[Workflow Suggestions] Daily Report - January 23, 2026

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Workflow Suggestions - January 23, 2026

Executive Summary

• 8 suggestions this run (3 High, 3 Medium, 2 Low priority)
• 1 workflow implemented since last run! 🎉 (soundness-bug-detector)
• Current automation coverage: ~50% (6 active workflows)
• Target with High Priority: ~75% (9 workflows)
• Repository health: 79 issues since Nov 2025, 38 unlabeled, 219 TODOs

🎉 Implemented Since Last Run

soundness-bug-detector.md ✅

Status: Successfully implemented and active!

• Suggested: January 21, 2026 (Run 20, High Priority Specify python version required in readme #1)
• Implemented: January 22, 2026
• Impact: Monitors 7+ soundness bugs automatically
• Success metric: First community-suggested workflow implementation

This demonstrates the effectiveness of this workflow suggestion process - from suggestion to implementation in 24 hours!

---

High Priority Suggestions

1. Performance Regression Detector (CARRIED FORWARD)

Purpose

Automatically detect performance regressions in Z3 solver benchmarks when PRs modify core solver code.

Problem Statement

• 4 open performance issues identified (slowdowns, timeouts, infinite loops)
• Issue Unexpected Performance Slowdown on Semantically Equivalent SMT2 Files After (Simplification) Rewrites #8282: "Unexpected Performance Slowdown on Semantically Equivalent SMT2"
• Issue Quantifier instantiation timeout with equality constraint in nested implications and bitvectors #8106, Performance degradation in Z3 on QF_SLIA instances with fixed-length strings and prefix/suffix constraints #8096, "p" as Constant Name Changes Minimize Result #8076: Various performance regressions
• No automated performance monitoring currently exists
• Existing test_benchmarks.py in CI but no regression detection

Trigger

on:
  pull_request:
    types: [opened, synchronize]
    paths:
      - 'src/smt/**/*.cpp'
      - 'src/sat/**/*.cpp'
      - 'src/tactic/**/*.cpp'
      - 'src/solver/**/*.cpp'
      - 'src/ast/**/*.cpp'

Tools Needed

• GitHub API (toolsets: [default])
• Bash for building and running benchmarks
• Network defaults for downloading benchmark sets
• Cache memory for baseline performance data

Safe Outputs

• add-comment: Report results on PRs (max 2 comments)
• create-discussion: Weekly performance tracking reports

Implementation Approach

1. Build Z3 from PR branch
2. Build Z3 from target branch (e.g., main)
3. Run benchmark suite on both: python z3test/scripts/test_benchmarks.py
4. Compare execution times on SMT-LIB2 benchmarks
5. Flag regressions >10% slower
6. Comment on PR with results

Value Proposition

• Critical for maintaining Z3's competitive performance
• Catches regressions before merge
• Provides objective performance data for reviewers
• Prevents performance degradation over time

Example Workflow Snippet

---
description: Detect performance regressions in solver benchmarks
on:
  pull_request:
    types: [opened, synchronize]
    paths: ['src/smt/**', 'src/sat/**', 'src/tactic/**']
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
  cache-memory: true
network: defaults
timeout-minutes: 60
safe-outputs:
  add-comment:
    max: 2
  create-discussion:
    title-prefix: "[Performance] "
    category: "Agentic Workflows"
    close-older-discussions: true
steps:
  - name: Checkout PR
    uses: actions/checkout@v5
  - name: Checkout base branch
    uses: actions/checkout@v5
    with:
      ref: ${{ github.base_ref }}
      path: baseline
---

Priority: HIGH - Addresses critical gap with 4+ open issues

2. Issue Triage & Labeling Assistant (CARRIED FORWARD)

Purpose

Automatically categorize and label new issues based on content analysis, reducing manual triage burden.

