Review Feedback Schema

Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality.

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

csv

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

Field	Type	Description	Example Values
date	ISO date	When review occurred	2025-12-23
file	path	Relative file path	amelia/agents/developer.py
line	string	Line number(s)	128, 190-191
rule_source	string	Skill and rule that triggered issue	python-code-review/common-mistakes:unused-variables, pydantic-ai-common-pitfalls:tool-decorator
category	enum	Issue taxonomy	type-safety, async, error-handling, style, patterns, testing, security
severity	enum	As flagged by reviewer	critical, major, minor
issue	string	Brief description	Return type list[Any] loses type safety
verdict	enum	Human decision	ACCEPT, REJECT, DEFER, ACKNOWLEDGE
rationale	string	Why verdict was chosen	pydantic-ai docs explicitly support this pattern

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.

2. 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.

3. 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.

4. 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

Verdict	Meaning	Action
ACCEPT	Issue is valid, will fix	Code change made
REJECT	Issue is invalid/wrong	No change; may improve skill
DEFER	Valid but not fixing now	Tracked for later
ACKNOWLEDGE	Valid but intentional	Document why it's intentional

When to Use Each

ACCEPT: The reviewer correctly identified a real issue.

csv

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.

csv

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.

csv

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.

csv

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

Category	Description	Examples
type-safety	Type annotation issues	Missing types, incorrect types, Any usage
async	Async/await issues	Blocking in async, missing await
error-handling	Exception handling	Bare except, missing error handling
style	Code style/formatting	Line length, naming conventions
patterns	Design patterns	Anti-patterns, framework misuse
testing	Test quality	Missing coverage, flaky tests
security	Security issues	Injection, secrets exposure

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 project conventions (e.g. AGENTS.md or CLAUDE.md)"
• "Legacy API compatibility requirement"
• "Matches upstream library pattern"

Example Log

csv

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

Pattern	Root Cause	Prevention
"Unused variable"	Variable used elsewhere	Search all references
"Missing validation"	Framework validates	Check Pydantic/Zod/etc.
"Type assertion"	Actually annotation	Confirm as vs :
"Memory leak"	Cleanup exists	Check effect returns
"Wrong syntax"	New framework version	Verify against current docs
"Style issue"	Preference not rule	Both approaches valid

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

Improvement Signals

Pattern	Skill Improvement
"linter passes" rejections	Add linter verification step before flagging style issues
"docs support this" rejections	Add exception for documented framework patterns
"intentional" rejections	Add codebase context check before flagging
"wrong code path" rejections	Add code tracing step before claiming gaps