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Agent Orchestration
Skill

/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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hoyeon
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Install
$ npx -y skills add team-attention/hoyeon --skill issue --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/issue

Context preview

The summary Claude sees to decide when to auto-load this skill.

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"

SKILL.md

issue.SKILL.md
name: issue
description: |
  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"
allowed_tools:
  - Read
  - Grep
  - Glob
  - Bash
  - Agent
  - AskUserQuestion
validate_prompt: |
  Must complete with one of:
  1. GitHub issue created (URL returned)
  2. User cancelled after preview
  Must NOT: create issue without user confirmation, skip impact analysis.

/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 type | Label | |-----------|------| | Bug, error, broken behavior | `bug` | | New feature, addition, improvement | `enhancement` | | Documentation related | `documentation` | | Question, investigation, needs clarification | `question` |

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)
Read more
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