencefieldWhy
The confidence scoring approach (0-100 percentages) was subjective and led to false positives. The new evidence-based approach is binary: either you can prove the issue with actual code, or you don't report it.
Key principle: No evidence = No finding
Test plan
🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
Refactor
Tests
Bug Fixes
✏️ Tip: You can customize this high-level summary in your review settings.