Claude-skill-registry-data moai-alfred-issue-labels
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
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry-data
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/moai-alfred-issue-labels" ~/.claude/skills/majiayu000-claude-skill-registry-data-moai-alfred-issue-labels && rm -rf "$T"
manifest:
data/moai-alfred-issue-labels/SKILL.mdsource content
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) |
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
(Workflow patterns)moai-alfred-practices
(Issue specification)moai-foundation-specs
For detailed label reference: reference.md
For real-world examples: examples.md
Last Updated: 2025-11-12
Status: Production Ready (Enterprise v4.0.0)