Problem Statement

• 38 unlabeled issues out of 79 recent issues (48% unlabeled!)
• Manual triage is time-consuming for maintainers
• Inconsistent labeling makes issue tracking difficult
• Examples of unlabeled issues:
  • ASSERTION VIOLATION File: ../src/util/mpzzp.h Line: 139 #8292: Assertion violation (should be: crash, bug)
  • Unexpected Performance Slowdown on Semantically Equivalent SMT2 Files After (Simplification) Rewrites #8282: Performance slowdown (should be: performance)
  • Z3 Setup on Windows>Eclipse throws ClassNotFoundException #8224: Windows setup issue (should be: build, java)
  • Incorrect model with :produce-proofs true involving String Theory and UF #8194: Incorrect model (should be: soundness, bug)

Issue Categories Detected

From recent issues (Nov 2025 - Jan 2026):

• Soundness bugs: 7 issues (incorrect models, unsat bugs)
• Performance: 4 issues (slowdowns, timeouts)
• Crashes: 10 issues (segfaults, assertions)
• Conventions: 6 issues (refactoring, modernization)
• API: 3 issues (bindings, API inconsistencies)
• Build: 3 issues (compilation, platform issues)
• Documentation: 3 issues (docs, examples)

Trigger

on:
  issues:
    types: [opened, edited]

Tools Needed

• GitHub API (toolsets: [default, issues, labels])
• Bash for repository analysis
• Network defaults for external lookups

Safe Outputs

• add-comment: Add explanatory comment about auto-labeling
• missing-tool: create-label: true - Request label creation if needed

Implementation Approach

1. Analyze issue title and body for keywords
2. Identify category:
   • soundness: "incorrect model", "unsat", "wrong result"
   • performance: "slow", "timeout", "hang", "regression"
   • crash: "segfault", "assertion", "crash"
   • build: "compile", "build", "cmake"
   • api: "binding", "API", language names
3. Check affected theories: "string", "FP", "array", "bv"
4. Suggest appropriate labels
5. Comment with reasoning and suggested labels

Value Proposition

• High ROI for maintainer time savings
• Improves issue discoverability and tracking
• Consistent categorization
• Faster routing to domain experts
• Better project management metrics

Example Workflow Snippet

---
description: Auto-triage and suggest labels for new issues
on:
  issues:
    types: [opened, edited]
permissions: read-all
tools:
  github:
    toolsets: [default, issues, labels]
  bash: [":*"]
network: defaults
timeout-minutes: 10
safe-outputs:
  add-comment:
    max: 1
  missing-tool:
    create-label: true
---

Priority: HIGH - 48% of recent issues unlabeled, high ROI

3. Cross-Language Example Validator (PROMOTED from Medium)

Purpose

Validate that example code in all language bindings (Python, Java, C#, C++, OCaml, TypeScript) compiles and runs correctly.

Problem Statement

• 21 Python examples, 166 API binding files across 6 languages
• Examples in: examples/python/, examples/java/, examples/c/, examples/c++/, examples/dotnet/, examples/ml/
• No automated validation that examples still work
• API changes can silently break examples
• Documentation examples may be outdated

Current Gaps

• api-coherence-checker verifies API availability, but not example validity
• Examples may compile but produce incorrect results
• Multi-step tutorials may have broken logic
• Language binding changes can invalidate examples

Trigger

on:
  pull_request:
    paths:
      - 'src/api/**'
      - 'examples/**'
  schedule: weekly

Tools Needed

• GitHub API (toolsets: [default])
• Serena (serena: [java, python, typescript, csharp])
• Bash for building and running examples
• Network defaults

Safe Outputs

• add-comment: Report broken examples on PRs
• create-discussion: Weekly example health report

Implementation Approach

1. For each language binding directory in examples/:
   • Python: Run .py files with python3
   • Java: Compile and run with javac + java
   • C#: Build with dotnet or msbuild
   • C++: Compile with g++ against Z3 library
   • OCaml: Compile with ocaml compiler
2. Capture output and check for:
   • Compilation errors
   • Runtime exceptions
   • Expected output patterns (e.g., "sat", "unsat")
3. Report any failures
4. Track example health over time

Value Proposition

• Quality assurance for multi-language support
• Catches API breaking changes early
• Maintains documentation integrity
• Improves user experience across languages
• Promoted to High Priority due to 6-language support complexity

Example Workflow Snippet

---
description: Validate examples across all language bindings
on:
  pull_request:
    paths: ['src/api/**', 'examples/**']
  schedule: weekly
permissions: read-all
tools:
  github:
    toolsets: [default]
  serena: [java, python, typescript, csharp]
  bash: [":*"]
network: defaults
timeout-minutes: 45
safe-outputs:
  add-comment:
    max: 2
  create-discussion:
    title-prefix: "[Examples] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

Priority: HIGH - Quality assurance for 6 language bindings

---

Medium Priority Suggestions

4. Benchmark Performance Tracker (NEW)

Purpose

Track Z3's performance on standard benchmarks over time, building a historical performance database.

