Agent Performance Report - Week of December 23-29, 2024

[github-actions[bot]]

Executive Summary

• Agents analyzed: 174 workflows
• Engine distribution: Copilot (majority), Claude, Codex
• Safe output adoption: 133 workflows (76%) use safe outputs
• Slash command agents: 15 interactive workflows
• Ecosystem maturity: High complexity with meta-orchestrators managing performance

Performance Analysis Framework

Note: This analysis is based on repository structure and workflow configurations. Direct GitHub API access is unavailable (gh CLI not authenticated), limiting historical metrics analysis. Future runs should leverage the Metrics Collector workflow data stored in shared memory.

Workflow Categorization

Meta-Orchestrators (3 workflows) 🎯

• agent-performance-analyzer - This workflow (current)
• campaign-manager - Campaign health and coordination
• workflow-health-manager - Workflow monitoring and diagnostics

Assessment: Strategic layer providing ecosystem-wide insights and coordination.

Campaign Workflows (1 identified)

• go-file-size-reduction-project64.campaign.g.md - Generated campaign workflow

Coverage Gap: Limited campaign workflow generation despite Campaign Manager orchestrator presence.

Daily Monitoring (20+ workflows)

• daily-code-metrics, daily-doc-updater, daily-file-diet
• daily-issues-report, daily-malicious-code-scan
• daily-news, daily-performance-summary
• daily-repo-chronicle, daily-team-status
• daily-workflow-updater, daily-copilot-token-report
• daily-multi-device-docs-tester, daily-firewall-report

Pattern: Consistent daily execution for ongoing maintenance and reporting.

Slash Command Agents (15 workflows) 🎤

Interactive agents triggered by user commands:

• archie - Mermaid diagram generation for issue relationships
• brave - Web search integration
• cloclo, craft, plan, q - Various utilities
• mergefest, pdf-summary, poem-bot - Specialized tools
• pr-nitpick-reviewer, grumpy-reviewer - Code review assistants
• scout, tidy, unbloat-docs - Documentation and cleanup

Quality Indicators:

• High user engagement (reaction-based triggers)
• Clear, focused purposes
• Interactive feedback loop

Health & Monitoring Workflows (15+ workflows)

• audit-workflows, safe-output-health, workflow-health-manager
• ci-coach, ci-doctor, smoke-detector
• breaking-change-checker, security-compliance
• blog-auditor, org-health-report

Assessment: Comprehensive coverage of system health monitoring.

Analysis & Reporting (20+ workflows)

• copilot-agent-analysis, copilot-pr-merged-report
• copilot-session-insights, copilot-pr-nlp-analysis
• static-analysis-report, deep-report
• metrics-collector, portfolio-analyst
• duplicate-code-detector, go-pattern-detector

Strength: Data-driven insights across multiple dimensions.

Developer Tools (15+ workflows)

• developer-docs-consolidator, technical-doc-writer
• glossary-maintainer, slide-deck-maintainer
• issue-arborist, issue-classifier, issue-triage-agent
• workflow-generator, spec-kit-executor
• typist, dictation-prompt, terminal-stylist

Assessment: Strong developer experience focus.

Smoke Tests (9 workflows)

• smoke-copilot, smoke-claude, smoke-codex
• smoke-copilot-playwright, smoke-copilot-safe-inputs
• smoke-copilot-no-firewall, smoke-codex-firewall
• smoke-detector, smoke-srt, smoke-srt-custom-config

Purpose: Engine and feature validation across configurations.

Engine Distribution Analysis

Copilot Engine (Dominant)

Workflows: 80+ (majority of active workflows)

Strengths:

• Primary engine for most production workflows
• Strong GitHub API integration
• Reliable performance for standard tasks

Use Cases:

• Campaign management and orchestration
• Issue/PR management and triage
• Documentation generation and maintenance
• Daily monitoring and reporting

Claude Engine (Specialized)

Workflows: 20+ specialized workflows

Identified workflows:

• audit-workflows, safe-output-health
• daily-code-metrics, example-workflow-analyzer
• semantic-function-refactor, go-logger
• copilot-session-insights, copilot-agent-analysis
• static-analysis-report, security-fix-pr
• developer-docs-consolidator, lockfile-stats
• typist, scout, go-fan
• cli-version-checker, schema-consistency-checker
• github-mcp-structural-analysis, github-mcp-tools-report
• prompt-clustering-analysis, instructions-janitor

Pattern: Claude used for complex analysis, security, and refactoring tasks.

