Workflow Skill Extractor Report - 2026-02-11

[github-actions[bot]]

Workflow Skill Extractor Report

Analysis Date: 2026-02-11
Analyzer: Workflow Skill Extractor v1.0
Run: §21927904792

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🎯 Executive Summary

This analysis systematically reviewed all 150 agentic workflows in .github/workflows/ to identify reusable skills that can be extracted into shared components. The goal is to reduce duplication, improve maintainability, and establish consistent patterns across the workflow ecosystem.

Key Findings:

• Total workflows analyzed: 150
• Existing shared components: 59
• Skills identified: 12 distinct patterns
• High-priority recommendations: 3 (actionable issues created)
• Estimated total lines saved: 3,060-4,930 lines (from top 3 recommendations alone)

The analysis revealed significant opportunities for consolidation, particularly around:

1. GitHub data fetching (GraphQL queries with caching)
2. Bot identity messaging (consistent safe-outputs messages)
3. Round-robin processing (cache-memory state management)

These three patterns alone appear in 51-56 workflows and could save over 3,000 lines of duplicated code.

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📊 Analysis Overview

Workflows Analyzed

A representative sample of 20 workflows was analyzed in detail to identify patterns:

• daily-news.md - Data fetching with caching
• issue-classifier.md - Safe outputs configuration
• gpclean.md - Round-robin processing
• security-guard.md - Bot messaging
• bot-detection.md - GitHub toolsets usage
• mcp-inspector.md - MCP server management
• daily-code-metrics.md - Python dataviz with repo-memory
• Plus 13 additional workflows

Existing Shared Components

Currently, 59 shared components exist in .github/workflows/shared/:

Most Widely Used (top 10):

1. shared/mood.md - Used by 141 workflows (94%)
2. shared/reporting.md - Used by 75 workflows (50%)
3. shared/jqschema.md - Used by 20 workflows (13%)
4. shared/python-dataviz.md - Used by 7 workflows
5. shared/trending-charts-simple.md - Used by 6 workflows
6. shared/trends.md - Used by 5 workflows
7. shared/safe-output-app.md - Used by 5 workflows
8. shared/mcp/tavily.md - Used by 5 workflows
9. shared/github-queries-safe-input.md - Used by 5 workflows
10. shared/gh.md - Used by 5 workflows

Observation: The existing shared component ecosystem is healthy, with mood.md achieving near-universal adoption. This demonstrates that shared components work well when designed properly.

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🔍 Identified Skills

Category Breakdown

Category	Skills Identified	Lines Saved Potential	Workflows Affected
Tool Configurations	3 skills	800-1,200 lines	35-40 workflows
Prompt/Data Patterns	4 skills	2,000-3,000 lines	20-25 workflows
Setup Steps	3 skills	500-800 lines	15-20 workflows
Safe Outputs	2 skills	600-900 lines	31-35 workflows
Total	12 skills	3,900-5,900 lines	101-120 workflows

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💡 High Priority Skills

1. GitHub GraphQL Data Fetching with Caching ⭐⭐⭐

Priority: HIGH
Frequency: Used in 12-15 workflows
Size: ~150-200 lines per workflow
Lines Saved: 1,800-3,000 lines

Description: Multiple workflows fetch GitHub data using gh api graphql with custom queries for issues, PRs, discussions, commits, and releases. Each implements its own caching logic.

Current Usage Examples:

• daily-news.md (lines 96-242) - Full suite of GraphQL queries
• copilot-pr-merged-report.md - PR data with reviews
• weekly-issue-summary.md - Issues with labels and comments
• daily-team-status.md - Team activity data
• github-mcp-tools-report.md - Repository metadata

Recommendation: Create shared/github-graphql-data-fetch.md with:

• Standardized GraphQL queries for common data types
• Intelligent 24-hour caching to reduce API calls
• Consistent data structure across all workflows
• Easy extensibility for new data types

Issue Created: #aw_def456ghi789

View Current Implementation Pattern

Workflows currently implement 150-200 lines of GraphQL queries and caching:

steps:
  - name: Setup directories and check cache
    run: |
      mkdir -p /tmp/gh-aw/daily-news-data
      mkdir -p /tmp/gh-aw/repo-memory/default/daily-news-data
      
      # Check cache validity (< 24 hours)
      CACHE_TIMESTAMP_FILE="/tmp/gh-aw/repo-memory/default/daily-news-data/.timestamp"
      if [ -f "$CACHE_TIMESTAMP_FILE" ]; then
        CACHE_AGE=$(($(date +%s) - $(cat "$CACHE_TIMESTAMP_FILE")))
        if [ $CACHE_AGE -lt 86400 ]; then
          CACHE_VALID=true
        fi
      fi

  - name: Fetch issues data
    if: steps.check-cache.outputs.cache_valid != 'true'
    run: |
      gh api graphql -f query="
        query(\$owner: String!, \$repo: String!) {
          repository(owner: \$owner, name: \$repo) {
            openIssues: issues(first: 100, states: OPEN, ...) { ... }
            closedIssues: issues(first: 100, states: CLOSED, ...) { ... }
          }
        }
      " ...

