version: "1.0.1" name: review-feedback-schema description: Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality. user-invocable: false

Review Feedback Schema

Purpose

Structured format for logging code review outcomes. This data enables:

1. Identifying rules that produce false positives
2. Tracking skill accuracy over time
3. Automated skill improvement via pattern analysis

Schema

date,file,line,rule_source,category,severity,issue,verdict,rationale

Gates (feedback log rows)

Run in order before appending a row. Do not skip ahead while a gate fails.

1. Evidence bound to code

• Pass when: file is a repo-relative path that exists (or existed at review time), and line identifies line number(s) you actually opened—not only a paraphrased summary.

1. Rule source attributable

• Pass when: rule_source matches skill-name[/section]:rule-id (see Rule Source Format). If the trigger is unknown, set a best-effort source and state the gap in rationale instead of inventing a rule id.

1. Verdict backed by artifact

• Pass when: For REJECT, rationale cites something checkable (command + output, doc URL, or quoted code). For ACCEPT, it states the fix or points to the change. For DEFER/ACKNOWLEDGE, it names a tracker, timeline, or documented intent per Verdict Types.

1. Row shape valid

• Pass when: The line has nine comma-separated fields matching the header row; fields that contain commas or newlines are CSV-quoted so a standard parser preserves columns.

Verdict Types

When to Use Each

ACCEPT: The reviewer correctly identified a real issue.

2025-12-27,amelia/agents/developer.py,128,python-code-review:type-safety,type-safety,major,Return type list[Any] loses type safety,ACCEPT,Changed to list[AgentMessage]

REJECT: The reviewer was wrong - the code is correct.

2025-12-23,amelia/drivers/api/openai.py,102,python-code-review:line-length,style,minor,Line too long (104 > 100),REJECT,ruff check passes - no E501 violation exists

DEFER: Valid issue but out of scope for current work.

2025-12-22,api/handlers.py,45,fastapi-code-review:error-handling,error-handling,minor,Missing specific exception type,DEFER,Refactoring planned for Q1

ACKNOWLEDGE: Intentional design decision.

2025-12-21,core/cache.py,89,python-code-review:optimization,patterns,minor,Using dict instead of dataclass,ACKNOWLEDGE,Performance-critical path - intentional

Rule Source Format

Format: skill-name/section:rule-id or skill-name:rule-id

Examples:

• python-code-review/common-mistakes:unused-variables
• pydantic-ai-common-pitfalls:tool-decorator
• fastapi-code-review:dependency-injection
• pytest-code-review:fixture-scope

Use the skill folder name and identify the specific rule or section that triggered the issue.

Category Taxonomy

Writing Good Rationales

For ACCEPT

Explain what you fixed:

• "Changed Exception to (FileNotFoundError, OSError)"
• "Fixed using model_copy(update={...})"
• "Removed unused Any import"

For REJECT

Explain why the issue is invalid:

• "ruff check passes - no E501 violation exists" (linter authoritative)
• "pydantic-ai docs explicitly support this pattern" (framework idiom)
• "Intentional optimization documented in code comment" (documented decision)

For DEFER

Explain when/why it will be addressed:

• "Tracked in issue #123"
• "Refactoring planned for Q1"
• "Blocked on dependency upgrade"

For ACKNOWLEDGE

Explain why it's intentional:

• "Performance-critical path per CLAUDE.md"
• "Legacy API compatibility requirement"
• "Matches upstream library pattern"

Example Log

date,file,line,rule_source,category,severity,issue,verdict,rationale
2025-12-20,tests/integration/test_cli_flows.py,407,pytest-code-review:parametrization,testing,minor,Unused extra_args parameter in parametrization,ACCEPT,Fixed - removed dead parameter
2025-12-20,tests/integration/test_cli_flows.py,237-242,pytest-code-review:coverage,testing,major,Missing review --local in git repo error test,REJECT,Not applicable - review uses different error path
2025-12-21,amelia/server/orchestrator/service.py,1702,python-code-review:immutability,patterns,critical,Direct mutation of frozen ExecutionState,ACCEPT,Fixed using model_copy(update={...})
2025-12-23,amelia/drivers/api/tools.py,48-53,pydantic-ai-common-pitfalls:tool-decorator,patterns,major,Misleading RunContext pattern - should use decorators,REJECT,pydantic-ai docs explicitly support passing raw functions with RunContext to Agent(tools=[])
2025-12-23,amelia/drivers/api/openai.py,102,python-code-review:line-length,style,minor,Line too long (104 > 100),REJECT,ruff check passes - no E501 violation exists
2025-12-27,amelia/core/orchestrator.py,190-191,python-code-review:exception-handling,error-handling,major,Generic exception handling in get_code_changes_for_review,ACCEPT,Changed Exception to (FileNotFoundError OSError)
2025-12-27,amelia/agents/developer.py,128,python-code-review:type-safety,type-safety,major,Return type list[Any] loses type safety,ACCEPT,Changed to list[AgentMessage] and removed unused Any import

Pre-Review Verification Checklist

Before reporting ANY finding, reviewers MUST verify:

Verification Steps

1. Confirm the issue exists: Read the actual code, don't infer from context
2. Check surrounding code: The issue may be handled elsewhere (guards, earlier checks)
3. Trace state/variable usage: Search for all references before claiming "unused"
4. Verify assertions: If claiming "X is missing", confirm X isn't present
5. Check framework handling: Many frameworks handle validation/errors automatically
6. Validate syntax understanding: Verify against current docs (Tailwind v4, TS 5.x, etc.)

Common False Positive Patterns

Signals of False Positive Risk

If you're about to flag any of these, double-check:

• "This variable appears unused" → Search for ALL references first
• "Missing error handling" → Check parent/framework handling
• "Should use X instead of Y" → Both may be valid
• "This syntax looks wrong" → Verify against current version docs

Reference: review-verification-protocol for full verification workflow.

How This Feeds Into Skill Improvement

1. Aggregate by rule_source: Identify which rules have high REJECT rates
2. Analyze rationales: Find common themes in rejections
3. Update skills: Add exceptions, clarifications, or verification steps
4. Track impact: Measure if changes reduce rejection rate

See review-skill-improver skill for the full analysis workflow.

Improvement Signals