Strengths:

• Deep code analysis
• Security and compliance review
• Complex reasoning tasks
• Structural analysis

Codex Engine (Legacy)

Workflows: 5-7 workflows

Identified workflows:

• smoke-codex, smoke-codex-firewall
• deep-report, issue-arborist
• daily-performance-summary, daily-issues-report
• duplicate-code-detector, close-old-discussions

Assessment: Legacy engine still in use for specific tasks, may be candidate for migration to newer engines.

Tool Usage Patterns

Safe Outputs (76% adoption)

133 of 174 workflows use safe-outputs configuration.

Categories:

• create-issue (most common)
• create-discussion (reporting workflows)
• add-comment (interactive agents)
• create-pull-request (fix workflows)
• messages (user feedback)

High adoption rate indicates: Mature ecosystem with proper GitHub API integration.

GitHub API Tool (Universal)

Near 100% usage - Core functionality for all agents.

Toolsets observed:

• [default] - Standard issue/PR operations
• [actions] - Workflow run analysis
• [repos] - Repository metadata access

Playwright Integration (Browser Automation)

Limited but strategic usage:

• blog-auditor (website verification)
• daily-multi-device-docs-tester (documentation testing)
• smoke-copilot-playwright (smoke tests)

Assessment: Appropriately used for web-based validation tasks.

Repo Memory Tool (State Management)

Strategic usage in meta-orchestrators:

• agent-performance-analyzer
• campaign-manager
• workflow-health-manager
• audit-workflows

Purpose: Persistent state for trend analysis and coordination.

Key Findings

Strengths ✅

1. Comprehensive Coverage

   • 174 workflows covering development lifecycle
   • Meta-orchestrators provide strategic oversight
   • Daily monitoring ensures consistent maintenance

2. Engine Diversity

   • Appropriate engine selection for task types
   • Claude for complex analysis
   • Copilot for standard operations
   • Codex for legacy support

3. High Safe Output Adoption

   • 76% of workflows use safe outputs
   • Proper GitHub API integration
   • Structured communication patterns

4. Interactive Capabilities

   • 15 slash command agents
   • User-driven workflows
   • Flexible invocation patterns

5. Quality Infrastructure

   • Comprehensive smoke tests
   • Health monitoring workflows
   • Audit and compliance checks

Areas for Improvement 📉

1. Limited Metrics Visibility

   • Issue: No access to historical performance data in this run
   • Impact: Cannot assess actual effectiveness, task completion rates, or quality scores
   • Recommendation: Ensure Metrics Collector workflow runs daily and populates shared memory
   • Action: Verify /tmp/gh-aw/repo-memory-default/memory/meta-orchestrators/metrics/ exists and contains data

2. Campaign Workflow Gap

   • Finding: Only 1 campaign workflow identified despite Campaign Manager orchestrator
   • Impact: Campaign generation may not be active or campaigns are short-lived
   • Recommendation: Review Campaign Manager effectiveness and campaign lifecycle

3. Codex Engine Legacy

   • Finding: 5-7 workflows still using Codex engine
   • Impact: May miss improvements from newer engines
   • Recommendation: Evaluate migration path to Claude/Copilot for these workflows

4. No Behavioral Pattern Data

   • Issue: Cannot analyze over/under-creation, duplication, or scope creep without API access
   • Impact: Missing critical quality indicators
   • Recommendation: Future runs should use Metrics Collector data and GitHub MCP server

5. Collaboration Analysis Missing

   • Issue: Cannot map agent interactions without issue/PR relationship data
   • Impact: Unknown if agents are building on each other's work effectively
   • Recommendation: Use GitHub MCP server to analyze cross-agent collaboration patterns