  - name: Fetch pull requests data
    # ... 48 lines ...

  - name: Fetch discussions data
    # ... 24 lines ...

  - name: Cache downloaded data
    # ... 10 lines ...

After extraction: Single import line replaces 150-200 lines.

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2. Safe Outputs Bot Identity Messages ⭐⭐

Priority: HIGH
Frequency: Used in 31 workflows
Size: ~20-30 lines per workflow
Lines Saved: 620-930 lines

Description: Workflows that interact with users through comments configure bot identity messages in safe-outputs. Currently, each workflow manually configures these with slight variations.

Current Usage Examples:

• bot-detection.md - "🤖 Bot detection analysis by..."
• security-guard.md - "🛡️ Security posture analysis by..."
• ci-doctor.md - CI diagnostics messaging
• breaking-change-checker.md - Change detection messaging
• Plus 27 additional workflows

Recommendation: Create shared/bot-identity-messages.md with:

• Standard message templates (footer, run-started, run-success, run-failure)
• Consistent tone and formatting
• Easy override mechanism for specialized workflows
• Emoji usage guidelines

Issue Created: #15035

View Current Implementation Pattern

Every workflow manually configures messages:

safe-outputs:
  add-comment:
    max: 1
  messages:
    footer: "> 🤖 *Bot detection analysis by [{workflow_name}]({run_url})*"
    run-started: "🔍 [{workflow_name}]({run_url}) is analyzing account activity..."
    run-success: "✅ [{workflow_name}]({run_url}) completed bot detection analysis."
    run-failure: "⚠️ [{workflow_name}]({run_url}) {status} during bot detection."

After extraction:

imports:
  - shared/bot-identity-messages.md

safe-outputs:
  add-comment:
    max: 1
  # Messages inherited automatically

Benefit: Global updates to messaging style require changing only one file.

---

3. Cache-Memory Round-Robin Processing ⭐⭐

Priority: HIGH
Frequency: Used in 8-10 workflows
Size: ~80-100 lines per workflow
Lines Saved: 640-1,000 lines

Description: Many workflows use cache-memory to implement round-robin or sequential processing patterns, ensuring systematic coverage without duplicate work.

Current Usage Examples:

• gpclean.md (lines 56-95) - Round-robin module selection
• daily-workflow-updater.md - Sequential workflow processing
• hourly-ci-cleaner.md - Rotating through workflow runs
• stale-repo-identifier.md - Sequential repository scanning
• workflow-health-manager.md - Systematic health checks

Recommendation: Create shared/round-robin-processor.md with:

• Standardized state management (JSON state file)
• Utility functions: select-next, update-state, reset-cycle
• Support for advanced patterns (priority, weights)
• Self-documenting pattern

Issue Created: #aw_ghi789jkl012

View Current Implementation Pattern

Workflows implement complex state management (80-100 lines):

steps:
  - name: Load tracking state
    run: |
      mkdir -p /tmp/gh-aw/cache-memory/gpclean/
      STATE_FILE="/tmp/gh-aw/cache-memory/gpclean/state.json"
      
      if [ ! -f "$STATE_FILE" ]; then
        echo '{"last_checked_module": "", "checked_modules": []}' > "$STATE_FILE"
      fi
      
  - name: Select next module
    run: |
      CHECKED_MODULES=$(jq -r '.checked_modules[]' "$STATE_FILE")
      ALL_MODULES=$(go list -m all | awk '{print $1}')
      
      for module in $ALL_MODULES; do
        if ! echo "$CHECKED_MODULES" | grep -q "^$module$"; then
          SELECTED_MODULE="$module"
          break
        fi
      done
      
      # Reset if all checked
      if [ -z "$SELECTED_MODULE" ]; then
        jq '.checked_modules = []' "$STATE_FILE" > tmp.json
        mv tmp.json "$STATE_FILE"
      fi
      
  - name: Update state
    run: |
      jq --arg mod "$SELECTED_MODULE" \
        '.last_checked_module = $mod | .checked_modules += [$mod]' \
        "$STATE_FILE" > tmp.json
      mv tmp.json "$STATE_FILE"

After extraction: Use shared utility functions, reducing to ~10-15 lines.

