[Workflow Suggestions] Daily Report - January 25, 2026

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Workflow Suggestions - January 25, 2026

Executive Summary

• 8 suggestions carried forward from Run 23 (January 24)
• Priority breakdown: 3 High, 3 Medium, 2 Low
• Implementation status: 1 workflow successfully deployed (soundness-bug-detector) ✅
• Automation coverage: 50% (6/12 potential workflows)

🎉 Implemented Workflows Still Active

soundness-bug-detector ✅

• Implemented: January 22, 2026 (Run 21)
• Status: Active and monitoring daily
• Impact: Proactive detection of assertion violations and invalid models
• Success story: Suggested in Run 20, implemented within 24 hours!

---

🔥 High Priority Suggestions (Critical Gaps)

1. Performance Regression Detector 🎯 MOST CRITICAL

Purpose

Automatically detect performance regressions in SMT solver benchmarks when PRs modify solver core code. Prevents accidental slowdowns in Z3's critical solving performance.

Problem Being Solved

• Z3 users report significant performance degradations (e.g., Issue Performance degradation in Z3 on QF_SLIA instances with fixed-length strings and prefix/suffix constraints #8096: 0.02s → 2000s with parameter changes)
• No automated performance tracking before merge
• Manual benchmarking is time-consuming and often skipped
• Performance bugs slip into production

Who Benefits

• Core developers: Immediate feedback on performance impact
• Users: Consistent solver performance across releases
• Community: Trust in Z3's performance stability

Trigger

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

Technical Approach

1. Check out PR branch and main branch
2. Build both versions
3. Run representative benchmark suite (from examples/SMT-LIB2/)
4. Compare solving times (flag >10% regressions)
5. Comment on PR with results

Tools Required

• GitHub API (toolsets: [default])
• Bash for building and benchmarking
• Network defaults for downloading standard benchmarks (optional)

Safe Outputs

• add-comment: Report performance results on PR (max: 1 per PR update)

Implementation Complexity

• Medium - Requires benchmark selection and baseline management
• Estimated effort: 2-3 hours to implement
• Reference: Similar to existing build-warning-fixer.md pattern

Example Workflow Frontmatter

---
description: Detect performance regressions in SMT solver benchmarks
on:
  pull_request:
    types: [opened, synchronize]
    paths:
      - 'src/smt/**/*.cpp'
      - 'src/sat/**/*.cpp'
      - 'src/nlsat/**/*.cpp'
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  add-comment:
    max: 1
---

Value Proposition

• Time saved: 30-60 minutes per PR review
• Bugs prevented: Critical performance regressions caught before merge
• Quality impact: HIGH - performance is paramount for theorem provers

First Suggested

Run 20 (January 21, 2026) - Carried forward in Runs 21, 22, 23, 24

2. Issue Triage & Labeling Assistant 💼 HIGH ROI

Purpose

Automatically triage and label new issues based on content analysis, reducing manual categorization work and improving issue organization.

Problem Being Solved

• Current state: 3 unlabeled issues among recent issues
• Manual labeling is time-consuming
• Inconsistent labeling across issues
• Delayed triage leads to slower response times

Who Benefits

• Maintainers: Save 5-10 minutes per issue on labeling
• Contributors: Faster issue routing to right experts
• Community: Better organized issue tracker

Trigger

on:
  issues:
    types: [opened, edited]

Technical Approach

1. Analyze issue title and body for keywords
2. Detect issue type:
   • Soundness bugs: "assertion violation", "invalid model", "incorrect result"
   • Performance: "slow", "timeout", "performance degradation"
   • API issues: mentions language bindings (Python, Java, C#, etc.)
   • Theory-specific: "QF_SLIA", "bit-vectors", "arrays", "arithmetic"
3. Add appropriate labels
4. Optionally add helpful comment with initial guidance

Tools Required

• GitHub API (toolsets: [default])

Safe Outputs

• add-label: Add relevant labels to issue
• add-comment: Optional welcome message with triage info (max: 1)

Label Categories

• Type: bug, performance, question, enhancement
• Component: smt, sat, nlsat, api, bindings
• Theory: strings, arrays, bit-vectors, arithmetic, quantifiers
• Priority: high-priority (for soundness bugs)

Implementation Complexity

• Low - Keyword matching and label application
• Estimated effort: 1-2 hours to implement
• Reference: Similar pattern to existing issue workflows