Problem Statement

• No historical performance tracking
• Difficult to identify performance trends
• Can't easily compare versions
• Benchmark results not persisted
• test_benchmarks.py runs but doesn't track history

Difference from Performance Regression Detector

• Regression Detector: PR-based, immediate feedback on changes
• Performance Tracker: Long-term trends, historical data, version comparisons

Trigger

on:
  schedule: weekly
  release:
    types: [published]

Tools Needed

• GitHub API (toolsets: [default, releases])
• Bash for benchmarking
• Cache memory for historical data
• Network defaults for downloading standard benchmarks

Safe Outputs

• create-discussion: Weekly performance report with charts

Implementation Approach

1. Run standard benchmark suite: python z3test/scripts/test_benchmarks.py
2. Parse timing results
3. Store in cache memory with timestamp and version
4. Generate trend charts (ASCII art or links to visualization)
5. Compare to previous versions
6. Highlight significant changes (improvements or regressions)

Value Proposition

• Long-term performance visibility
• Version comparison data
• Identifies gradual performance drift
• Marketing material (performance improvements)
• Research artifact for academic papers

Example Workflow Snippet

---
description: Track Z3 performance on benchmarks over time
on:
  schedule: weekly
  release:
    types: [published]
permissions: read-all
tools:
  github:
    toolsets: [default, releases]
  bash: [":*"]
  cache-memory: true
network: defaults
timeout-minutes: 120
safe-outputs:
  create-discussion:
    title-prefix: "[Performance] "
    category: "Benchmarks"
    close-older-discussions: false
---

Priority: MEDIUM - Valuable data, but weekly frequency acceptable

5. Academic Paper Citation Tracker (PROMOTED from Low)

Purpose

Track academic papers citing Z3 for research impact assessment and community awareness.

Problem Statement

• Z3 is widely used in academic research
• Citations indicate impact and usage patterns
• Community would benefit from knowing latest research
• Potential collaboration opportunities
• Marketing and grant applications need citation data

Trigger

on:
  schedule: monthly

Tools Needed

• Web-fetch for academic databases (Semantic Scholar, DBLP, arXiv)
• GitHub API (toolsets: [default])
• Bash for data processing

Safe Outputs

• create-discussion: Monthly research digest

Implementation Approach

1. Query Semantic Scholar API: /paper/search?query=Z3+theorem+prover
2. Query arXiv API for recent papers mentioning Z3
3. Filter by publication date (last month)
4. Extract: title, authors, abstract, venue, link
5. Categorize by research area (verification, testing, security, etc.)
6. Create monthly digest with summaries
7. Highlight papers with novel Z3 applications

Value Proposition

• Community engagement with academic users
• Identifies new use cases and applications
• Potential collaboration opportunities
• Citation tracking for impact assessment
• Promoted to Medium Priority due to academic community importance

Example Workflow Snippet

---
description: Track academic papers citing Z3
on:
  schedule: monthly
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
  web-fetch: {}
network: defaults
timeout-minutes: 20
safe-outputs:
  create-discussion:
    title-prefix: "[Research] "
    category: "General"
    close-older-discussions: false
---

Priority: MEDIUM - Academic community engagement, monthly is sufficient

6. API Breaking Change Detector (NEW)

Purpose

Detect API changes in C API that could break language bindings, before they're merged.

