본문으로 건너뛰기
cccskills
무료GitHub에 공개

receiving-code-review

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md6.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Code Review Reception

Overview

Code review requires technical evaluation, not emotional performance.

Core principle: Verify before implementing. Ask before assuming. Technical correctness over social comfort.

The Response Pattern

WHEN receiving code review feedback:

1. READ: Complete feedback without reacting
2. UNDERSTAND: Restate requirement in own words (or ask)
3. VERIFY: Check against codebase reality
4. EVALUATE: Technically sound for THIS codebase?
5. RESPOND: Technical acknowledgment or reasoned pushback
6. IMPLEMENT: One item at a time, test each

Forbidden Responses

NEVER:

  • "You're absolutely right!" (explicit instruction-file violation)
  • "Great point!" / "Excellent feedback!" (performative)
  • "Let me implement that now" (before verification)

INSTEAD:

  • Restate the technical requirement
  • Ask clarifying questions
  • Push back with technical reasoning if wrong
  • Just start working (actions > words)

Handling Unclear Feedback

IF any item is unclear:
  STOP - do not implement anything yet
  ASK for clarification on unclear items

WHY: Items may be related. Partial understanding = wrong implementation.

Example:

your human partner: "Fix 1-6"
You understand 1,2,3,6. Unclear on 4,5.

❌ WRONG: Implement 1,2,3,6 now, ask about 4,5 later
✅ RIGHT: "I understand items 1,2,3,6. Need clarification on 4 and 5 before proceeding."

Source-Specific Handling

From your human partner

  • Trusted - implement after understanding
  • Still ask if scope unclear
  • No performative agreement
  • Skip to action or technical acknowledgment

From External Reviewers

BEFORE implementing:
  1. Check: Technically correct for THIS codebase?
  2. Check: Breaks existing functionality?
  3. Check: Reason for current implementation?
  4. Check: Works on all platforms/versions?
  5. Check: Does reviewer understand full context?

IF suggestion seems wrong:
  Push back with technical reasoning

IF can't easily verify:
  Say so: "I can't verify this without [X]. Should I [investigate/ask/proceed]?"

IF conflicts with your human partner's prior decisions:
  Stop and discuss with your human partner first

your human partner's rule: "External feedback - be skeptical, but check carefully"

YAGNI Check for "Professional" Features

IF reviewer suggests "implementing properly":
  grep codebase for actual usage

  IF unused: "This endpoint isn't called. Remove it (YAGNI)?"
  IF used: Then implement properly

your human partner's rule: "You and reviewer both report to me. If we don't need this feature, don't add it."

Implementation Order

FOR multi-item feedback:
  1. Clarify anything unclear FIRST
  2. Then implement in this order:
     - Blocking issues (breaks, security)
     - Simple fixes (typos, imports)
     - Complex fixes (refactoring, logic)
  3. Test each fix individually
  4. Verify no regressions

When To Push Back

Push back when:

  • Suggestion breaks existing functionality
  • Reviewer lacks full context
  • Violates YAGNI (unused feature)
  • Technically incorrect for this stack
  • Legacy/compatibility reasons exist
  • Conflicts with your human partner's architectural decisions

How to push back:

  • Use technical reasoning, not defensiveness
  • Ask specific questions
  • Reference working tests/code
  • Involve your human partner if architectural

If you're uncomfortable pushing back out loud: Name that tension, then tell your partner about the issue you've seen. They'll appreciate your honesty.

Acknowledging Correct Feedback

When feedback IS correct:

✅ "Fixed. [Brief description of what changed]"
✅ "Good catch - [specific issue]. Fixed in [location]."
✅ [Just fix it and show in the code]

❌ "You're absolutely right!"
❌ "Great point!"
❌ "Thanks for catching that!"
❌ "Thanks for [anything]"
❌ ANY gratitude expression

Why no thanks: Actions speak. Just fix it. The code itself shows you heard the feedback.

If you catch yourself about to write "Thanks": DELETE IT. State the fix instead.

Gracefully Correcting Your Pushback

If you pushed back and were wrong:

✅ "You were right - I checked [X] and it does [Y]. Implementing now."
✅ "Verified this and you're correct. My initial understanding was wrong because [reason]. Fixing."

❌ Long apology
❌ Defending why you pushed back
❌ Over-explaining

State the correction factually and move on.

Common Mistakes

MistakeFix
Performative agreementState requirement or just act
Blind implementationVerify against codebase first
Batch without testingOne at a time, test each
Assuming reviewer is rightCheck if breaks things
Avoiding pushbackTechnical correctness > comfort
Partial implementationClarify all items first
Can't verify, proceed anywayState limitation, ask for direction

Real Examples

Performative Agreement (Bad):

Reviewer: "Remove legacy code"
❌ "You're absolutely right! Let me remove that..."

Technical Verification (Good):

Reviewer: "Remove legacy code"
✅ "Checking... build target is 10.15+, this API needs 13+. Need legacy for backward compat. Current impl has wrong bundle ID - fix it or drop pre-13 support?"

YAGNI (Good):

Reviewer: "Implement proper metrics tracking with database, date filters, CSV export"
✅ "Grepped codebase - nothing calls this endpoint. Remove it (YAGNI)? Or is there usage I'm missing?"

Unclear Item (Good):

your human partner: "Fix items 1-6"
You understand 1,2,3,6. Unclear on 4,5.
✅ "Understand 1,2,3,6. Need clarification on 4 and 5 before implementing."

GitHub Thread Replies

When replying to inline review comments on GitHub, reply in the comment thread (gh api repos/{owner}/{repo}/pulls/{pr}/comments/{id}/replies), not as a top-level PR comment.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

spacering-net/codeg4천2026년 10월 19일 업데이트

Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

spacering-net/codeg4천2026년 10월 19일 업데이트

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

spacering-net/codeg4천2026년 10월 19일 업데이트

Use when you have a written implementation plan to execute in a separate session with review checkpoints

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

spacering-net/codeg4천2026년 10월 19일 업데이트

Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

spacering-net/codeg4천2026년 10월 19일 업데이트

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

spacering-net/codeg4천2026년 10월 19일 업데이트

spacering-net의 스킬 모두 보기

이 스킬의 문제 신고