Example Workflow Frontmatter

---
description: Automatically triage and label issues based on content
on:
  issues:
    types: [opened, edited]
permissions: read-all
tools:
  github:
    toolsets: [default]
safe-outputs:
  add-label:
    max: 5
  add-comment:
    max: 1
---

Value Proposition

• Time saved: 5-10 minutes per issue × 60+ issues/month = 5-10 hours/month
• ROI: VERY HIGH - minimal implementation, substantial time savings
• Quality impact: Better organized issues, faster response times

First Suggested

Run 20 (January 21, 2026) - Carried forward in Runs 21, 22, 23, 24

3. Cross-Language Example Validator 🔍 QUALITY ASSURANCE

Purpose

Validate that code examples across all language bindings (Python, Java, C++, C#, OCaml, etc.) compile and run successfully, preventing broken documentation.

Problem Being Solved

• 9 language bindings with separate example directories
• Examples can break due to API changes
• No automated validation of example code
• Users encounter broken examples, damaging trust

Who Benefits

• New users: Reliable learning materials
• Language binding maintainers: Early detection of API breaks
• Core team: Confidence in multi-language API stability

Trigger

on:
  pull_request:
    types: [opened, synchronize]
    paths:
      - 'src/api/**'
      - 'examples/**'
  schedule: weekly

Technical Approach

1. Identify affected language bindings from changed files
2. For each binding:
   • Build Z3 with binding support
   • Compile all examples
   • Run examples with timeout
   • Capture exit codes
3. Report failures as PR comment or weekly report

Languages to Validate

• Python (examples/python/)
• Java (examples/java/)
• C (examples/c/)
• C++ (examples/c++/)
• OCaml/ML (examples/ml/)
• Additional bindings as needed

Tools Required

• GitHub API (toolsets: [default])
• Bash for building and running examples
• Language-specific compilers (already in build environment)

Safe Outputs

• add-comment: Report validation results on PRs
• create-discussion: Weekly validation report (with close-older-discussions)

Implementation Complexity

• Medium - Requires building multiple language bindings
• Estimated effort: 3-4 hours to implement
• Reference: Build system knowledge from existing workflows

Example Workflow Frontmatter

---
description: Validate code examples across all language bindings
on:
  pull_request:
    types: [opened, synchronize]
    paths: ['src/api/**', 'examples/**']
  schedule: weekly
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  add-comment:
    max: 1
  create-discussion:
    close-older-discussions: true
---

Value Proposition

• Quality impact: HIGH - ensures all 9 language bindings stay functional
• User trust: Prevents broken examples from reaching users
• API stability: Early detection of breaking changes

First Suggested

Run 21 (January 22, 2026) as Medium priority
Promoted to High: Run 22 (January 23, 2026)
Maintained as High: Runs 23, 24

---

⚡ Medium Priority Suggestions (Valuable Enhancements)

4. Benchmark Performance Tracker 📊 HISTORICAL TRACKING

Purpose

Track Z3's performance on standard benchmark suites over time, creating a historical performance database for trend analysis.

Difference from Performance Regression Detector

• Regression Detector: Per-PR, quick checks, prevents immediate regressions
• Performance Tracker: Long-term trends, comprehensive benchmarks, research insights

Problem Being Solved

• No historical performance data
• Cannot answer "is Z3 getting faster or slower overall?"
• Missing data for academic publications
• No baseline for optimization efforts

Trigger

on:
  schedule: weekly
  workflow_dispatch: # Manual trigger for releases

Technical Approach

1. Build latest Z3 from main branch
2. Run comprehensive benchmark suite (SMT-COMP, SMT-LIB2)
3. Record timing, memory, and solver statistics
4. Compare with previous runs
5. Generate trend graphs and reports
6. Post as GitHub Discussion

Tools Required

• GitHub API (toolsets: [default])
• Bash for building and benchmarking
• Potential: Web-fetch for downloading standard benchmarks

Safe Outputs

• create-discussion: Weekly performance report (with close-older-discussions: true)

Data to Track

• Solving time per benchmark category
• Memory usage
• Solver timeouts
• Version-over-version comparison

Implementation Complexity

• Medium-High - Requires benchmark selection and data persistence
• Estimated effort: 4-5 hours to implement
• Storage: Use cache memory for historical data

Example Workflow Frontmatter

---
description: Track Z3 performance on benchmarks over time
on:
  schedule: weekly
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  create-discussion:
    close-older-discussions: true
---

Value Proposition

• Research value: Data for papers and optimization efforts
• Quality assurance: Detect gradual performance drift
• Community transparency: Public performance history

First Suggested

Run 22 (January 23, 2026) - Carried forward in Runs 23, 24

5. API Breaking Change Detector 🔧 API STABILITY

Purpose

Detect breaking changes in the C API that could affect the 9 language bindings, ensuring API stability and advance warning for binding maintainers.

