1Password에 저장한 비밀번호와 API 키를 명령과 설정 템플릿에 사용합니다. CLI 설치와 인증부터 비밀 값 조회 및 환경 변수 전달까지 안내합니다.
- 1Password CLI 설치와 설정
- 저장된 API 키로 명령 실행
- 설정 템플릿에 비밀 값 삽입
Independently reviews deliverables handed over through Kanban, checks them against acceptance criteria and test evidence, and records an approval, actionable change request, or escalation when a human decision or external prerequisite is needed.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review Kanban handoffs and route verified outcomes.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Independently verify work handed from a Kanban implementation run to the review lane, then approve it, request changes, or escalate. This skill reviews the deliverable and its evidence; it does not take over the implementer's work.
Use this skill when all of the following are true:
review lane;review_requested handoff;Do not use it for a separate downstream review card. A downstream card is ordinary implementation work with a review-oriented specification and completes through its own lifecycle.
kanban_show, kanban_comment, kanban_complete, kanban_request_changes, and kanban_block.read_file, search_files, and terminal when the deliverable is code.kanban_show.This skill is loaded automatically by the review dispatcher. Start with kanban_show before inspecting files or choosing a verdict.
review_requested handoff.| Verdict | When | Final action |
|---|---|---|
| Approve | Acceptance criteria and verification pass | kanban_complete |
| Request changes | Correctable implementation defects remain | kanban_comment, then kanban_request_changes |
| Escalate | A human decision or external prerequisite is required | kanban_block |
A requested-changes transition returns the task to its original implementer. When that implementer requests review again without naming a reviewer, the persisted reviewer provenance routes the re-review back to the same reviewer profile.
Vary how you look at the work on each round instead of repeating the same inspection. Decorrelated lenses catch different defect classes: a cold read of the artifact surfaces design and correctness problems that the implementer's narrative would have framed away, execution surfaces claims that do not reproduce, and a strict contract audit surfaces quiet scope drift. Repeating the round-1 lens on round 3 mostly re-finds what round 1 already found.
Determine the current round from the history the task record already gives you: count the changes_requested entries in the "Prior attempts on this task" section of your worker context (also visible as prior runs in kanban_show). The current review round is that count plus one. Round 1 therefore shows zero changes_requested attempts; round 2 shows one; and so on.
| Round | Lens | How to apply it |
|---|---|---|
| 1 | Artifact | Read the diff or deliverable cold, before the implementer's summary. Form an independent judgment, then compare it against the handoff narrative and investigate every mismatch. |
| 2 | Execution | Check out the work and actually run it via terminal: build, test, and exercise the reported behavior yourself. Verify each handoff claim empirically instead of re-reading the artifact. |
| 3+ | Contract | Re-read the ORIGINAL task body and acceptance criteria, then audit the deliverable strictly against them. Also verify that every item from every prior kanban_request_changes round actually landed. |
The baseline duties in the Procedure section still apply on every round; the lens sets which inspection you lead with and weight most heavily.
The same principle applies outside the Kanban review lane. When spawning multiple parallel reviewers via delegate_task, give each reviewer a different lens — one diff-only brief, one full-context brief, one checkout-and-run brief — rather than identical briefs. Identical briefs produce correlated verdicts and duplicate findings; varied briefs cover more defect classes for the same review spend.
Call kanban_show and identify:
Treat the handoff as a claim to verify, not as proof that the work is correct.
Map every acceptance criterion to concrete implementation or output evidence. Note omissions, changed semantics, and unrelated scope before deciding whether to run deeper checks.
For code work:
read_file and search_files to inspect the changed paths and their callers.terminal to inspect the diff and run the project's existing focused tests, lint, type checks, or build commands.For non-code work:
Approve only when the acceptance criteria are satisfied and the evidence is sufficient. Call:
kanban_complete(
summary="Reviewed and approved. <what was verified>",
metadata={"review_outcome": "approved", "reviewer_checks": [...]}
)
Include the exact checks that passed and any bounded caveat that does not block acceptance.
Use this for specific, correctable defects. First record actionable findings:
kanban_comment(
task_id="<current-task-id>",
body="Changes requested:\n1. <file or artifact + defect>\n2. <required correction>",
)
Then return the same task to its implementer:
kanban_request_changes(
reason="<concise summary of the required corrections>"
)
State where the defect is, how it reproduces, why it violates the task, and what minimum outcome would resolve it. The transition does not use blocker recurrence accounting.
Use escalation only when the reviewer and implementer cannot resolve the problem without a human decision or external prerequisite:
kanban_block(
reason="escalation: <decision or prerequisite required>"
)
Explain the blocked decision and the smallest information needed to continue.
Do not edit the implementation while acting as reviewer. Request changes and let the implementer produce the next candidate; then independently verify that candidate in the next review run.
kanban_request_changes; reserve kanban_block for genuine external blockers or human decisions.Before submitting the verdict, confirm:
kanban_show was read for the current task and run.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
1Password에 저장한 비밀번호와 API 키를 명령과 설정 템플릿에 사용합니다. CLI 설치와 인증부터 비밀 값 조회 및 환경 변수 전달까지 안내합니다.
손익계산서·재무상태표·현금흐름표를 수식으로 연결한 Excel 모델을 만듭니다. 실적과 예측 가정을 입력하고 시나리오를 비교하며 세 재무제표의 일관성을 단계별로 확인합니다.
Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
기술에 익숙하지 않고 쉽게 불만을 느끼는 사용자로 앱을 시험합니다. 혼란스러운 조작과 이탈 원인을 찾고, 개선 과제와 개인적 불만을 나눠 수정안을 정리합니다.
두 에이전트가 각각 수정한 브랜치의 Git 충돌을 양쪽 변경 내용과 의도에 따라 중립적으로 해결합니다. 충돌별 판단 이유를 기록하고 빌드나 테스트로 통합 결과를 확인합니다.