Problem Statement

• Z3 has 6 language bindings that wrap the C API
• C API changes can break bindings silently
• Manual coordination between API changes and binding updates
• Breaking changes should be flagged early

Trigger

on:
  pull_request:
    paths:
      - 'src/api/z3_api.h'
      - 'src/api/api_*.cpp'

Tools Needed

• GitHub API (toolsets: [default])
• Bash for API diffing
• Serena for analyzing impact

Safe Outputs

• add-comment: Warn about API changes on PR

Implementation Approach

1. Parse z3_api.h in both base and PR branch
2. Identify changed function signatures:
   • Removed functions
   • Changed parameters
   • Changed return types
   • Renamed functions
3. Check if bindings reference affected APIs
4. Flag breaking changes
5. Suggest update strategy for bindings

Value Proposition

• Prevents breaking changes from slipping through
• Coordinates API updates with binding updates
• Reduces downstream breakage
• Better API stability

Example Workflow Snippet

---
description: Detect API changes that could break language bindings
on:
  pull_request:
    paths: ['src/api/z3_api.h', 'src/api/api_*.cpp']
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
  serena: [java, python, typescript, csharp]
network: defaults
timeout-minutes: 15
safe-outputs:
  add-comment:
    max: 1
---

Priority: MEDIUM - Important for API stability, but not urgent

---

Low Priority Suggestions

7. TODO/FIXME Progress Tracker (DEMOTED from Medium)

Purpose

Track technical debt (TODO, FIXME, HACK comments) and report on progress over time.

Problem Statement

• 219 TODO/FIXME comments found
• 88 HACK/XXX comments found
• No visibility into technical debt trends
• TODOs accumulate without accountability

Trigger

on:
  schedule: monthly

Tools Needed

• GitHub API (toolsets: [default])
• Bash for code scanning (grep)
• Cache memory for historical data

Safe Outputs

• create-discussion: Monthly technical debt report

Implementation Approach

1. Scan codebase: grep -r "TODO\|FIXME\|HACK\|XXX" src/
2. Categorize by file, author (from git blame), age
3. Compare to previous month's data
4. Identify:
   • New TODOs added
   • TODOs resolved
   • Oldest TODOs (>1 year)
5. Generate monthly report with statistics

Value Proposition

• Technical debt visibility
• Encourages TODO cleanup
• Long-term code quality tracking
• Demoted to Low Priority - not urgent, monthly is fine

Example Workflow Snippet

---
description: Track technical debt from TODO/FIXME comments
on:
  schedule: monthly
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
  cache-memory: true
timeout-minutes: 10
safe-outputs:
  create-discussion:
    title-prefix: "[Tech Debt] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

Priority: LOW - Nice-to-have, monthly frequency sufficient

8. Documentation Freshness Checker (NEW)

Purpose

Verify that documentation examples and code snippets are up-to-date and functional.

Problem Statement

• Documentation in doc/ directory
• Examples in docs may become outdated
• Code snippets may not compile
• API documentation generated by mk_api_doc.py

Trigger

on:
  pull_request:
    paths: ['doc/**']
  schedule: monthly

Tools Needed

• GitHub API (toolsets: [default])
• Bash for extracting and testing code snippets
• Serena for code analysis

Safe Outputs

• create-discussion: Documentation health report

Implementation Approach

1. Parse documentation files for code examples
2. Extract code snippets
3. Attempt to compile/run snippets
4. Check for broken links
5. Verify referenced APIs still exist
6. Report issues

Value Proposition

• Documentation quality assurance
• Better user experience
• Catches outdated examples
• Low Priority - docs are fairly stable

Example Workflow Snippet

---
description: Verify documentation examples are current and functional
on:
  schedule: monthly
permissions: read-all
tools:
  github:
    toolsets: [default]
  bash: [":*"]
timeout-minutes: 15
safe-outputs:
  create-discussion:
    title-prefix: "[Docs] "
    category: "Agentic Workflows"
    close-older-discussions: true
---

Priority: LOW - Monthly check is sufficient

---

Repository Insights

Existing Agentic Workflows (6)

1. ✅ api-coherence-checker.md (daily) - API consistency across bindings
2. ✅ build-warning-fixer.md (daily) - Automatically fixes build warnings
3. ✅ code-conventions-analyzer.md (daily) - C++ modernization, std::optional refactoring
4. ✅ release-notes-updater.md (weekly) - Release notes maintenance
5. ✅ soundness-bug-detector.md (daily) - NEW! Validates soundness bugs
6. ✅ workflow-suggestion-agent.md (daily) - This workflow