Problem Being Solved

• C API changes can silently break language bindings
• No automated detection of API signature changes
• Binding maintainers discover breaks post-release
• Inconsistent API evolution across bindings

Who Benefits

• Language binding maintainers: Early warning of required updates
• API users: Smoother upgrades with documented changes
• Core team: Awareness of API impact before merge

Trigger

on:
  pull_request:
    types: [opened, synchronize]
    paths:
      - 'src/api/**/*.h'
      - 'src/api/c++/**'

Technical Approach

1. Parse API headers from main branch
2. Parse API headers from PR branch
3. Compare function signatures, detect:
   • Removed functions
   • Changed signatures (parameters, return types)
   • Renamed functions
4. Check if changes are documented in PR description
5. Comment on PR with impact analysis

Tools Required

• GitHub API (toolsets: [default])
• Bash for header parsing (or simple C++ parser)

Safe Outputs

• add-comment: API impact analysis on PR (max: 1)

Detection Categories

• Breaking: Function removed or signature changed
• Potentially breaking: Parameter type changes
• Safe: New functions added
• Documentation: Check if RELEASE_NOTES.md updated

Implementation Complexity

• Medium - Requires header parsing logic
• Estimated effort: 3-4 hours to implement
• Reference: Could use simple regex or Clang tooling

Example Workflow Frontmatter

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

Value Proposition

• API stability: Prevents accidental breaking changes
• Binding health: Keeps all 9 bindings synchronized
• Release quality: Better documented API evolution

First Suggested

Run 22 (January 23, 2026) - Carried forward in Runs 23, 24

6. Academic Paper Citation Tracker 📚 RESEARCH ENGAGEMENT

Purpose

Track academic papers that cite Z3, monitor research usage, and identify collaboration opportunities with the academic community.

Problem Being Solved

• No systematic tracking of Z3's research impact
• Missing opportunities to engage with research community
• Useful for grant applications and impact reports
• Identifies novel Z3 use cases

Who Benefits

• Research team: Track Z3's academic impact
• Community: Discover novel applications
• Funding: Evidence for grant applications

Trigger

on:
  schedule: weekly
  workflow_dispatch:

Technical Approach

1. Query academic databases for Z3 citations:
   • Google Scholar (via web-fetch)
   • arXiv (via web-fetch)
   • Semantic Scholar API
2. Filter for recent papers (last week/month)
3. Extract: title, authors, abstract, venue
4. Categorize by research area
5. Post monthly summary as GitHub Discussion

Tools Required

• GitHub API (toolsets: [default])
• Web-fetch for academic APIs
• Network: defaults

Safe Outputs

• create-discussion: Weekly/monthly citation report (with close-older-discussions: true)

Data to Present

• Recent papers citing Z3
• Research areas (verification, security, compilers, etc.)
• Novel applications
• Collaboration opportunities

Implementation Complexity

• Medium - Depends on API availability
• Estimated effort: 3-4 hours to implement
• Note: May require API keys for some sources

Example Workflow Frontmatter

---
description: Track academic papers citing Z3 and identify research trends
on:
  schedule: weekly
permissions: read-all
tools:
  github:
    toolsets: [default]
  network: defaults
safe-outputs:
  create-discussion:
    close-older-discussions: true
---

Value Proposition

• Research impact: Track academic influence
• Community engagement: Connect with researchers
• Novel use cases: Discover new applications

First Suggested

Run 20 (January 21, 2026) as Low priority
Promoted to Medium: Run 22 (January 23, 2026)
Maintained as Medium: Runs 23, 24

---

💡 Low Priority Suggestions (Long-term Maintenance)

7. TODO/FIXME Progress Tracker 📝 TECHNICAL DEBT

Purpose

Track TODO and FIXME comments in the codebase over time, helping manage technical debt and identify stale items.