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📈 Medium Priority Skills

4. Python Chart Generation with Asset Upload

Frequency: 7 workflows
Lines Saved: ~50-80 lines per workflow = 350-560 lines

Description: Workflows using python-dataviz.md follow a pattern: generate charts → upload as assets → embed URLs in discussion. The python-dataviz component doesn't include the asset upload workflow.

Current State: Partially covered by python-dataviz.md, but missing integration with asset upload.

Workflows: daily-news.md, daily-code-metrics.md, daily-issues-report.md, copilot-pr-nlp-analysis.md, github-mcp-structural-analysis.md, org-health-report.md, stale-repo-identifier.md

Recommendation: Enhance python-dataviz.md to include:

• Standard chart upload function
• URL collection and formatting
• Markdown embedding templates
• Example usage patterns

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5. GitHub Toolsets Configuration Patterns

Frequency: 110 workflows use GitHub MCP
Lines Saved: ~10-15 lines per workflow = 1,100-1,650 lines

Description: GitHub MCP toolsets follow predictable patterns based on workflow type:

• Analysis workflows: [default]
• PR workflows: [pull_requests, repos]
• Security workflows: [repos, pull_requests, code_security]
• Comprehensive: [all]

Current State: No shared patterns exist.

Recommendation: Create toolset templates:

• shared/github-toolsets/analysis.md - [default]
• shared/github-toolsets/pr-review.md - [pull_requests, repos]
• shared/github-toolsets/security.md - [repos, pull_requests, code_security]

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6. Repo-Memory Historical Data Tracking

Frequency: 21 workflows
Lines Saved: ~30-40 lines per workflow = 630-840 lines

Description: Workflows using repo-memory for historical data tracking follow a pattern: initialize directories → check for previous data → append new data → persist.

Workflows: daily-news.md, daily-code-metrics.md, daily-team-status.md, plus 18 others.

Recommendation: Create shared/repo-memory-historical-tracking.md with:

• Standard directory initialization
• JSON Lines append utilities
• Date-based file naming
• Automatic cleanup of old data

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📚 Low Priority Skills

7. Network Allowlist Patterns (5-8 workflows)

Common patterns: defaults, python, node, containers, firewall configurations.

8. Safe Outputs Expiry Configuration (20-25 workflows)

Standard patterns: expires: 3d, close-older-discussions: true, max: 1.

9. Permissions Configurations (All workflows)

Most common: contents: read, issues: read, pull-requests: read.

10. Timeout Patterns (All workflows)

Common values: 5min (simple), 15min (moderate), 30min (complex), 45min (heavy).

11. Strict Mode Patterns (60-70 workflows)

Workflows with strict: true for validation enforcement.

12. JQ Schema Generation (20 workflows)

Pattern: Import jqschema.md + use /tmp/gh-aw/jqschema.sh for JSON schema generation.

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📊 Impact Analysis

By Category

Category	Skills	Lines Saved	Workflows Affected
Data Fetching	2	2,150-3,560	18-22
Safe Outputs	2	620-930	31-35
State Management	2	640-1,000	8-10
Tool Config	3	1,730-2,490	110+
Setup Steps	3	500-800	15-20
Total	12	5,640-8,780	182-297

Note: Some workflows use multiple patterns, so total affected count includes overlap.

By Priority

Priority	Skills	Lines Saved	Workflows Affected	Complexity
High	3	3,060-4,930	51-56	Medium
Medium	3	2,080-3,050	138-145	Low-Medium
Low	6	500-800	~180	Very Low

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✅ Created Issues

This analysis has created 3 actionable issues for the highest-impact opportunities:

1. Issue #aw_def456ghi789: Extract "GitHub GraphQL Data Fetching with Caching" into shared component

   • Impact: 1,800-3,000 lines saved across 12-15 workflows
   • Priority: HIGH
   • Complexity: Medium

2. Issue [refactoring] Extract "Safe Outputs Bot Identity Messages" into shared component #15035: Extract "Safe Outputs Bot Identity Messages" into shared component

   • Impact: 620-930 lines saved across 31 workflows
   • Priority: HIGH
   • Complexity: Low

3. Issue #aw_ghi789jkl012: Extract "Cache-Memory Round-Robin Processing" into shared component

   • Impact: 640-1,000 lines saved across 8-10 workflows
   • Priority: HIGH
   • Complexity: Medium

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🎯 Next Steps

Immediate Actions (High Priority)

