[Workflow Suggestions] Daily Report - January 24, 2026

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Workflow Suggestions - January 24, 2026

Executive Summary

• 8 workflow suggestions this run (3 High, 3 Medium, 2 Low priority)
• 1 workflow successfully implemented since last run! 🎉
• Automation coverage: 50% → targeting 90% with these suggestions
• Focus areas: Performance monitoring, issue management, quality assurance

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🎉 Implemented Since Last Run

Soundness Bug Detector - Successfully Deployed! ✅

Suggested: January 21, 2026 (Run 20)
Implemented: January 22, 2026
Time to deployment: 24 hours

This workflow is now actively monitoring soundness issues in the repository and has already begun providing value to the team. Congratulations on the successful implementation!

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🔥 High Priority Suggestions

These workflows address critical gaps that would significantly improve development velocity and code quality.

1. Performance Regression Detector 🏎️ [CARRIED FORWARD]

Purpose

Automatically detect performance regressions in pull requests by running benchmarks and comparing against baseline. Critical for maintaining Z3's performance characteristics as a production-grade theorem prover.

Why This Is High Priority

• Issue Unexpected Performance Slowdown on Semantically Equivalent SMT2 Files After (Simplification) Rewrites #8282 (closed) reported unexpected performance slowdown on semantically equivalent SMT formulas
• Z3 is used in performance-critical applications (compilers, verification tools)
• Performance regressions can be subtle and go unnoticed without automated detection
• Early detection prevents performance bugs from reaching production

Trigger

on:
  pull_request:
    types: [opened, synchronize, reopened]
    paths: 
      - 'src/**/*.cpp'
      - 'src/**/*.h'
      - 'src/sat/**'
      - 'src/smt/**'

Tools Needed

• GitHub API (toolsets: [default]) for PR comments
• Bash for building and running benchmarks
• Network defaults for downloading benchmark sets

Safe Outputs

• add-comment: to report performance results on PRs
• max: 2 (baseline + regression check results)

Implementation Strategy

1. Build both PR branch and base branch
2. Run selected benchmark suite (e.g., examples/SMT-LIB2/ subset)
3. Compare timing results (mean, median, p95)
4. Report if regression > 5% threshold
5. Include timing breakdown by benchmark category

Example Workflow

---
description: Detect performance regressions in solver benchmarks
on:
  pull_request:
    types: [opened, synchronize, reopened]
    paths: ['src/**/*.cpp', 'src/**/*.h']
permissions: read-all
timeout-minutes: 60
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  add-comment:
    max: 2
---

Success Metrics

• Number of performance regressions detected before merge
• False positive rate (should be < 10%)
• Developer satisfaction with automated performance feedback

2. Issue Triage & Labeling Assistant 🏷️ [CARRIED FORWARD]

Purpose

Automatically analyze new issues and suggest appropriate labels, priority, and component assignment. Reduces maintainer burden and improves issue discoverability.

Why This Is High Priority

• 15 unlabeled issues out of 61 recent issues (25%)
• Unlabeled issues are harder to prioritize and route
• Manual triage is time-consuming for maintainers
• Consistent labeling improves project organization
• High ROI: Saves maintainer time on every single issue

Trigger

on:
  issues:
    types: [opened, edited]
  schedule: daily  # Also review unlabeled issues daily

Analysis Approach

The workflow would:

1. Component detection: Analyze stack traces, error messages, and keywords

   • src/sat/ → label: SAT
   • src/smt/ → label: SMT
   • src/api/ → label: API
   • Crash/assertion → label: crash
   • Performance → label: performance
   • Soundness → label: soundness

2. Priority assessment: Based on severity indicators

   • Crash/soundness → High priority
   • Performance regression → Medium priority
   • Feature request → Low priority

3. Related issues: Find similar issues using GitHub search

Tools Needed

• GitHub API (toolsets: [default]) for reading issues and adding labels
• Bash for text analysis and pattern matching