Workflow Quality Assessment (Configuration-Based)

Excellent Configuration Quality (5/5) 🏆

Criteria: Well-documented, proper safe outputs, appropriate tools, clear purpose

• agent-performance-analyzer - This workflow
• campaign-manager - Strategic orchestration
• workflow-health-manager - Comprehensive monitoring
• audit-workflows - Detailed audit process
• brave - Clean slash command implementation
• archie - Well-scoped diagram generation

Strong Configuration (4/5)

Most daily monitoring workflows:

• Clear schedules
• Appropriate permissions
• Safe output configurations
• Focused purposes

Needs Configuration Review (3/5 or lower)

Workflows with missing/incomplete engine specs:

• commit-changes-analyzer (engine: field empty)
• cloclo (engine: field empty)
• unbloat-docs (engine: field empty)
• glossary-maintainer (engine: field empty)
• issue-template-optimizer (engine: field empty)
• technical-doc-writer (engine: field empty)
• changeset (engine: field empty)
• daily-multi-device-docs-tester (engine: field empty)
• smoke-copilot-playwright (engine: field empty)
• smoke-claude (engine: field empty)
• daily-fact (engine: field empty)
• poem-bot (engine: field empty)
• daily-choice-test (engine: field empty)
• ai-moderator (multiple engine: entries)

Recommendation: Audit and fix workflows with incomplete engine configurations.

Recommendations

High Priority

1. Fix Metrics Collection Infrastructure ⚠️

   • Issue: Cannot access performance data for meaningful analysis
   • Action: Verify Metrics Collector workflow execution
   • Expected Impact: Enable data-driven performance assessment
   • Estimated Effort: 1-2 hours

2. Repair Empty Engine Configurations

   • Issue: 15+ workflows have missing/empty engine specifications
   • Action: Audit and set appropriate engine for each workflow
   • Expected Impact: Ensure consistent execution
   • Estimated Effort: 2-3 hours

3. Configure GitHub MCP Server for Agent Performance Analyzer

   • Issue: No GitHub API access limits analysis capabilities
   • Action: Add GitHub MCP tool configuration to this workflow
   • Expected Impact: Enable comprehensive performance analysis
   • Estimated Effort: 30 minutes

Medium Priority

4. Evaluate Codex Engine Migration

   • Issue: 5-7 workflows using legacy Codex engine
   • Action: Test migration to Claude/Copilot for better performance
   • Expected Impact: Improved analysis quality
   • Estimated Effort: 4-6 hours

5. Campaign Workflow Analysis

   • Issue: Limited campaign workflow presence
   • Action: Review Campaign Manager effectiveness and workflow generation
   • Expected Impact: Better understanding of campaign lifecycle
   • Estimated Effort: 2-3 hours

6. Create Workflow Quality Standards

   • Issue: Inconsistent configuration patterns
   • Action: Document and enforce configuration standards
   • Expected Impact: Improved workflow maintainability
   • Estimated Effort: 3-4 hours

Low Priority

7. Smoke Test Coverage Expansion

   • Current: 9 smoke tests
   • Recommendation: Add smoke tests for new features (MCP servers, repo-memory, etc.)
   • Expected Impact: Earlier detection of regressions

8. Slash Command Agent Documentation

   • Create user guide for 15 slash command agents
   • Include examples and best practices
   • Expected Impact: Improved user engagement

Coverage Analysis

Well-Covered Areas ✅

• Campaign orchestration - Meta-orchestrator + generator
• Code health - Multiple daily monitors + analysis workflows
• Documentation - 10+ documentation-focused workflows
• Issue/PR management - Triage, classification, review workflows
• Security - Compliance, malicious code scanning, security fixes
• Testing - Comprehensive smoke tests, multi-device testing

Coverage Gaps 🔍

• Performance optimization - Limited automated performance improvement
• User experience - Few workflows focused on UX improvements
• Dependency management - Only dependabot-go-checker identified
• Release automation - Limited release workflow coverage
• Incident response - Only 1 workflow (incident-response)

