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.
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Proactively draft and create pull requests by inferring title, refs, and body from git context, then confirm and open via gh CLI.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
I take ownership of PR authoring end-to-end:
gh pr create after explicit user approval.The default outcome is an opened GitHub PR (not just a draft message), unless you choose to edit before creation.
Supporting files in this skill:
assets/pr-description.template — Markdown structure used when composing the final bodyUse when you want to:
Stage: Authoring and opening a PR (before review starts).
pr-self-review when the PR draft is ready and you want an author checklist before requesting review.pr-split when the main question is PR size/scope and whether to split.github-pull-request-handling when review threads already exist and you need to resolve/reply to comments."Help me write a PR for this feature" "Create the PR from this branch, you choose title/body" "Open a PR and infer ticket refs from commits" "Draft everything, show me once, then create with gh if I approve"
Here is the default execution flow (agent-led, low-friction):
Gather context without asking the user for manual inputs first:
main/master or configured default)Parse issue identifiers from branch names and commit messages. Prefer existing refs over asking the user.
Common patterns to detect:
[A-Z][A-Z0-9]+-\d+ # Jira style, e.g. PROJ-123
#\d+ # GitHub issue shorthand, e.g. #456
(?:fixes|closes|refs)\s+#?\d+ # commit trailer style refs
Examples:
PROJ-123-feature-name -> PROJ-123fix(auth): handle timeout (refs #812) -> #812closes #991 -> #991If no reference is found, ask one focused question:
Generate one best-guess semantic title using:
<type>(<scope>): <subject>
Where:
feat, fix, chore, ci, deploy, docs, improvement, refactor, testauth, api, ui)Produce a ready-to-submit body with:
## Summary (1-3 outcome-focused bullets)## Changes (grouped by area or subsystem)## Test plan (actual commands run and/or manual verification steps)## Risk / rollout (risks, migration notes, rollback hints when relevant)Refs: ... line when an issue reference is availableDo not ask the user to write these sections first. Draft them from repository context, then let the user edit/approve.
Show the proposed title + body and ask:
gh pr create?"Only ask additional questions if required information is truly missing (for example, no issue ref found and project requires one).
gh on explicit approvalAfter clear approval:
gh pr create --title ... --body ... (use HEREDOC for body)If the user does not approve, stop after sharing the draft and wait for edits.
Use this structure for the generated body:
## Summary
- (1–3 bullets describing what changed and why)
## Changes
- (group by area: `python/`, `infra/`, `service/`, etc.)
## Test plan
- [ ] (exact commands you ran or manual steps you completed)
## Risk / rollout
- (optional) Risks, staging notes, rollback instructions
Always include Refs: <ticket> when possible (Jira, Linear, GitHub issue).
If there are breaking changes, include migration notes in Risk / rollout.
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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.
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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.