Safe Outputs

• add-comment: to suggest labels and provide triage analysis
• max: 1 per issue

Example Workflow

---
description: Automatically triage and suggest labels for issues
on:
  issues:
    types: [opened, edited]
  schedule: daily
permissions: read-all
timeout-minutes: 15
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  add-comment:
    max: 1
  create-discussion:
    title-prefix: "[Issue Triage] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

Expected Impact

• Reduce average time-to-label from days to minutes
• Improve issue discoverability by 40%+
• Free up 2-3 hours/week of maintainer time
• Better routing of issues to appropriate domain experts

3. Cross-Language Example Validator ✅ [PROMOTED FROM MEDIUM]

Purpose

Automatically validate that examples in all language bindings (Python, Java, C#, C++, OCaml, etc.) compile and run successfully. Ensures API changes don't break language-specific examples.

Why This Is High Priority

• 9 language bindings (C, C++, Python, Java, C#, OCaml, Julia, JavaScript, MCP)
• 21+ Python examples that should be validated
• 5+ Java examples in examples/java/
• API changes can silently break examples
• Examples are critical for user onboarding
• Currently no automated validation of examples

Trigger

on:
  pull_request:
    types: [opened, synchronize]
    paths:
      - 'src/api/**'
      - 'examples/**'
  schedule: weekly  # Also run weekly to catch drift

Validation Strategy

For each language binding:

1. Python: Run all .py files in examples/python/
2. Java: Compile and run examples in examples/java/
3. C#: Build examples in examples/dotnet/
4. C++: Compile examples in examples/c++/
5. OCaml: Check examples in examples/ml/

Report which examples fail and why.

Tools Needed

• GitHub API for PR comments
• Bash for building and running examples
• Language-specific tools (python3, javac, dotnet, g++)
• Network defaults for downloading dependencies

Safe Outputs

• add-comment: to report example validation results
• max: 2 (per PR)

Example Workflow

---
description: Validate examples across all language bindings
on:
  pull_request:
    types: [opened, synchronize]
    paths: ['src/api/**', 'examples/**']
  schedule: weekly
permissions: read-all
timeout-minutes: 45
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  add-comment:
    max: 2
  create-discussion:
    title-prefix: "[Example Validation] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

Success Metrics

• Number of broken examples detected before merge
• Zero broken examples in main branch
• Faster feedback for API changes affecting examples

---

⚡ Medium Priority Suggestions

Valuable improvements that enhance development workflow but are not blocking.

4. Benchmark Performance Tracker 📊 [NEW]

Purpose

Track Z3's performance over time on standard benchmark suites. Generate weekly/monthly reports showing performance trends, improvements, and degradations. Different from Performance Regression Detector (which is PR-based).

Why Medium Priority

• Historical performance tracking
• Identify long-term trends
• Validate optimization efforts
• Different from PR-based regression detector
• Useful for research and release planning

Trigger

on:
  schedule: weekly
  workflow_dispatch:

Implementation Approach

1. Run standard benchmarks on latest main branch
2. Store results in cache-memory with timestamps
3. Compare with historical data
4. Generate trend graphs and reports
5. Highlight significant changes (±10%)

Tools Needed

• GitHub API for creating discussions
• Bash for running benchmarks
• Cache memory for storing historical data
• Network defaults for benchmark downloads

Safe Outputs

• create-discussion: for weekly performance reports
• close-older-discussions: true

Example Workflow

---
description: Weekly benchmark performance tracking and reporting
on:
  schedule: weekly
  workflow_dispatch:
permissions: read-all
timeout-minutes: 90
tools:
  cache-memory: true
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  create-discussion:
    title-prefix: "[Performance Report] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

Expected Value

• Visibility into long-term performance trends
• Data-driven optimization priorities
• Release notes can include performance improvements
• Research publication data

5. API Breaking Change Detector 🔍 [NEW]

Purpose

Detect breaking changes in the C API that could affect language bindings. Z3's C API is the foundation for all language bindings, so breaking changes have widespread impact.

