smithery.ai

issue-labeler

Analyze unlabeled GitHub issues and generate label recommendations for review. Supports batch submission after approval.

First seen Apr 17, 2026

Installation

$ npx skills add https://smithery.ai

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Other skills from smithery.ai · top by installs.

npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0
LicenseMIT
CompatibilityRequires gh CLI authenticated with repo access.
Allowed toolsbash gh jq
More metadata
author
patrick
version
1.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,223 B
  • docs SUMMARY.md 141 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Issue Labeler

Generate label recommendations for unlabeled GitHub issues, review them, then submit in batch.

When to Use

  • Repository has many unlabeled or poorly labeled issues
  • You want to triage issues without manually reading each one
  • Need consistent labeling based on issue content

Workflow

Step 1: Generate Recommendations

# Fetch unlabeled issues and analyze them
./scripts/analyze.sh owner/repo

This creates recommendations.json with suggested labels for each issue.

Step 2: Review Recommendations

# Launch the review UI
./scripts/serve.sh

The UI shows:

  • Issue title and preview
  • Current labels (if any)
  • Recommended labels with confidence
  • Checkbox to approve/reject each recommendation

Step 3: Submit Approved Labels

After reviewing in the UI, export approved recommendations and run:

# Apply approved labels
./scripts/apply.sh recommendations-approved.json

Or use the "Submit All" button in the UI to apply via gh CLI.

Label Categories

The analyzer suggests labels from these categories:

Category Labels
Type bug, enhancement, question, documentation
Priority priority-1, priority-2, priority-3
Status triage, needs-info, investigating, confirmed
Area (detected from content: auth, cli, api, ui, etc.)

How Analysis Works

For each unlabeled issue, the LLM analyzes:

  1. Title keywords - error, feature, how to, crash, etc.
  2. Body content - stack traces, repro steps, feature requests
  3. Existing patterns - what labels similar issues have
  4. Repository context - available labels in the repo

Files

issue-labeler/
├── SKILL.md              # This file
├── review.html           # Review UI for recommendations
├── scripts/
│   ├── analyze.sh        # Fetch and generate recommendations
│   ├── apply.sh          # Apply approved labels
│   └── serve.sh          # Launch review UI
├── recommendations.json  # Generated recommendations (git-ignored)
└── approved.json         # Approved recommendations (git-ignored)

Example Recommendation

{
  "number": 123,
  "title": "App crashes when clicking submit",
  "current_labels": [],
  "recommended_labels": ["bug", "priority-2", "needs-info"],
  "confidence": 0.85,
  "reasoning": "Title indicates crash (bug). No repro steps provided (needs-info). User-facing issue (priority-2).",
  "approved": null
}

Safety

  • No labels applied without explicit approval
  • Review UI shows reasoning for each recommendation
  • Dry-run mode available: ./scripts/apply.sh --dry-run
  • All actions logged for audit

Notes

  • Requires gh CLI authenticated with write access to issues
  • Run gh auth status to verify permissions
  • For large repos, analyze in batches using --limit flag