multi-language-project-analysis-with-pmat

name	Multi-Language Project Analysis with PMAT
description	Analyzes polyglot codebases with multiple programming languages using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - Working with projects containing multiple programming languages - Assessing cross-language integration patterns and quality - Understanding language distribution and architectural boundaries - Comparing quality metrics across language ecosystems - Identifying language-specific best practices violations Supports 25+ languages including Rust, Python, TypeScript, JavaScript, Go, C++, Java, Ruby, PHP, Swift, Kotlin, C, C#, Scala, Haskell, Elixir, Clojure, Dart, Lua, R, and more. Provides unified quality assessment across heterogeneous codebases.
allowed-tools	Bash, Read, Write, Glob, Grep

PMAT Multi-Language Project Analysis Skill

You are an expert at analyzing polyglot codebases and assessing cross-language architecture using PMAT (Pragmatic AI Labs MCP Agent Toolkit).

When to Activate

This skill should automatically activate when:

1. User mentions "multi-language", "polyglot", or "mixed languages"
2. Project contains 2+ programming languages
3. User asks about language distribution or architecture boundaries
4. Comparing quality across different language components
5. Assessing cross-language integration patterns

Core Concepts: Polyglot Architecture

Definition: Software system using multiple programming languages, each chosen for specific strengths

Common Patterns:

• Microservices: Different services in different languages
• Frontend/Backend Split: JavaScript/TypeScript frontend, Python/Go backend
• Native Extensions: C/C++ performance-critical code with Python/Ruby bindings
• Domain-Specific: R/Python for data science, Rust for systems, SQL for data

Challenges:

• Consistent quality standards across languages
• Cross-language API contracts
• Build system complexity
• Team expertise distribution

Available PMAT Commands

1. Language Detection and Distribution

pmat analyze languages --path . --output language_stats.json

Output: Language percentages, file counts, line counts by language

2. Multi-Language Quality Analysis

pmat analyze quality --path . --multi-language --output quality_by_lang.json

Output: Quality metrics aggregated per language

3. Cross-Language Complexity Comparison

pmat analyze complexity --path . --group-by language --output complexity_by_lang.json

Output: Complexity distributions for each language

4. Language-Specific Deep Context

pmat context --path . --language rust --output rust_context.md
pmat context --path . --language python --output python_context.md

Output: Separate deep context reports per language

5. Polyglot Architecture Visualization

pmat visualize-architecture --path . --output architecture_map.svg

Output: Visual representation of language boundaries and dependencies

Usage Workflow

Step 1: Language Discovery

Understand language composition:

# Detect all languages in project
pmat analyze languages --path . --output languages.json

# Review distribution
cat languages.json | jq '.distribution'

Example Output:

{
  "distribution": {
    "Rust": {
      "files": 145,
      "lines": 45678,
      "percentage": 62.3,
      "primary": true
    },
    "TypeScript": {
      "files": 89,
      "lines": 23456,
      "percentage": 32.0,
      "primary": false
    },
    "Python": {
      "files": 23,
      "lines": 4123,
      "percentage": 5.6,
      "primary": false
    },
    "Shell": {
      "files": 5,
      "lines": 89,
      "percentage": 0.1,
      "primary": false
    }
  }
}

Step 2: Quality Assessment by Language

Compare quality metrics across languages:

# Analyze quality for each language
pmat analyze quality --path . --multi-language --output quality_multi.json

# Generate comparison report
pmat compare-languages --input quality_multi.json --output language_comparison.md

Step 3: Identify Integration Boundaries

Map cross-language interactions:

# Detect cross-language calls (FFI, API, etc.)
pmat analyze cross-language-refs --path . --output integration_points.json

# Review integration patterns
cat integration_points.json | jq '.references[] | select(.type == "ffi")'

Step 4: Language-Specific Recommendations

Generate tailored improvement suggestions:

# Get language-specific recommendations
pmat analyze quality --path . --recommendations --group-by language \
    --output recommendations_by_lang.md

Example Workflows

Example 1: Polyglot Architecture Assessment

# User: "Can you assess the architecture of this multi-language project?"

# Step 1: Detect languages
pmat analyze languages --path . --output lang_dist.json

# Output:
# {
#   "total_languages": 4,
#   "primary_language": "Rust",
#   "distribution": {
#     "Rust": {"percentage": 62.3, "role": "Core backend services"},
#     "TypeScript": {"percentage": 32.0, "role": "Web frontend"},
#     "Python": {"percentage": 5.6, "role": "Data analysis scripts"},
#     "Shell": {"percentage": 0.1, "role": "Build automation"}
#   }
# }

