Agent-Skills.md

Agent Skills: Review Feedback Schema

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

structured-loggingschema-managementquality-metricscode-review
reviewID: existential-birds/beagle/review-feedback-schema

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existential-birds/beagle
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skills/review-feedback-schema/SKILL.md

Skill Metadata

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.

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

| 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 |

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.

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

| 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 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

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

| 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 |