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issue

GitHub issue creation skill. Analyzes the entire codebase impact based on user request, then creates a structured issue with AI-verified/human-judgment-needed/caution sections. /issue "issue description" Trigger: "/issue", "이슈 만들어", "issue 만들자", "깃헙 이슈"

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包含的文件(1)

  • SKILL.md5.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

/issue — Structured GitHub Issue Creator

Investigate the codebase based on the user's request and create a GitHub issue with clearly defined confidence boundaries.

Input

The text the user typed after /issue is the original request. Preserve it verbatim.

Examples:

  • /issue Duplicate Shorts URL fetches in YouTube subscription feed
  • /issue Add notification settings tab to Settings page
  • /issue Scheduler occasionally runs twice

If the input is too vague (e.g., "there's a bug"), ask ONE clarifying question. Otherwise, start investigating immediately.

Phase 1: Impact Analysis

Perform a full impact analysis based on the user's request. Use Agent to investigate in parallel.

What to Investigate

Launch agents in parallel where possible:

  1. Related code exploration — Identify files, functions, and modules directly related to the request
  2. Dependency analysis — Where is this code referenced, and which modules are affected
  3. Existing test coverage — Whether related tests exist and what they cover
  4. Related issues/history — Relevant change history from git log, known issues

Classifying Findings

Classify all findings into three confidence levels:

✅ AI Verified

Objective facts confirmed through code exploration. No need for human re-verification.

  • Function/file locations, call relationships
  • Whether tests exist
  • Current behavior (as read directly from code)
  • Relevant config values, environment variables

🤔 Decision Required

Decision points that AI cannot make on your behalf.

  • Trade-off choices (performance vs. accuracy, UX vs. security, etc.)
  • Business logic decisions
  • Scope decisions (how much to fix)
  • Priority judgment

⚠️ Human Verify

Risks and caveats AI may have missed.

  • Potential side effects
  • Risks from production environment differences
  • External service dependencies
  • Whether data migration is needed
  • Areas AI could not verify (external systems, real user data, etc.)

Phase 2: Preview & Confirm

After investigation, show the user a preview of the issue body.

Issue Body Template

## Request

> {original text the user typed after /issue, verbatim}

## Impact Analysis

### Related Code
- `file:line` — description
- ...

### Scope of Impact
- List of affected modules/features

---

## ✅ AI Verified
> Facts confirmed through code exploration. No further verification needed.

- [ ] Confirmed fact 1
- [ ] Confirmed fact 2

## 🤔 Decision Required
> Decision points requiring human judgment.

- [ ] Decision point 1 — Option A vs B, considerations
- [ ] Decision point 2

## ⚠️ Human Verify
> Risks AI may have missed. Needs human review before and/or after implementation.

- [ ] Verification point 1 — why this needs checking
- [ ] Verification point 2

After showing the preview, confirm with AskUserQuestion:

AskUserQuestion(
  question: "Should I create a GitHub issue with this content?",
  header: "Issue Preview",
  options: [
    { label: "Create", description: "Create the issue as-is" },
    { label: "Edit then create", description: "I want to make changes first" },
    { label: "Cancel", description: "Do not create the issue" }
  ]
)
  • Create → Proceed to Phase 3
  • Edit then create → Incorporate user feedback, then show preview again
  • Cancel → "Issue creation cancelled." → Stop

Phase 3: Create Issue

Create the issue with gh issue create.

gh issue create --title "Issue title" --body "$(cat <<'EOF'
Issue body
EOF
)"

Title Rules

  • Under 70 characters
  • Use a prefix: feat:, fix:, refactor:, chore:, etc. (based on content)
  • English or Korean OK

Label Auto-mapping

Based on the issue content, add matching labels via the --label flag using the table below. Multiple labels allowed. If no match, create without labels.

Issue typeLabel
Bug, error, broken behaviorbug
New feature, addition, improvementenhancement
Documentation relateddocumentation
Question, investigation, needs clarificationquestion

After creation, return the issue URL to the user.

Hard Rules

  1. Investigate first — Never create an issue without investigation
  2. Confirm first — Never create an issue without user confirmation
  3. Preserve original — The user's original request must be included verbatim in the "Request" section
  4. Facts only — AI Verified contains only things directly confirmed from code. No speculation.
  5. Be honest — Anything unverified goes into Human Verify. Never pretend to know.
  6. Keep it concise — Do not let the issue body grow unnecessarily long

Checklist Before Stopping

  • Codebase impact analysis completed
  • Findings classified into three confidence levels
  • User's original request included verbatim
  • User reviewed the preview
  • gh issue create executed and URL returned (or user cancelled)

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Analyze an open-source project from What/Why perspective (not how-it's-implemented). Use when the user says "/analyze-oss", "분석해줘 이 오픈소스", "이 레포 뭐하는거야", "analyze this repo", "what does X do", "이거 왜 쓰는거야", "이 라이브러리 분석", provides a GitHub URL and wants understanding, or asks to deeply understand an OSS project's purpose, value, target users, and usage flow. Clones the repo to ~/opensource-analysis/<repo-name>/ (git pull if already exists), dispatches parallel subagents per analysis lens, then synthesizes a What/Why-focused report in chat. Supports optional user-specific follow-up questions.

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

blueprint

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"/blueprint", "blueprint", "task graph", "contract derivation", "execution plan", "plan tasks from requirements", "contract-first planning" Turn requirements.md into an executable blueprint (plan.json + contracts.md). Five phases: Contracts → Tasks → Journeys → Verify Plan → Commit. Sits between /specify and /execute. Scope-adaptive (greenfield → bugfix). Uses hoyeon-cli (plan.json only; requirements.md is read as-is via Read tool).

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

Recon-first browser automation. Orchestrator explores the site first via chromux, saves a guide file with insights, then delegates execution to browser-explorer agent. Use when: "/browser-work", "브라우저 작업", "사이트에서 해줘", "웹에서 해줘", "LinkedIn에서", "크롬으로", "browser task", "automate this site".

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

bugfix

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Root cause based one-shot bug fix. debugger diagnosis → requirements.md generation → /execute. /bugfix "error description" Full investigation pipeline: debugger + gap-analyzer + standard verify. QA suggestion after successful fix.

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

check

免费

This skill should be used when the user wants to verify their changes before pushing, or update the project's rule checklists. Phase 1: validate changed files against .hoyeon/rules/ checklists and report PASS/WARN. Phase 2 (conditional): propose rule additions when unmatched patterns are detected. Essential before git push. Trigger phrases: "check", "checklist", "verify changes", "what did I miss", "pre-push check", "cascading changes", "any more changes needed", "update checklist", "update rules", "rules update", "체크", "체크리스트", "변경 확인", "빠뜨린 거 없나", "push 전 확인", "뭐 더 건드려야 해?", "연쇄 변경 확인", "규칙 갱신".

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

clarify

免费

"/clarify", "clarify this", "keep asking until clear", "remove ambiguity", "clarify requirements", "clarify design", "clarify the plan", "질문 계속해", "모호한 게 없게", "명확해질 때까지", "계속 물어봐", "Q&A로 정리", "질문답변 기록", "요구사항 명확화", "설계 명확화". Relentless ambiguity-resolution interview that records Q&A under .hoyeon/clarify/<topic>/ and hands off to specify/blueprint/docs when clear.

中文简介正在准备中。当前显示原文说明。

team-attention/hoyeon1732026年5月21日 更新

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