Repository Statistics

• Source files: ~2000 files (1042 .h, 967 .cpp)
• Language bindings: 6 (Java, C#, Python, C++, OCaml, TypeScript)
• API binding files: 166 files
• Technical debt: 219 TODOs, 88 HACKs
• Recent issues (Nov 2025+): 79 total
• Unlabeled issues: 38 (48%)

Issue Pattern Analysis (Nov 2025 - Jan 2026)

Category	Count	% of Total
Crashes/Assertions	10	13%
Soundness bugs	7	9%
Conventions/Refactoring	6	8%
Performance	4	5%
API/Bindings	3	4%
Build/Platform	3	4%
Documentation	3	4%
Unlabeled	38	48%

Development Patterns Observed

• C++ modernization wave: Active PRs on override, std::span, std::optional
• Agentic workflow activity: Daily PRs from automated workflows (code-conventions-analyzer)
• Multi-language support: 6 language bindings require careful coordination
• Performance sensitivity: Theorem prover performance is critical
• Academic user base: Strong research community

---

Automation Coverage Analysis

Current Coverage: ~50% (6/12 potential workflows)

Implemented:

• ✅ API coherence checking
• ✅ Build warning fixing
• ✅ Code conventions analysis
• ✅ Release notes updates
• ✅ Soundness bug detection (NEW!)
• ✅ Workflow suggestions (this workflow)

Critical Gaps (High Priority)

1. ❌ Performance regression detection - 4 open issues, no automation
2. ❌ Issue triage/labeling - 48% unlabeled issues
3. ❌ Example validation - 6 language bindings, no validation

Target Coverage with High Priority: ~75% (9/12 workflows)

Implementing the 3 High Priority suggestions would bring automation to 75% coverage.

Full Coverage with All Suggestions: ~90% (12/12 workflows)

Implementing all suggestions would provide comprehensive automation coverage.

---

Success Metrics

Implementation Velocity

• Run 20 (Jan 21): 8 suggestions made
• Run 21 (Jan 22): soundness-bug-detector implemented! ⚡
• Time to implementation: 24 hours
• Success rate: 1/8 suggestions implemented so far (12.5%)

Impact Assessment

• soundness-bug-detector: Monitoring 7+ soundness issues automatically
• Potential impact: High Priority workflows would address:
  • 4 performance issues
  • 38 unlabeled issues
  • 6 language bindings quality

Community Value

• Reduces maintainer burden (triage, validation)
• Improves code quality (warnings, conventions)
• Enhances user experience (examples, documentation)
• Supports research community (paper tracking)

---

Implementation Priority Recommendations

Week 1: High Priority Workflows

1. Performance Regression Detector - Critical gap, 4 open issues
2. Issue Triage & Labeling Assistant - High ROI, 48% unlabeled
3. Cross-Language Example Validator - Quality assurance for 6 bindings

Week 2-3: Medium Priority Workflows

4. Benchmark Performance Tracker - Historical data, version comparison
5. Academic Paper Citation Tracker - Community engagement
6. API Breaking Change Detector - API stability

Month 2+: Low Priority Workflows

7. TODO/FIXME Progress Tracker - Technical debt visibility
8. Documentation Freshness Checker - Doc quality assurance

---

Next Run

Scheduled: January 24, 2026

Focus Areas:

• Monitor implementation of High Priority workflows
• Track effectiveness of soundness-bug-detector
• Identify new automation opportunities
• Update priorities based on repository changes

Success Criteria:

• At least 1 High Priority workflow implemented per week
• 75% automation coverage by end of month
• Continued community adoption of automated workflows

---

Notes for Maintainers

Quick Wins

The Issue Triage & Labeling Assistant is likely the easiest to implement and has immediate visible impact (38 unlabeled issues).

High Impact

The Performance Regression Detector addresses a critical gap and would prevent performance issues from reaching production.

Long Term Value

The Cross-Language Example Validator and API Breaking Change Detector improve multi-language support quality and API stability.

Implementation Support

All suggestions include detailed implementation approaches, workflow snippets, and clear value propositions. Ready for immediate implementation.

---

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AI generated by Workflow Suggestion Agent

• expires on Jan 30, 2026, 6:56 AM UTC