📊 Workflow Health Dashboard - 2026-01-04

[github-actions]

Workflow Health Dashboard - 2026-01-04

Overview

• Total Workflows: 128
• Healthy: Unable to determine (no metrics data) 🔴
• Warning: 10 (7.8%) - Outdated lock files ⚠️
• Critical: 2 issues identified 🚨
• Inactive: Unknown (no metrics data)

Critical Issues 🚨

Issue 1: Metrics Collection System Down

• Status: No execution metrics available
• Error: /tmp/gh-aw/repo-memory-default/memory/default/metrics/latest.json does not exist
• Impact: Cannot monitor workflow health, success rates, or failure patterns
• Root Cause: metrics-collector.md workflow has outdated lock file
• Action: Issue created for enabling metrics collection
• Priority: P0

Issue 2: 10 Workflows with Outdated Lock Files (7.8%)

• Status: Source .md files modified after .lock.yml compilation
• Impact: Runtime behavior may not match source code
• Affected Workflows:
  • smoke-copilot-playwright.md
  • go-fan.md
  • stale-repo-identifier.md
  • duplicate-code-detector.md
  • copilot-pr-nlp-analysis.md
  • smoke-srt.md
  • github-mcp-structural-analysis.md
  • metrics-collector.md ⚠️ Critical
  • incident-response.md
  • layout-spec-maintainer.md
• Action: Issue created for recompilation
• Priority: P0

Structural Health Analysis ✅

Since execution metrics are unavailable, this assessment focuses on structural health:

Compilation Coverage

• ✅ 100% coverage: All 128 workflows have .lock.yml files
• ⚠️ 7.8% outdated: 10 workflows need recompilation

Engine Distribution

Engine	Count	Percentage	Status
Copilot	69	53.9%	✅ Healthy diversity
Claude	25	19.5%	✅ Good alternative
Codex	7	5.5%	✅ Specialized use
Other	27	21.1%	⚠️ Needs classification

Analysis: Healthy distribution prevents single point of failure. Copilot as primary engine is appropriate for GitHub integration.

Workflow Categories

Category	Count	Notes
Campaign Workflows	2	Campaign orchestration
Smoke Tests	10	Testing infrastructure
Daily Scheduled	17	Regular maintenance
Weekly Scheduled	1	Long-term analysis
Hourly Scheduled	1	High-frequency monitoring
Event-Triggered	~97	Majority of workflows

Analysis: Good balance of scheduled vs. event-triggered workflows. Scheduling spread reduces resource contention.

Tool Usage

Tool	Workflows	Coverage	Status
GitHub MCP	94	73%	✅ Excellent adoption
Playwright	11	9%	✅ Appropriate for UI testing
Fetch	8	6%	✅ Web content retrieval

Analysis: Heavy GitHub MCP usage is expected and healthy for repository operations.

Systemic Patterns

Positive Indicators ✅

1. Complete compilation coverage: All workflows have lock files
2. Strong naming conventions: Clear categorization (daily-, smoke-, etc.)
3. Engine diversity: Multiple engines prevent vendor lock-in
4. Standardized tooling: Widespread GitHub MCP adoption
5. No orphaned lock files: Clean 1:1 mapping between source and compiled files

Areas of Concern ⚠️

1. Meta-monitoring gap: Metrics collector itself is outdated
2. No execution visibility: Cannot assess runtime health
3. Missing metrics infrastructure: Need 7 days of data for trends
4. Safe outputs visibility: Frontmatter declarations appear missing

Data Limitations 🔴

Current Analysis Limited By:

• ❌ No workflow execution metrics
• ❌ No failure rate data
• ❌ No runtime performance data
• ❌ No error pattern analysis
• ❌ Cannot calculate MTBF
• ❌ Cannot identify failing workflows

Reason: Metrics Collector workflow is outdated and metrics storage not populated.

Impact: This assessment can only evaluate structural health (compilation, configuration, categorization). Runtime health monitoring requires metrics data.

Recommendations

Immediate Actions (P0)

1. ✅ Recompile outdated workflows - Issue created
2. ✅ Enable metrics collection - Issue created
3. ⏳ Verify metrics collection - Pending workflow fix
4. ⏳ Wait for baseline data - Need 7 days of metrics

High Priority (P1)

1. Establish monitoring alerts - Set up notifications for workflow failures
2. Document workflow dependencies - Map inter-workflow relationships
3. Verify safe outputs usage - Deep dive into workflow bodies

Medium Priority (P2)

1. Analyze execution patterns - Once metrics available
2. Optimize scheduling - Prevent resource contention
3. Review smoke test coverage - Ensure critical paths tested

Low Priority (P3)

1. Standardize frontmatter - Consistent metadata across workflows
2. Add workflow descriptions - Improve discoverability
3. Document engine selection - Guidelines for choosing engines

Actions Taken This Run

• ✅ Scanned 128 executable workflows
• ✅ Verified 100% compilation coverage
• ✅ Identified 10 outdated lock files
• ✅ Created 2 P0 issues for critical problems
• ✅ Saved analysis to shared repo memory
• ✅ Created coordination alerts for other meta-orchestrators

Trends

• Overall health score: Unable to calculate (no metrics data)
• Compilation health: 92.2% (118/128 up-to-date)
• New failures this week: Unknown (no metrics)
• Fixed issues this week: Unknown (no metrics)
• Average success rate: Unknown (no metrics)

Next Steps

1. ⏳ Monitor recompilation issue resolution
2. ⏳ Monitor metrics collection enablement
3. ⏳ Wait 7 days for metrics baseline
4. 🔄 Re-run comprehensive health analysis with execution data
5. 🔄 Establish ongoing monitoring and alerting

Success Metrics Target

Once metrics are available, track:

• Overall workflow health score > 80/100
• Workflow success rate > 90%
• Mean time between failures (MTBF) > 7 days
• Outdated lock files < 5%
• Failed workflows detected within 24 hours

---

Last updated: 2026-01-04T02:59:53Z
Next check: After metrics collection enabled (7 days minimum for baseline)
Dashboard maintained by: Workflow Health Manager
Shared memory: /tmp/gh-aw/repo-memory/default/workflow-health-latest.md

AI generated by Workflow Health Manager - Meta-Orchestrator

[github-actions]

This issue was automatically closed because it expired on 2026-01-05T03:04:07.324Z.