Potential Redundancy ⚠️

• Multiple Copilot analysis workflows (5+) - copilot-agent-analysis, copilot-pr-merged-report, copilot-session-insights, copilot-pr-nlp-analysis, copilot-pr-prompt-analysis
  • Recommendation: Consider consolidation or clear differentiation
• Documentation maintenance (4+) - developer-docs-consolidator, technical-doc-writer, glossary-maintainer, slide-deck-maintainer
  • Assessment: May be appropriate if targeting different doc types

Ecosystem Health

Diversity Score: 8/10

• Good distribution across workflow types
• Multiple engines utilized appropriately
• Mix of scheduled, event-driven, and interactive workflows

Maturity Score: 7/10

• Strong infrastructure (meta-orchestrators, metrics, health checks)
• High safe output adoption
• Some configuration inconsistencies need addressing

Scalability Score: 6/10

• 174 workflows is substantial
• Need better metrics infrastructure for performance tracking
• Potential redundancy areas to investigate

Innovation Score: 9/10

• Creative slash command agents
• Meta-orchestration layer
• Browser automation integration
• Sophisticated analysis workflows

Trends (Configuration-Based Analysis)

Cannot provide time-series trends without historical metrics data.

Expected metrics once data is available:

• Overall agent quality score
• Average effectiveness rate
• Output volume trends
• PR merge rate trends
• Resource efficiency metrics

Actions Needed for Next Run

Prerequisites for Effective Analysis

1. Ensure Metrics Collector runs daily

   • Verify workflow execution
   • Check data in /tmp/gh-aw/repo-memory-default/memory/meta-orchestrators/metrics/
   • Validate JSON structure

2. Add GitHub MCP Server to this workflow

   • Enable comprehensive GitHub API access
   • Required for agent output quality analysis
   • Enable behavioral pattern detection

3. Fix workflows with empty engine configurations

   • Prevents execution failures
   • Ensures consistent behavior

Analysis Enhancements for Future Runs

4. Implement output quality scoring

   • Sample recent issues/PRs from agents
   • Rate clarity, accuracy, completeness
   • Generate quality scores

5. Track task completion rates

   • Analyze issue resolution rates
   • Measure PR merge rates
   • Calculate effectiveness scores

6. Detect behavioral patterns

   • Identify over/under-creation
   • Find duplication patterns
   • Flag scope creep

Next Steps

1. Immediate: Fix empty engine configurations in 15+ workflows
2. This week: Configure GitHub MCP server for this workflow
3. This week: Verify Metrics Collector workflow execution and data availability
4. Next week: Re-run with full metrics access for comprehensive analysis
5. Ongoing: Monitor Codex engine workflows for migration opportunities

Conclusion

The gh-aw repository demonstrates a sophisticated and mature agentic workflow ecosystem with 174 workflows covering the development lifecycle. The presence of meta-orchestrators, comprehensive health monitoring, and high safe output adoption (76%) indicates strong architectural foundations.

Critical Gap: This analysis is limited by lack of metrics data and GitHub API access. The workflow requires GitHub MCP server configuration to provide meaningful performance assessment, quality scores, and behavioral pattern analysis.

Key Strengths:

• Strategic meta-orchestration layer
• Diverse workflow types and purposes
• High safe output adoption
• Engine diversity for appropriate tasks

Immediate Improvements Needed:

1. Fix 15+ workflows with empty engine configurations
2. Enable metrics collection infrastructure
3. Add GitHub MCP server to agent-performance-analyzer
4. Establish baseline performance metrics

Once these prerequisites are met, future runs can provide:

• Detailed quality scores (0-100)
• Agent effectiveness rankings
• Behavioral pattern analysis
• Collaboration mapping
• Data-driven recommendations

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Analysis Period: December 23-29, 2024 (configuration-based analysis)
Next Report: After metrics infrastructure is operational
Workflow: Agent Performance Analyzer
Limitations: No GitHub API access, no historical metrics data available

AI generated by Agent Performance Analyzer - Meta-Orchestrator