| name | label-issue |
| description | Classify and label GitHub issues based on repository-specific labeling instructions. Use when (1) auto-labeling new issues, (2) classifying issue types (bug, feature, etc.), (3) adding priority or area labels, (4) applying consistent labeling rules. Triggers on requests like "label issue", "classify issue", "what labels should this issue have", "add labels to issue". |
Label Issue Skill
Automatically classify and label GitHub issues based on repository-specific labeling instructions.
Overview
This skill analyzes GitHub issue content (title, body, comments) and applies appropriate labels based on labeling rules defined in the target repository's .github/llms.md file.
Workflow
- Input: Receive issue URL or issue number with repository (owner/repo)
- Fetch labeling instructions: Read
.github/llms.md from the checked-out repository before deciding labels
- Fetch issue: Get issue details (title, body, existing labels)
- Analyze issue: Match issue content against labeling rules
- Determine labels: Select appropriate labels based on:
- Keyword matching
- Issue type detection (bug, feature, question, etc.)
- Priority assessment
- Area/component identification
- Apply labels: Use Python script to add labels via GitHub API
- Report: Confirm labels applied with reasoning
Reading Labeling Instructions
Fetch .github/llms.md from the checked-out target repository. Do not use GitHub MCP tools. If repository content must be fetched remotely, use gh api with GH_TOKEN/GITHUB_TOKEN. The file should define:
- Available labels: List of valid labels with descriptions
- Labeling rules: Criteria for when to apply each label
- Keywords mapping: Keywords that trigger specific labels
Only apply labels explicitly defined in this document. Do not apply any other labels.
The only exceptions are IssueLens lifecycle labels that are applied by the owning agent, such as ai-triaged and duplicate.
If .github/llms.md is not found:
- Stop and report that labeling cannot continue because repository labeling instructions are missing.
- Do not fall back to applying labels from the repository label list alone.
Issue Analysis
Analyze issue content to determine appropriate labels by:
- Type Detection: Match issue keywords against label names/descriptions
- Priority Assessment: Identify severity indicators in the issue
- Area Detection: Match issue content against area-specific labels
Applying Labels
Run the bundled Python script from the repository root to validate labels against .github/llms.md and add them via gh CLI:
python .github/skills/label-issue/scripts/label_issue.py <owner> <repo> <issue_number> <labels>
python .github/skills/label-issue/scripts/label_issue.py microsoft vscode 123 "bug,ai-triaged"
python .github/skills/label-issue/scripts/label_issue.py microsoft vscode 123 "needs more info,ai-triaged"
The script scripts/label_issue.py handles the gh issue edit call.
Do not use gh issue edit --add-label directly for IssueLens labeling because that bypasses .github/llms.md validation.
Example Commands
Output
Report the labeling decision with:
- Labels applied: List of labels added
- Reasoning: Why each label was chosen
- Type: "Detected as bug (keywords: 'not working', 'error')"
- Priority: "High priority (affects core functionality)"
- Area: "Matched 'ui' area (keywords: button, dialog)"
- Existing labels: Labels already on the issue (not modified)
Example Output
✅ Labels added to issue #123: bug, priority:high, area:ui
**Reasoning:**
- **bug**: Issue describes broken functionality ("button not working")
- **priority:high**: Core feature affected, no workaround mentioned
- **area:ui**: UI-related keywords detected (button, click, display)
**Existing labels:** needs-triage (unchanged)
Configuration
The skill requires:
- GH_TOKEN or GITHUB_TOKEN environment variable with
issues: write permission
- .github/llms.md in target repository
Fallback Behavior
If labeling instructions are not found:
- Do not apply labels.
- Report that
.github/llms.md is required for labeling.