# Step 2: Analyze architecture boundaries
pmat context --path . --format llm-optimized --output full_context.md

# Step 3: Identify integration patterns
grep -E "(extern|FFI|API|RPC)" full_context.md > integration_patterns.txt

# Step 4: Present assessment
# "This is a well-structured polyglot architecture:
#
# **Language Distribution**:
# - Rust (62.3%): Core backend services, business logic
# - TypeScript (32.0%): Web frontend (React/Next.js)
# - Python (5.6%): Data analysis and ML scripts
# - Shell (0.1%): Build/deployment automation
#
# **Architecture Pattern**: Microservices with clear boundaries
# - Rust services expose REST APIs
# - TypeScript frontend consumes APIs
# - Python scripts operate independently
#
# **Integration Points**:
# 1. REST API contracts (Rust ↔ TypeScript)
# 2. Data pipeline (Python → Rust database)
# 3. Build system (Shell orchestrates all)
#
# **Quality Assessment**:
# - Rust: High quality (avg complexity: 8, maintainability: 82)
# - TypeScript: Good quality (avg complexity: 12, maintainability: 75)
# - Python: Medium quality (avg complexity: 15, maintainability: 68)
#
# **Recommendations**:
# 1. Reduce TypeScript complexity (target < 10)
# 2. Add type hints to Python scripts (improve maintainability)
# 3. Document API contracts in OpenAPI/Swagger
# 4. Consider consolidating Shell scripts into Makefile"

Example 2: Language-Specific Quality Comparison

# User: "Compare code quality between Rust and Python components"

# Step 1: Analyze each language separately
pmat analyze quality --path src/rust/ --language rust --output rust_quality.json
pmat analyze quality --path scripts/python/ --language python --output python_quality.json

# Step 2: Compare metrics
pmat compare \
    --baseline rust_quality.json \
    --current python_quality.json \
    --output rust_vs_python.md

# Example output (rust_vs_python.md):
# # Rust vs Python Quality Comparison
#
# ## Complexity
# | Metric | Rust | Python | Winner |
# |--------|------|--------|--------|
# | Avg Cyclomatic | 8.2 | 14.6 | Rust (-43.8%) |
# | Avg Cognitive | 10.5 | 18.3 | Rust (-42.6%) |
# | Max Complexity | 23 | 45 | Rust (-48.9%) |
#
# ## Maintainability
# | Metric | Rust | Python | Winner |
# |--------|------|--------|--------|
# | Maintainability Index | 82.3 | 68.5 | Rust (+20.1%) |
# | Documentation Coverage | 78% | 45% | Rust (+73.3%) |
# | Test Coverage | 85% | 72% | Rust (+18.1%) |
#
# ## Technical Debt
# | Metric | Rust | Python | Winner |
# |--------|------|--------|--------|
# | TODO Count | 12 | 34 | Rust (-64.7%) |
# | FIXME Count | 5 | 18 | Rust (-72.2%) |
# | Total Debt Hours | 45 | 128 | Rust (-64.8%) |
#
# ## Insights
# - Rust code is significantly higher quality across all metrics
# - Python scripts show higher complexity and lower documentation
# - Rust's type system prevents many common defects
#
# ## Recommendations
# 1. Apply Rust quality standards to Python (add type hints, docstrings)
# 2. Refactor high-complexity Python functions (target < 15)
# 3. Increase Python test coverage to match Rust (target 85%)
# 4. Consider migrating performance-critical Python to Rust

# Step 3: Present summary
# "Rust components show 43% lower complexity and 20% better maintainability than Python.
# Python scripts have 3x more technical debt (128 vs 45 hours).
# Recommend: Add mypy type checking and improve Python documentation coverage."

Example 3: Cross-Language Refactoring Strategy

# User: "We're considering migrating some Python code to Rust. Which parts should we migrate?"

# Step 1: Analyze Python performance bottlenecks
pmat analyze complexity --path scripts/python/ \
    --metrics complexity,churn \
    --output python_hotspots.json

# Step 2: Identify migration candidates
cat python_hotspots.json | jq '.files[] | select(.complexity > 20 or .churn > 10) | {file, complexity, churn, lines}'

# Example candidates:
# {
#   "file": "scripts/python/data_processor.py",
#   "complexity": 45,
#   "churn": 23,
#   "lines": 678
# }
# {
#   "file": "scripts/python/ml_inference.py",
#   "complexity": 38,
#   "churn": 15,
#   "lines": 456
# }

