Implement comprehensive error handling for Python code paths to keep services resilient and user-friendly. Use when failures are currently silent or exceptions leak through.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide authors through a structured self-review before requesting peer review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
I help you review your own pull request through a structured five-category checklist before you request external review:
By catching issues before external review, you save reviewers' time and reduce review cycles.
Supporting files in this skill:
assets/self-review-checklist.template — The five-category checklist as a markdown file you can copy into a PR commentUse when you want to:
Stage: Author pre-review checklist (after implementation, before external review).
pr-create when you need to draft the PR title/body before opening it.pr-review when reviewing someone else's PR as a reviewer.pr-split when the core question is whether to split a large PR."Review my own PR before I request review" "Walk me through a self-review checklist" "Help me check my PR for debug code and missing tests"
Here's how I guide you through self-review:
I'll ask for your PR diff or a link to the open PR if not provided in context.
I'll ask: Does the PR description accurately describe every file in the diff?
I'll ask: Are all files in the diff intentionally included?
I'll ask: Are there any obvious leftover debug artifacts? I'll flag obvious issues like:
console.log, print, println, println! (debug output)debugger, pdb, debug! (breakpoints).only, .skip on test cases (ensures tests run)For TODO/FIXME comments, I'll ask: did you intend to resolve these before requesting review?
Note: Thorough artifact detection is your reviewers' job—this catches the obvious ones you might have missed.
I'll ask: Does every changed function/method have test coverage?
I'll ask: Is this PR focused on a single concern, or does it mix unrelated changes?
/pr-split skillI'll summarize the findings:
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.
Implement comprehensive error handling for Python code paths to keep services resilient and user-friendly. Use when failures are currently silent or exceptions leak through.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Tabular and numerical data analysis with descriptive statistics and insights. Use when the user provides data, tables, CSVs, or numbers and wants analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Financial factsheet analysis with key metrics extraction and investment rationale
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Diagnose and fix CI failures without leaving your editor.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reproduce ai-repo PR checks locally with Poe and CI scripts, including per-package typecheck and coverage behavior. Use when validating changes before push or when user asks which local commands match CI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Ask clarifying questions before implementing to ensure Python requirements are understood. Use when a task lacks detail, dependencies are unclear, or multiple interpretations are possible.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.