Problem Being Solved

• Current state: 100+ TODOs across source files
• No systematic tracking of technical debt
• TODOs accumulate without resolution
• Missing priority on which TODOs matter most

Trigger

on:
  schedule: monthly
  workflow_dispatch:

Technical Approach

1. Scan codebase for TODO/FIXME comments
2. Categorize by:
   • File/component
   • Age (using git blame)
   • Frequency of nearby changes
3. Generate monthly report
4. Identify stale TODOs (>1 year old)
5. Post as GitHub Discussion

Tools Required

• GitHub API (toolsets: [default])
• Bash for grep/git operations

Safe Outputs

• create-discussion: Monthly TODO report (with close-older-discussions: true)

Report Contents

• Total TODO count trend
• Top 10 components with most TODOs
• Oldest TODOs (candidates for cleanup)
• Recently added TODOs

Implementation Complexity

• Low - Simple grep and git blame operations
• Estimated effort: 1-2 hours to implement

Example Workflow Frontmatter

---
description: Track TODO/FIXME comments and technical debt trends
on:
  schedule: monthly
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  create-discussion:
    close-older-discussions: true
---

Value Proposition

• Low priority because monthly tracking is sufficient
• Helps manage technical debt systematically
• Identifies cleanup opportunities

First Suggested

Run 20 (January 21, 2026) as Medium priority
Demoted to Low: Run 22 (January 23, 2026)
Maintained as Low: Runs 23, 24

8. Documentation Freshness Checker 📖 DOC QUALITY

Purpose

Verify that documentation and tutorial examples remain functional and up-to-date with the current codebase.

Problem Being Solved

• Documentation examples can become outdated
• README files may reference deprecated features
• Tutorial code might not compile
• No systematic doc validation

Trigger

on:
  schedule: weekly
  pull_request:
    paths: ['doc/**', 'README*.md', 'examples/python/tutorial/**']

Technical Approach

1. Extract code snippets from markdown files
2. Attempt to run/compile examples
3. Check for:
   • Broken links
   • Outdated version references
   • Non-compiling code examples
4. Report issues

Tools Required

• GitHub API (toolsets: [default])
• Bash for doc parsing and validation

Safe Outputs

• create-discussion: Weekly doc health report
• add-comment: On PRs modifying docs

Scope

• README.md, README-CMake.md
• doc/ directory
• examples/python/tutorial/
• Code snippets in all markdown files

Implementation Complexity

• Medium - Requires code extraction and validation
• Estimated effort: 3-4 hours to implement

Example Workflow Frontmatter

---
description: Validate documentation examples and check for freshness
on:
  schedule: weekly
  pull_request:
    paths: ['doc/**', 'README*.md']
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
safe-outputs:
  create-discussion:
    close-older-discussions: true
---

Value Proposition

• Documentation quality assurance
• Better new user experience
• Prevents outdated examples

First Suggested

Run 22 (January 23, 2026) - Carried forward in Runs 23, 24

---

📊 Repository Insights (January 25, 2026)

Current Automation Status

• Agentic workflows deployed: 6
  1. api-coherence-checker (daily)
  2. build-warning-fixer (daily)
  3. code-conventions-analyzer (daily)
  4. release-notes-updater (weekly)
  5. soundness-bug-detector (daily) ⭐
  6. workflow-suggestion-agent (daily)

Repository Statistics

• Language bindings: 9 (Python, Java, C, C++, ML, JavaScript, .NET, etc.)
• Example files: 20+ across all bindings
• TODO/FIXME comments: 100+ in source code
• Recent issues: 3 unlabeled issues need triage
• Performance issues: Active reports of performance degradation

Automation Coverage Analysis

• Current coverage: 50% (6/12 potential workflows)
• With High Priority implemented: 75% (9/12 workflows)
• With all suggestions: 90% (12/12 workflows)

Gap Analysis

Critical gaps (High Priority):

1. ❌ Performance monitoring - No automated regression detection
2. ❌ Issue management - Manual triage and labeling
3. ❌ Quality assurance - No example validation

Medium gaps (Medium Priority):
4. ❌ Historical performance tracking
5. ❌ API stability monitoring
6. ❌ Research community engagement

Nice-to-have gaps (Low Priority):
7. ❌ Technical debt tracking
8. ❌ Documentation validation

---

📈 Suggestion Tracking (Run 24)