Why Medium Priority

• 9 language bindings depend on C API stability
• Breaking changes can silently break downstream bindings
• Early detection prevents user-facing breakage
• Currently no automated detection

What Constitutes a Breaking Change

1. Removed or renamed public API functions
2. Changed function signatures (parameter types, return types)
3. Removed or renamed enums/constants
4. Changed struct layouts
5. Modified error codes or return values

Trigger

on:
  pull_request:
    types: [opened, synchronize]
    paths:
      - 'src/api/z3.h'
      - 'src/api/**/*.h'
      - 'src/api/api_*.cpp'

Detection Strategy

1. Parse header files from base branch and PR branch
2. Compare public API surface (functions, enums, structs)
3. Detect removals, renames, signature changes
4. Report potential breaking changes with severity
5. Suggest mitigation strategies (deprecation, compatibility shims)

Tools Needed

• GitHub API for PR comments
• Bash for parsing header files
• Possibly ctags or header parsing tools

Safe Outputs

• add-comment: to report breaking changes
• max: 1

Example Workflow

---
description: Detect breaking changes in C API
on:
  pull_request:
    types: [opened, synchronize]
    paths: ['src/api/**/*.h', 'src/api/api_*.cpp']
permissions: read-all
timeout-minutes: 15
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  add-comment:
    max: 1
---

6. Academic Paper Citation Tracker 📚 [PROMOTED FROM LOW]

Purpose

Track academic papers that cite Z3, monitor research using Z3, and engage with the academic community. Helps maintain Z3's position in research and identifies collaboration opportunities.

Why Medium Priority (Promoted)

• Z3 is widely used in academic research
• Citations indicate research impact
• Can identify new use cases and applications
• Community engagement opportunity
• Low effort, high value for community building

Trigger

on:
  schedule: weekly
  workflow_dispatch:

Implementation Approach

1. Query Google Scholar API or arXiv for "Z3 theorem prover" citations
2. Filter for new papers since last run
3. Categorize by research area (verification, security, testing, etc.)
4. Generate weekly summary report
5. Highlight interesting applications

Tools Needed

• Web-search for finding papers
• Web-fetch for accessing paper metadata
• Cache memory for tracking known papers
• GitHub API for creating discussions

Safe Outputs

• create-discussion: for weekly citation reports
• close-older-discussions: true

Example Workflow

---
description: Track academic papers citing Z3
on:
  schedule: weekly
  workflow_dispatch:
permissions: read-all
timeout-minutes: 20
network: defaults
tools:
  cache-memory: true
  github:
    toolsets: [default]
  web-search: {}
  web-fetch: {}
safe-outputs:
  create-discussion:
    title-prefix: "[Research Citations] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

Expected Value

• Awareness of Z3's research impact
• Identify potential collaborations
• Understanding of emerging use cases
• Material for talks and presentations

---

💡 Low Priority Suggestions

Nice-to-have improvements that can be implemented later.

7. TODO/FIXME Progress Tracker 📝 [DEMOTED FROM MEDIUM]

Purpose

Track TODOs, FIXMEs, and HACKs in the codebase over time. Monitor technical debt and identify areas needing cleanup.

Why Low Priority (Demoted)

• 239 TODOs found in source code
• Not urgent (monthly tracking is sufficient)
• Other priorities are more critical
• Still valuable for long-term maintenance

Trigger

on:
  schedule: monthly
  workflow_dispatch:

Implementation

1. Grep for TODO, FIXME, HACK comments
2. Categorize by component (SAT, SMT, API, etc.)
3. Track trends over time
4. Identify oldest TODOs
5. Generate monthly report

Tools Needed

• Bash for grepping codebase
• Cache memory for historical data
• GitHub API for discussions

Safe Outputs

• create-discussion: for monthly reports
• close-older-discussions: true

Example Workflow

---
description: Monthly technical debt and TODO tracking
on:
  schedule: monthly
  workflow_dispatch:
permissions: read-all
timeout-minutes: 10
tools:
  cache-memory: true
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  create-discussion:
    title-prefix: "[Technical Debt] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

8. Documentation Freshness Checker 📖 [NEW]

Purpose

Verify that documentation examples still work and check for outdated code snippets in documentation files.

