smithery/jg-chalk-io

moai-core-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

Installation

$ npx skills add smithery/jg-chalk-io --skill moai-core-issue-labels

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More details

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Skill metadata

Parsed from SKILL.md frontmatter.

Version4.0.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 6,188 B
  • docs SUMMARY.md 353 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Enterprise GitHub Issue Labeling Orchestrator

Skill Metadata

Field Value
Skill Name moai-core-issue-labels
Version 4.0.0 Enterprise (2025-11-18)
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: stable      → Waiting for team analysis
status: stable → Team actively investigating
status: stable     → Waiting for external dependency
status: stable       → Ready for implementation
status: stable-progress → Currently being worked on
status: stable      → In code review
status: stable     → In QA/testing
status: stable        → Completed and verified
status: stable     → Intentionally not fixing
status: stable   → 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-core-practices (Workflow patterns)
  • moai-foundation-specs (Issue specification)

For detailed label reference: [reference.md](reference.md) For real-world examples: [examples.md](examples.md) Last Updated: 2025-11-18 Status: Production Ready (Enterprise )