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moai-alfred-issue-labels

modu-ai
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About

This Claude Skill automates GitHub issue management using AI-powered labeling with a semantic taxonomy system. It handles issue classification, priority assignment, and workflow automation to accelerate triage processes. Use it for enterprise-level GitHub issue organization, label hierarchy management, and team communication coordination.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/modu-ai/moai-adk
Git CloneAlternative
git clone https://github.com/modu-ai/moai-adk.git ~/.claude/skills/moai-alfred-issue-labels

Copy and paste this command in Claude Code to install this skill

Documentation

Enterprise GitHub Issue Labeling Orchestrator v4.0.0

Skill Metadata

FieldValue
Skill Namemoai-alfred-issue-labels
Version4.0.0 Enterprise (2025-11-12)
AI Integration✅ Context7 MCP, semantic analysis, auto-classification
Auto-loadOn issue creation/update for auto-labeling
CategoriesType, Priority, Status, Component, Custom
Lines of Content850+ with 13+ production examples
Progressive Disclosure3-level (taxonomy, patterns, automation)

What It Does

Provides comprehensive issue labeling system with semantic taxonomy, AI-powered auto-labeling, label hierarchy, workflow automation, and stakeholder communication patterns.


Semantic Label Taxonomy

Type Labels

type: bug          → Something isn't working correctly
type: feature      → New capability or enhancement
type: refactor     → Code restructuring without behavior change
type: chore        → Maintenance tasks (dependencies, configs)
type: docs         → Documentation improvements
type: test         → Test suite improvements
type: security     → Security vulnerability or hardening
type: performance  → Performance optimization
type: infra        → Infrastructure/DevOps changes

Priority Labels

priority: critical  → Blocks production, urgent (SLA: 4 hours)
priority: high      → Significant impact, schedule soon (SLA: 1 day)
priority: medium    → Normal priority, standard schedule (SLA: 1 week)
priority: low       → Nice to have, backlog (SLA: unbounded)

Status Labels

status: triage      → Waiting for team analysis
status: investigating → Team actively investigating
status: blocked     → Waiting for external dependency
status: ready       → Ready for implementation
status: in-progress → Currently being worked on
status: review      → In code review
status: testing     → In QA/testing
status: done        → Completed and verified
status: wontfix     → Intentionally not fixing
status: duplicate   → Duplicate of another issue

Component Labels

component: api          → REST/GraphQL API
component: database     → Database layer
component: auth        → Authentication/Authorization
component: ui          → User interface
component: performance  → Performance-related
component: documentation → Docs and guides
component: infrastructure → DevOps/Cloud
component: sdk          → Client SDK

Special Labels

good first issue  → Suitable for new contributors
help wanted       → Seeking community assistance
needs design      → Requires design/architecture review
needs security review → Requires security audit
breaking-change   → Will break backward compatibility
requires-testing  → Needs comprehensive testing

AI-Powered Auto-Labeling

Detection Heuristics

Issue title/body contains:
  "bug", "error", "crash"     → type: bug
  "feature", "add", "support" → type: feature
  "refactor", "reorganize"    → type: refactor
  "update docs", "README"     → type: docs
  "security", "vulnerability" → type: security
  "slow", "performance"       → type: performance
  "dependency", "package"     → type: chore

Severity Assessment

Critical signals:
  - "production down"
  - "data loss"
  - "security vulnerability"
  - "all users affected"
  - "regression"
  
High signals:
  - "breaks feature"
  - "many users affected"
  - "workaround unknown"
  
Medium signals:
  - "specific feature broken"
  - "some users affected"
  - "workaround exists"
  
Low signals:
  - "cosmetic issue"
  - "single user"
  - "easy workaround"

Label Workflow Automation

Triage Workflow

New Issue
    ↓
Auto-labeled (AI classification)
    ↓
[Label confirmed?]
    ├─ Yes → Route to component owner
    └─ No → Manual triage by team lead
    ↓
Assigned to sprint/milestone
    ↓
In-progress (implementation)
    ↓
Review (code review)
    ↓
Testing (QA verification)
    ↓
Done (released)

Label Transition Rules

triage → investigating → [blocked|ready]
  ↓
ready → in-progress → review → testing → done

Blocked → ready (dependency resolved)
WontFix → closed (decision made)
Duplicate → linked to original

Best Practices

DO

  • ✅ Use exactly 5-8 labels per issue (minimal, curated)
  • ✅ Always include: type + priority + status
  • ✅ Use component labels for multi-repo tracking
  • ✅ Update status as work progresses
  • ✅ Use "blocking" relationships for dependencies
  • ✅ Review and prune unused labels monthly
  • ✅ Link duplicate issues
  • ✅ Add assignee before "in-progress"

DON'T

  • ❌ Use 20+ labels per issue (too much metadata)
  • ❌ Create labels for single issues (not scalable)
  • ❌ Leave issues in "triage" indefinitely
  • ❌ Use labels instead of milestones
  • ❌ Change priority without discussion
  • ❌ Add "working on it" without in-progress label
  • ❌ Forget to update status as issue progresses

Related Skills

  • moai-alfred-practices (Workflow patterns)
  • moai-foundation-specs (Issue specification)

For detailed label reference: reference.md
For real-world examples: examples.md
Last Updated: 2025-11-12
Status: Production Ready (Enterprise v4.0.0)

GitHub Repository

modu-ai/moai-adk
Path: src/moai_adk/templates/.claude/skills/moai-alfred-issue-labels
agentic-aiagentic-codingagentic-workflowclaudeclaudecodevibe-coding

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