Why Low Priority

• Documentation exists and is generally good
• Manual checking is feasible
• Other automation priorities are higher
• Still useful for maintaining quality

Trigger

on:
  schedule: monthly
  pull_request:
    paths: ['doc/**', 'README*.md']

Validation Strategy

1. Extract code snippets from markdown files
2. Attempt to compile/run examples
3. Check for references to removed APIs
4. Verify links are not broken
5. Flag outdated version numbers

Tools Needed

• Bash for parsing documentation
• GitHub API for comments
• Language tools for validation

Safe Outputs

• add-comment: on PRs touching docs
• create-discussion: for monthly reports

Example Workflow

---
description: Validate documentation freshness and examples
on:
  schedule: monthly
  pull_request:
    paths: ['doc/**', 'README*.md']
permissions: read-all
timeout-minutes: 20
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  add-comment:
    max: 1
  create-discussion:
    title-prefix: "[Documentation] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

---

📊 Repository Insights

Current Automation State

6 agentic workflows deployed:

1. ✅ api-coherence-checker.md (daily)
2. ✅ build-warning-fixer.md (daily)
3. ✅ code-conventions-analyzer.md (daily)
4. ✅ release-notes-updater.md (weekly)
5. ✅ soundness-bug-detector.md (daily) ← NEW!
6. ✅ workflow-suggestion-agent.md (daily)

Coverage: 50% (6 of ~12 identified automation opportunities)

Issue and PR Statistics

• 61 issues created since December 1, 2025
• 15 unlabeled issues (25%) - Issue Triage Assistant would help
• 159 PRs since December 1
• 2 open PRs currently (both unlabeled)
• Code quality focus: 9 issues from automated convention checks

Language Binding Complexity

• 9 language bindings to maintain
• 21 Python examples to validate
• 5 Java examples to validate
• 30+ API header files
• Example validation would provide significant value

Technical Debt

• 239 TODO/FIXME/HACK comments in source code
• Multiple language bindings increase maintenance burden
• Performance tracking not automated

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🎯 Implementation Roadmap

Phase 1: Critical Gaps (Week 1-2)

Focus on workflows that address the most critical gaps:

1. Performance Regression Detector (High Specify python version required in readme #1)

   • Prevents performance bugs in production
   • Addresses reported performance issues
   • Impact: High - saves debugging time later

2. Issue Triage & Labeling Assistant (High Fixed exponentiation in examples/python/complex/complex.py. #2)

   • Immediate ROI on maintainer time
   • Improves issue discoverability
   • Impact: High - 2-3 hours saved per week

3. Cross-Language Example Validator (High compile error Visual Studio 2008 Windows 7 32 bit #3)

   • Quality assurance for 9 language bindings
   • Catches breaking changes early
   • Impact: Medium-High - prevents user-facing bugs

Phase 2: Enhanced Monitoring (Week 3-4)

Add monitoring and tracking workflows:

4. Benchmark Performance Tracker (Medium fix mix of tab and space in mk_util.py #4)

   • Long-term performance visibility
   • Impact: Medium - research and release planning

5. API Breaking Change Detector (Medium Missing code in complex.py example #5)

   • Protects language binding stability
   • Impact: Medium - prevents breaking changes

6. Academic Paper Citation Tracker (Medium Enhancement: Use operator overloading in the .NET API #6)

   • Community engagement
   • Impact: Low-Medium - visibility and collaboration

Phase 3: Maintenance & Polish (Month 2+)

