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skill-review-response

Evaluate feedback from reviewers, CI bots, or other AI agents against the actual code and requirements. Apply valid fixes one at a time, verify each change, and explain disagreements using evidence from callers, tests, or existing conventions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Use when a reviewer, CI bot, or another AI leaves feedback to address

설치 방법 보기

이럴 때 유용합니다

  • Assess whether a review comment requires a code change
  • Respond to feedback from CI bots or AI reviewers
  • Explain disagreements with evidence from the code
  • Resolve conflicts between review feedback and requirements

포함된 파일(2)

  • SKILL.md4.3 KB
  • agents/openai.yaml176 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Host: Codex CLI — This skill was designed for Claude Code and adapted for Codex. Cross-reference commands use installed skill names in Codex rather than /octo:* slash commands. Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. For host tool equivalents, see skills/blocks/codex-host-adapter.md.

Receiving Code Review

Core Principle

Code review requires technical evaluation, not performative agreement.

Never blindly implement review feedback. Verify it's correct for THIS codebase before changing anything.

The Response Pattern

WHEN receiving code review feedback:

1. READ    — Complete feedback without reacting
2. RESTATE — Summarize the requirement in your own words
3. VERIFY  — Check against actual codebase state
4. EVALUATE — Is this technically sound for THIS context?
5. RESPOND — Technical acknowledgment OR reasoned pushback
6. IMPLEMENT — One item at a time, verify each change

Forbidden Responses

NEVER say:

  • "You're absolutely right!" (without verification)
  • "Great catch!" (before confirming it IS a catch)
  • "I'll fix that right away!" (before evaluating whether it needs fixing)
  • "Done!" (without running verification — see skill-verification-gate)

These are social performance, not technical evaluation. They lead to:

  • Implementing wrong suggestions
  • Introducing bugs to "fix" non-issues
  • Wasting time on style preferences disguised as bugs

Evaluation Checklist

For each piece of feedback:

QuestionIf YESIf NO
Is the issue real? (verify in code)Continue evaluationPush back with evidence
Does the suggested fix work here?Continue evaluationPropose alternative
Does fixing this break something else?Fix both or push backImplement the fix
Is this a style preference or a real problem?Acknowledge, deprioritizeFix it
Was this already considered and rejected?Explain the trade-offImplement

How to Push Back

When feedback is wrong or doesn't apply:

> Reviewer: "This function should handle null input"
>
> Response: "Checked — this function is only called from `processUser()`
> (line 47) which validates non-null before dispatch. Adding null handling
> here would be dead code. The caller contract guarantees non-null."

Provide:

  1. What you checked
  2. Why the suggestion doesn't apply
  3. Evidence (line numbers, call sites, tests)

Multi-Provider Review Context

In Claude Octopus workflows, review feedback comes from multiple sources:

  • Codex review — tends toward enterprise patterns, may over-engineer
  • Antigravity review — tends toward ecosystem conformity, may suggest unnecessary dependencies
  • Claude review — tends toward elegance, may under-engineer error handling
  • Sonnet review — tends toward thoroughness, may flag low-priority issues

When providers disagree:

  • Check which provider's suggestion matches the ACTUAL codebase conventions
  • The codebase's existing patterns win over any provider's preferences
  • If two providers flag the same issue, it's probably real

Handling Feedback Loops

When a reviewer flags an issue and you fix it:

  1. Make the fix
  2. Run verification (skill-verification-gate) — prove the fix works
  3. Re-read the original feedback — did you address the root cause or just the symptom?
  4. If the reviewer re-reviews and finds new issues, that's normal — don't get frustrated
  5. Each round should have FEWER issues, not different ones

If the same issue keeps coming back:

  • You're fixing symptoms, not the root cause
  • Stop and re-read the feedback from scratch
  • Ask the reviewer to clarify if the issue is ambiguous

When Review Feedback Conflicts with Requirements

If a reviewer suggests something that contradicts the spec/requirements:

  1. Note the conflict explicitly
  2. Check if the spec is wrong (it might be)
  3. If spec is correct: implement the spec, note the reviewer's concern for future consideration
  4. If spec is wrong: flag to the user before changing anything

Requirements trump review suggestions. User intent trumps both.

리뷰

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같은 저장소의 스킬

Interpret the saved results of a completed AI council review. Count verdicts, check whether enough participants responded and whether models are independent, surface substantive dissent, and explain what action the outcome supports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Explain a completed council review
  • Check vote counts and quorum
  • Assess model independence and dissent
nyldn/claude-octopus4.3천2026년 10월 21일 업데이트

Turn a technical choice into a structured debate between different AI model families. Assign explicit positions, launch the discussion, and summarize agreement, disagreement, and a recommendation supported by the strongest argument that survives the debate.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Compare Redis and Memcached for session storage
  • Debate monorepo versus polyrepo organization
  • Examine a technical choice from opposing positions
nyldn/claude-octopus4.3천2026년 10월 21일 업데이트

Clarify a feature’s requirements with multiple AI providers, combining technical constraints with user and business needs. Produce a scoped definition covering priorities, edge cases, exclusions, and a requirements checklist before implementation begins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Defining requirements for a user authentication system
  • Setting boundaries for a notification feature
  • Identifying technical constraints and edge cases
nyldn/claude-octopus4.3천2026년 10월 21일 업데이트

Review code or documents with available AI providers before delivery. Combine findings into a scored validation report that checks prior requirements and decisions, separates critical issues from warnings, and recommends next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Review authentication code before release
  • Check API readiness before deployment
  • Review a PRD for missing requirements
nyldn/claude-octopus4.3천2026년 10월 21일 업데이트

Develop complex features or business documents by gathering approaches from available AI providers. Combine their perspectives with prior project decisions and quality checks to produce a proposed plan, then proceed with implementation after user confirmation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Planning and implementing a complex feature
  • Comparing approaches for an external service integration
  • Developing a product requirements document or strategy
nyldn/claude-octopus4.3천2026년 10월 21일 업데이트

Research technical, market, or academic topics through multiple available AI providers working in parallel. Combine their findings into a report that preserves disagreements, identifies gaps, attributes sources, and recommends next steps with priorities.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Comparing libraries and implementation patterns
  • Researching markets and competitors
  • Synthesizing literature on a topic
nyldn/claude-octopus4.3천2026년 10월 21일 업데이트

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