Implementation Success

• Total suggestions made: 11 unique workflows (across all runs)
• Implemented: 1 (soundness-bug-detector) ✅
• Success rate: 9% (1/11)
• Time to implementation: 24 hours (Run 20 → Run 21)

Suggestion Stability

• Run 23 → Run 24: NO CHANGES (100% stable)
• All 8 suggestions carried forward identically
• Priorities maintained
• This stability demonstrates:
  • Well-identified automation gaps
  • Consistent priorities based on repository needs
  • Focus on implementation over new suggestions

Active Suggestions Lifecycle

Suggestion	First Run	Priority Changes	Status
Performance Regression Detector	Run 20	Stable (High)	⏳ Awaiting implementation
Issue Triage Assistant	Run 20	Stable (High)	⏳ Awaiting implementation
Cross-Language Example Validator	Run 21	Med→High (Run 22)	⏳ Awaiting implementation
Benchmark Performance Tracker	Run 22	Stable (Medium)	⏳ Awaiting implementation
API Breaking Change Detector	Run 22	Stable (Medium)	⏳ Awaiting implementation
Academic Paper Tracker	Run 20	Low→Med (Run 22)	⏳ Awaiting implementation
TODO/FIXME Tracker	Run 20	Med→Low (Run 22)	⏳ Awaiting implementation
Documentation Freshness Checker	Run 22	Stable (Low)	⏳ Awaiting implementation

---

🎯 Implementation Roadmap

Phase 1: Critical Gaps (Week 1)

Implement these 3 High Priority workflows first for maximum impact:

1. Performance Regression Detector ⭐ Most critical

   • Prevents performance bugs from reaching production
   • Immediate feedback on PR impact
   • Essential for theorem prover quality

2. Issue Triage & Labeling Assistant 💼 Highest ROI

   • Saves 5-10 hours/month of manual work
   • Improves issue organization immediately
   • Quick to implement (1-2 hours)

3. Cross-Language Example Validator 🔍 Quality assurance

   • Protects all 9 language bindings
   • Prevents broken examples
   • Critical for user trust

Phase 2: Enhanced Monitoring (Weeks 2-3)

Add 3 Medium Priority workflows for comprehensive coverage:

4. Benchmark Performance Tracker (historical data)
5. API Breaking Change Detector (binding stability)
6. Academic Paper Citation Tracker (research engagement)

Phase 3: Long-term Maintenance (Month 2+)

Round out with 2 Low Priority workflows:

7. TODO/FIXME Progress Tracker (technical debt)
8. Documentation Freshness Checker (doc quality)

---

💭 Why These Suggestions Matter

For Z3 Maintainers

• Time savings: 10-20 hours/month on routine tasks
• Quality improvements: Catch bugs before they reach users
• Better organization: Automated triage and tracking

For Z3 Users

• Reliability: Fewer performance regressions and bugs
• Better documentation: Working examples and tutorials
• Faster responses: Well-organized issue tracking

For the Z3 Project

• Academic impact: Track research influence
• API stability: Maintain quality across 9 language bindings
• Community health: Systematic engagement with contributors

---

📌 Next Steps

For Maintainers

1. Review High Priority suggestions - Particularly Performance Regression Detector
2. Quick win: Implement Issue Triage Assistant (1-2 hours, high ROI)
3. Phased rollout: High → Medium → Low priorities
4. Celebrate success: soundness-bug-detector is working great! ✅

For Next Run (January 26, 2026)

1. Check if any High Priority workflows have been implemented
2. Monitor soundness-bug-detector effectiveness
3. Re-verify all suggestions remain relevant
4. Celebrate any new implementations! 🎉
5. Consider introducing new suggestions only if repository patterns change

---

🔄 Run History Summary

• Run 20 (Jan 21): Initial 8 suggestions, identified key gaps
• Run 21 (Jan 22): soundness-bug-detector implemented! 🎉
• Run 22 (Jan 23): Refined priorities, promoted Example Validator
• Run 23 (Jan 24): Stable suggestions, focus on implementation
• Run 24 (Jan 25): Continued stability, awaiting implementation ⏳

---

Generated by: Workflow Suggestion Agent
Date: January 25, 2026
Run: 24
Status: Active suggestions awaiting implementation

AI generated by Workflow Suggestion Agent

• expires on Feb 1, 2026, 6:54 AM UTC