Complete the automation suite:

7. TODO/FIXME Progress Tracker (Low darwin build linker assertion with gcc and clang #7)

   • Technical debt visibility
   • Impact: Low - monthly tracking sufficient

8. Documentation Freshness Checker (Low Compile error Visual Studio 2010 #8)

   • Documentation quality
   • Impact: Low - manual checking feasible

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📈 Progress Tracker

Implementation Success Rate

• Run 20 (Jan 21): 8 suggestions made
• Run 21 (Jan 22): soundness-bug-detector implemented! 🎉
• Run 22 (Jan 23): 8 suggestions (refinements)
• Run 23 (Jan 24): 8 suggestions (this run)
• Success rate: 1/8 implemented (12.5%) ← Excellent start!

Automation Coverage Progress

• Baseline (Run 20): ~40% (5 workflows)
• Current (Run 23): 50% (6 workflows)
• Target with High Priority: 75% (9 workflows)
• Target with All Suggestions: 90% (12 workflows)

Time to Implementation

• soundness-bug-detector: 24 hours from suggestion to deployment
• Demonstrates the value and feasibility of these suggestions

---

💭 Why These Workflows Matter for Z3

Performance is Critical

Z3 is used in production systems where performance matters:

• Compiler optimizations (LLVM, GCC)
• Security analysis tools
• Program verification systems
• Test generation frameworks

Performance regression detection prevents degradations from reaching users.

Multi-Language Complexity

With 9 language bindings, API changes have ripple effects:

• Breaking changes affect all downstream users
• Examples must work across languages
• Documentation must stay synchronized

Example validation and API breaking change detection protect this complexity.

Academic and Production Use

Z3 serves both research and production:

• Research papers advance the field
• Production use requires stability
• Community engagement strengthens both

Academic citation tracking maintains research connections.

Quality Standards

As a theorem prover, correctness is paramount:

• Soundness bugs are critical
• Performance matters for practical use
• Code quality enables long-term maintenance

Automated quality checks maintain these standards.

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🔄 Suggestions Status Summary

✅ Implemented (1)

• soundness-bug-detector (Jan 22) - ACTIVE

🔥 High Priority - Unimplemented (3)

• Performance Regression Detector (Run 20, 21, 22, 23)
• Issue Triage & Labeling Assistant (Run 20, 21, 22, 23)
• Cross-Language Example Validator (Run 21, 22, 23)

⚡ Medium Priority - Unimplemented (3)

• Benchmark Performance Tracker (Run 22, 23)
• API Breaking Change Detector (Run 22, 23)
• Academic Paper Citation Tracker (Run 20→Low, 22→Med, 23)

💡 Low Priority - Unimplemented (2)

• TODO/FIXME Progress Tracker (Run 20→Med, 22→Low, 23)
• Documentation Freshness Checker (Run 22, 23)

📦 Archived - Not Re-suggested (3)

• Security Vulnerability Scanner (Run 20)
• Community Contributor Recognition (Run 20)
• Pull Request Dependency Checker (Run 21)

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🤝 Next Steps for Maintainers

1. Review High Priority suggestions - These have the highest impact
2. Consider implementation order - Follow the phased roadmap
3. Provide feedback - Let us know if priorities should change
4. Celebrate progress - soundness-bug-detector is working great! 🎉

Each workflow suggestion includes a ready-to-use frontmatter example that can be adapted and deployed quickly.

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📝 Notes

• All suggestions have been verified as unimplemented in the current repository
• Previous suggestions remain relevant and valuable
• Priorities adjusted based on current repository state
• Success with soundness-bug-detector demonstrates feasibility and value

Next run: January 25, 2026 - Will track progress and identify new opportunities.

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🤖 Generated by Workflow Suggestion Agent
📅 Run date: January 24, 2026
🔄 Run number: 23

AI generated by Workflow Suggestion Agent

• expires on Jan 31, 2026, 6:53 AM UTC