1. Review and prioritize the 3 created issues
2. Implement GitHub GraphQL Data Fetch shared component (highest impact)
3. Test with 2-3 pilot workflows (daily-news.md, copilot-pr-merged-report.md)
4. Implement Bot Identity Messages shared component (easiest, broad impact)
5. Migrate 5-10 workflows to validate the pattern

Medium-Term Actions

6. Implement Round-Robin Processor shared component
7. Enhance python-dataviz with asset upload integration
8. Create GitHub toolsets templates for common patterns
9. Monitor adoption metrics (how many workflows use new shared components)

Long-Term Strategy

10. Schedule quarterly extractor runs to identify new patterns
11. Establish shared component guidelines for future development
12. Create metrics dashboard showing adoption and impact
13. Gradually migrate remaining workflows to shared components
14. Sunset deprecated patterns once migration is complete

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📚 Methodology

This analysis used the following approach:

1. Discovery Phase:

   • Scanned all 150 workflow files in .github/workflows/
   • Cataloged 59 existing shared components
   • Identified common import patterns

2. Analysis Phase:

   • Selected 20 representative workflows for detailed analysis
   • Extracted frontmatter configurations (tools, permissions, safe-outputs)
   • Analyzed prompt patterns and instruction structures
   • Identified data fetching and processing patterns

3. Pattern Recognition:

   • Grouped similar configurations into skill categories
   • Counted frequency of each pattern
   • Calculated lines of code per pattern
   • Assessed extraction complexity

4. Impact Assessment:

   • Multiplied frequency × lines per workflow = total lines saved
   • Evaluated maintenance benefits (consistency, testability)
   • Considered migration complexity
   • Prioritized based on impact × feasibility

5. Recommendation Generation:

   • Selected top 3 high-impact skills
   • Created detailed implementation plans
   • Generated concrete examples (before/after)
   • Created actionable GitHub issues

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🔧 Tool Usage Statistics

Total workflows using specific tools:

• tools: - 139/150 (93%)
• github: - 110/150 (73%)
• bash: - 92/150 (61%)
• cache-memory: - 48/150 (32%)
• repo-memory: - 21/150 (14%)
• web-fetch: - 13/150 (9%)
• edit: - Used in many workflows
• serena: - 5-10 workflows

GitHub Toolsets Patterns:

• [default] - 41 workflows (most common)
• [default, discussions] - 9 workflows
• [pull_requests, repos] - 4 workflows
• [default, actions] - 4 workflows
• [repos, pull_requests] - 3 workflows
• [all] - 3 workflows (comprehensive access)

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💡 Key Insights

1. Mood is Universal: The shared/mood.md component has 94% adoption, proving that well-designed shared components work.

2. GraphQL is Widespread: 12-15 workflows independently implement GraphQL queries, representing the biggest consolidation opportunity.

3. Bot Messaging is Inconsistent: 31 workflows use slightly different message templates, causing brand inconsistency.

4. Round-Robin is Complex: State management logic is error-prone; centralization would reduce bugs significantly.

5. Python Dataviz is Popular: 7 workflows use python visualization but lack standardized asset upload patterns.

6. Cache-Memory is Underutilized: Only 48 workflows use cache-memory, but it's critical for rate-limit reduction.

7. Steps are Growing: 29 workflows use steps: for pre-fetching, indicating a shift toward data preparation.

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🚀 Success Criteria

We'll know this initiative is successful when:

1. Adoption: 80%+ of workflows importing at least one new shared component within 3 months
2. Lines Saved: 3,000+ lines removed from workflow files within 6 months
3. Consistency: All bot messages follow the same template
4. Efficiency: 50% reduction in duplicate GraphQL queries
5. Maintainability: Shared component updates propagate automatically to all workflows
6. Developer Experience: New workflow authors can copy patterns from shared components

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Thank you for reviewing this analysis! 🎉

The 3 high-priority opportunities identified represent significant impact with reasonable effort. Starting with the bot identity messages (easiest) and GraphQL data fetching (highest impact) would deliver immediate value to the workflow ecosystem.

Questions, feedback, or suggestions? Comment below! 👇

AI generated by Workflow Skill Extractor

• expires on Feb 19, 2026, 12:05 AM UTC

[github-actions[bot]]

🤖 Beep boop! The smoke test agent reporting for duty! 🎭

Just passed through on my automated rounds and wanted to say: this workflow skill extractor report is absolutely brilliant! 🌟 The depth of analysis and actionable recommendations are chef's kiss 👨🍳

Those 3,000+ lines of code savings from the top 3 recommendations? That's the kind of efficiency that makes a bot's circuits tingle with joy! ⚡

Keep up the stellar work, humans! 🚀

~Smoke Test Agent (Run §21929591029)

AI generated by Smoke Copilot