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pelikhan merged 1 commit into from
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[docs] docs: condense LabelOps patterns sections #22496

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32 changes: 13 additions & 19 deletions docs/src/content/docs/patterns/label-ops.md
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LabelOps uses GitHub labels as workflow triggers, metadata, and state markers. GitHub Agentic Workflows supports two distinct approaches to label-based triggers: `label_command` for command-style one-shot activation, and `names:` filtering for persistent label-state awareness.

## When to Use LabelOps

Use LabelOps for priority-based workflows (run checks when `priority: high` is added), stage transitions (trigger actions when moving between workflow states), specialized processing (different workflows for different label categories), and team coordination (automate handoffs between teams using labels).

## Label Command Trigger

The `label_command` trigger treats a label as a one-shot command: applying the label fires the workflow, and the label is **automatically removed** so it can be re-applied to re-trigger. This is the right choice when you want a label to mean "do this now" rather than "this item has this property."
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## Common LabelOps Patterns

**Priority Escalation**: Trigger workflows when high-priority labels (`P0`, `critical`, `urgent`) are added. The AI analyzes severity, notifies team leads, and provides escalation guidance with SLA compliance requirements.

**Label-Based Triage**: Respond to triage label changes (`needs-triage`, `triaged`) by analyzing issues and suggesting appropriate categorization, priority levels, affected components, and whether more information is needed.

**Security Automation**: When security labels are applied, automatically check for sensitive information disclosure, trigger security review processes, and ensure compliance with responsible disclosure policies.

**Release Management**: Track release-blocking issues by analyzing timeline impact, identifying blockers, generating release note content, and assessing testing requirements when release labels are applied.
| Pattern | Trigger Labels | Agent Response |
|---------|---------------|----------------|
| **Priority Escalation** | `P0`, `critical`, `urgent` | Analyze severity, notify leads, provide SLA guidance |
| **Label-Based Triage** | `needs-triage`, `triaged` | Suggest categorization, priority, affected components |
| **Security Automation** | Security labels | Check disclosure risks, trigger review process |
| **Release Management** | Release labels | Analyze timeline, identify blockers, draft release notes |

## AI-Powered LabelOps

**Automatic Label Suggestions**: AI analyzes new issues to suggest and apply appropriate labels for issue type, priority level, affected components, and special categories. Configure allowed labels in `safe-outputs` to control which labels can be automatically applied.

**Component-Based Auto-Labeling**: Automatically identify affected components by analyzing file paths, features, API endpoints, and UI elements mentioned in issues, then apply relevant component labels.

**Label Consolidation**: Schedule periodic label audits to identify duplicates, unused labels, inconsistent naming, and consolidation opportunities. AI analyzes label usage patterns and creates recommendations for cleanup and standardization.
- **Automatic Label Suggestions**: Analyze issues and apply labels for type, priority, and component. Use `safe-outputs.add-labels.allowed` to restrict which labels can be applied automatically.
- **Component Auto-Labeling**: Identify affected components from file paths, APIs, and UI elements, then apply relevant component labels.
- **Label Consolidation**: Schedule audits to identify duplicate, unused, and inconsistently named labels.

## Best Practices

Use specific label names in filters to avoid unwanted triggers (prefer `ready-for-review` over generic `ready`). Combine with safe outputs to maintain security while automating label-based workflows. Document label meanings in a LABELS.md file or use GitHub label descriptions. Limit automation scope by filtering for explicit labels like `automation-enabled`.

Address label explosion with AI-powered periodic audits for consolidation. Prevent ambiguous labels through AI suggestions and clear descriptions. Reduce manual upkeep by implementing AI-powered automatic labeling on issue creation and updates.
- Use specific label names (`ready-for-review` not `ready`) to avoid unintended triggers.
- Document label meanings in a LABELS.md file or GitHub label descriptions.
- Limit automation scope with opt-in labels like `automation-enabled`.
- Use safe outputs for all write operations to maintain security.

## Additional Resources

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