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name: "moai-alfred-issue-labels" version: "4.0.0" created: 2025-11-11 updated: 2025-11-12 status: stable description: Enterprise GitHub issue labeling orchestrator with semantic label taxonomy, AI-powered auto-labeling, label hierarchy system, workflow automation, issue triage acceleration, and stakeholder communication; activates for issue classification, label management, workflow automation, priority assignment, and team communication keywords: ['github-labels', 'issue-triage', 'label-taxonomy', 'ai-labeling', 'workflow-automation', 'issue-classification', 'priority-management', 'team-communication', 'semantic-labels', 'enterprise-triage'] allowed-tools:

• Read
• Bash
• AskUserQuestion
• mcp__context7__resolve-library-id
• mcp__context7__get-library-docs
• WebFetch

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Enterprise GitHub Issue Labeling Orchestrator v4.0.0

Skill Metadata

Field	Value
Skill Name	moai-alfred-issue-labels
Version	4.0.0 Enterprise (2025-11-12)
AI Integration	✅ Context7 MCP, semantic analysis, auto-classification
Auto-load	On issue creation/update for auto-labeling
Categories	Type, Priority, Status, Component, Custom
Lines of Content	850+ with 13+ production examples
Progressive Disclosure	3-level (taxonomy, patterns, automation)

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What It Does

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

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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

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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"

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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

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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

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Related Skills

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

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