본문으로 건너뛰기
cccskills
무료GitHub에 공개

Self-Review

Spawn a reviewer subagent with 4-6 specific verification questions before declaring non-trivial code work done. Triggers when changes are >30 LOC, multi-file, hot-path, prompt-affecting, or genuinely uncertain. Bad asks produce bad answers — this skill forces structured verification questions, not "is this good?"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

wrg32786/aigent-os스타 25(GitHub 25 + cccskills 0)

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Self-Review

Run an explicit second-pair-of-eyes pass on non-trivial work before declaring done. Quality of review depends on quality of question.

When to RUN self-review

  • More than ~30 lines OR multi-file change
  • Hot path (many requests flow through this code)
  • Subtle interactions with code you might have missed
  • Prompt or output affecting user-visible content
  • Code not worked in recently (mental model might be stale)
  • Any genuine uncertainty after writing the change

When to SKIP self-review

  • One-line edit, mechanical fix, obvious diff
  • Pure rename or move with no logic change
  • Code just written with full context, low blast radius
  • Cost-benefit doesn't justify (review takes longer than the fix did)

If skipping, say so explicitly: "Skipping self-review — N-line trivial fix." Don't silently bypass.

How to execute

Step 1 — Set context (3-5 lines max)

State, in plain language:

  • The bug / feature / what was asked
  • The cause (what was actually wrong)
  • The fix (what was actually changed)

Step 2 — List specific changes

- path/to/file_one.ts:L123-L145 — replaced regex with structured parser
- path/to/file_two.ts:L56 — added null guard for empty corpus
- path/to/config.json — bumped diversity_threshold 0.6 → 0.75

Step 3 — Ask 4-6 specific verification questions

Bad asks (do not produce useful answers):

  • "Is this good?"
  • "Does this look right?"
  • "Any issues?"
  • "Did I miss anything?"

Good asks (concrete, falsifiable, named):

  • "Does the new keyword order ever cause a wrong branch in routeRequest?"
  • "Does the diversity rule reach the model in the same prompt as the recent-titles list?"
  • "Is the early return at line 47 still bypassing this check?"
  • "If recent_titles is empty (first run), does the sort comparator return stable output?"
  • "Does the schema migration handle the existing 12K null rows or only new inserts?"

Each question must:

  • Reference a specific file:line or function name
  • State the failure mode you're worried about
  • Be answerable yes/no after a focused read

Step 4 — Ask explicitly about edge cases, regressions, missed interactions

"Edge cases I'm uncertain about:
- What happens when input X is empty?
- Does this break callers Y and Z that I didn't read?
- Any regression on the prior fix from PR #N?"

Step 5 — Request severity ratings + line references

"Please rate findings as HIGH / MEDIUM / LOW with concrete line references."

Step 6 — Spawn the reviewer subagent

Use the Agent tool with a code-reviewer subagent_type (or equivalent). Pass the full context block (steps 1-5) as the prompt. Set model to whatever is appropriate for the review depth.

Step 7 — Process findings

  • HIGH severity → fix before declaring done
  • MEDIUM severity → fix unless you have a specific reason not to. If skipping, document why.
  • LOW severity → judgment call. Often fold into a follow-up note.
  • Reviewer misread → trust your own reading, but re-verify before dismissing. A misread is a signal the code might need clarity.

The reviewer is a second pair of eyes, not an authority. Evaluate every suggestion. Blindly applying every note is as bad as ignoring them all.

Output format

After review completes, append to your delivery message:

**Self-review:** {APPROVED — severity X} / {N findings applied} / {N deferred — listed below}
- {finding} → {fix applied OR reason deferred}

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

Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

wrg32786/aigent-os252026년 10월 29일 업데이트

Compose the AIgent's body state by reading existing signals. Lazy compute. Never polled. Reports the five pressures + recommended reflex.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

wrg32786/aigent-os252026년 10월 29일 업데이트

brief

무료

Generate a structured delegation brief for any task

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

wrg32786/aigent-os252026년 10월 29일 업데이트

Quarterly skill that walks the skill-index against the actual SKILL.md files on disk, reports drift (skills indexed that no longer exist, skills on disk that aren't enrolled, skills whose descriptions have changed since enrollment). Hardens the auto-enrollment pipeline against silent rot.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

wrg32786/aigent-os252026년 10월 29일 업데이트

When a [CADDY] hint fires and the principal is confused why, /caddy-explain walks the scoring transparently — which trigger matched, what points it earned, what the threshold was, what other skills were close. Pairs with the legibility thesis. Caddy's decisions should be inspectable on demand.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

wrg32786/aigent-os252026년 10월 29일 업데이트

Enroll a skill into Caddy's index so it can be surfaced when the right prompt arrives. Use when a new skill is created, copied in, or when a skill needs updated triggers. Usage — "/caddy-enroll <path to SKILL.md>".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

wrg32786/aigent-os252026년 10월 29일 업데이트

wrg32786의 스킬 모두 보기

이 스킬의 문제 신고