[EPIC] Automated CI/CD Failure Detection and Remediation System

Description

This EPIC implements an intelligent, AI-powered system that automatically detects, analyzes, and remediates CI/CD failures in GitHub Actions workflows. The system uses Claude Code with the cicd-diagnostics skill to provide sophisticated failure classification, automatic issue management, and self-healing capabilities.

Problem Solved: CI/CD failures currently require manual investigation, leading to delayed feedback, developer context switching, and inconsistent issue tracking. Flaky tests, merge conflicts, infrastructure issues, and deterministic failures are not distinguished, making it hard to prioritize fixes.

Proposed Solution: Deploy an automated workflow that:

• Detects failures across merge queue, PR, trunk, and nightly workflows
• Uses AI to categorize failures (flaky tests, deterministic failures, merge conflicts, workflow issues, infrastructure problems)
• Automatically creates/updates GitHub issues with intelligent deduplication
• Generates failure rate statistics and trend analysis
• Optionally creates fix PRs for issues that don't require human clarification
• Supports both automated (CI) and interactive (local developer) usage

Customer Value:

• Reduced MTTR: Automatic diagnosis cuts mean-time-to-resolution by 40-60%
• Developer Productivity: Eliminates manual failure investigation and context switching
• Issue Quality: Consistent, AI-powered categorization with detailed evidence
• Self-Healing: Automatic fixes for flaky tests, workflow issues, and stale branches
• Visibility: Clear tracking of failure patterns and trends

Desired Outcome

Success Metrics:

• Diagnostic Accuracy: >80% correct root cause identification
• Issue Management: <10% duplicate issues created, >90% actionable issues
• MTTR Reduction: 20% decrease in average time from failure to resolution
• Automation Effectiveness: <5% of failures require manual investigation (low local skill usage = successful automation)
• Auto-Fix Success: >50% of auto-generated PRs merged without changes
• Team Satisfaction: Positive feedback on issue quality and categorization accuracy

Behavioral Evidence:

• All merge queue failures automatically diagnosed within 10 minutes
• Issues created with proper categorization (flaky, deterministic, infrastructure, workflow, etc.)
• Failure rate tracking shows trending data over 7/30 days
• Auto-fix PRs created for eligible categories with high confidence
• Team uses issue labels to filter and prioritize work
• Low local skill usage indicates automation handles most cases effectively
• Developers report reduced time spent on failure investigation

Target Personas

• Developer teams
• DevOps teams
• System administrators (dotCMS)

Links

• Implementation Plan
• cicd-diagnostics Skill
• Existing Workflow Documentation

Begin with the linked implementation plan, then inspect .claude/skills/cicd-diagnostics and .github/workflows to understand the proposed scope and existing workflow conventions. Done means the failure detection, classification, issue management, trend reporting, and optional remediation behaviors meet the stated success metrics and behavioral evidence.