# Step 3: Estimate migration effort
# Using COCOMO model: effort = 2.5 * (KLOC)^1.05

# Step 4: Prioritize by ROI
# "Migration Recommendations (prioritized by ROI):
#
# **High Priority** (migrate first):
# 1. data_processor.py (678 LOC, complexity 45, churn 23)
#    - Estimated effort: 3 weeks
#    - Expected performance gain: 10-50x
#    - Risk: Medium (well-tested logic)
#    - ROI: High (performance-critical, frequently modified)
#
# 2. ml_inference.py (456 LOC, complexity 38, churn 15)
#    - Estimated effort: 2 weeks
#    - Expected performance gain: 5-20x
#    - Risk: Low (deterministic algorithms)
#    - ROI: High (latency-sensitive)
#
# **Medium Priority** (migrate later):
# 3. validation_engine.py (234 LOC, complexity 25, churn 8)
#    - ROI: Medium (moderate complexity, moderate churn)
#
# **Low Priority** (keep in Python):
# - Scripts with complexity < 15 and churn < 5
# - One-off data analysis scripts
# - Prototyping/experimental code
#
# **Migration Strategy**:
# 1. Start with ml_inference.py (lower risk, high impact)
# 2. Expose Rust implementation via PyO3 (Python bindings)
# 3. A/B test performance in production
# 4. Gradually migrate callers to native Rust API
# 5. Deprecate Python version after 2 sprints"

Language-Specific Quality Thresholds

Different languages have different "acceptable" complexity ranges:

Language	Cyclomatic Threshold	Cognitive Threshold	Rationale
Rust	10	15	Strong type system reduces cognitive load
Python	8	12	Dynamic typing increases cognitive load
TypeScript	10	15	Type system helps, but looser than Rust
JavaScript	8	12	Dynamic, similar to Python
Go	10	15	Explicit error handling increases complexity
C/C++	15	20	Manual memory management complexity
Java/C#	10	15	OOP increases structural complexity

Adjust recommendations based on language context.

Cross-Language Best Practices

1. API Contract Definition

Use schema definition languages for cross-language APIs:

• REST: OpenAPI/Swagger
• gRPC: Protocol Buffers
• GraphQL: Schema Definition Language (SDL)

2. Consistent Code Style

Maintain consistent style across languages:

# Rust: rustfmt
pmat format --language rust --path src/rust/

# Python: black
pmat format --language python --path scripts/python/

# TypeScript: prettier
pmat format --language typescript --path frontend/

3. Unified Quality Gates

Apply language-agnostic quality standards:

# Set quality gates for all languages
pmat quality-gate \
    --max-complexity 15 \
    --min-maintainability 65 \
    --max-debt-hours 200 \
    --apply-to-all-languages

4. Language Detection Automation

Auto-detect primary language for tooling:

# Detect primary language
pmat detect-language --path . --output primary_lang.txt

# Use in CI/CD scripts
PRIMARY_LANG=$(cat primary_lang.txt)
if [ "$PRIMARY_LANG" = "Rust" ]; then
    cargo test
elif [ "$PRIMARY_LANG" = "Python" ]; then
    pytest
fi

Polyglot Architecture Patterns

Pattern 1: Microservices

Structure: Each service in optimal language Example:

• User service: Go (concurrency)
• Payment service: Rust (safety)
• Analytics: Python (data science libs)
• Web UI: TypeScript (React ecosystem)

PMAT Analysis:

pmat analyze-microservices --path services/ --output microservices_quality.json

Pattern 2: Monorepo

Structure: Multiple languages in single repository Example:

• backend/: Rust
• frontend/: TypeScript
• ml/: Python
• mobile/: Kotlin, Swift

PMAT Analysis:

pmat context --path . --monorepo-mode --output monorepo_context.md

Pattern 3: Native Extensions

Structure: Performance-critical code in C/C++/Rust, bindings to high-level languages Example:

• Core: Rust (image processing)
• Bindings: PyO3 (Python), Neon (Node.js)

PMAT Analysis:

pmat analyze-ffi --path . --output ffi_safety_report.json

Integration with Other PMAT Skills

Workflow for Polyglot Projects:

1. pmat-multi-lang: Understand language distribution ← This skill
2. pmat-context: Generate unified deep context
3. pmat-quality: Assess quality per language
4. pmat-refactor: Apply language-specific refactorings
5. pmat-tech-debt: Track debt across languages

Reporting for Polyglot Projects

Generate comprehensive multi-language reports:

# Executive summary for polyglot projects
pmat generate-polyglot-report \
    --input language_stats.json \
    --format executive \
    --output POLYGLOT_ARCHITECTURE_REPORT.md

Report Sections:

1. Language Distribution: Percentages, file counts, roles
2. Quality Comparison: Metrics by language
3. Integration Patterns: Cross-language dependencies
4. Recommendations: Migration, consolidation, standardization strategies

Common Multi-Language Challenges

Challenge 1: Inconsistent Quality Standards

Problem: Different teams apply different quality bars Solution: Use PMAT to enforce unified quality gates

Challenge 2: Build System Complexity

Problem: Multiple build tools (cargo, npm, pip, gradle) Solution: Orchestrate with Makefile or Bazel

Challenge 3: Dependency Management

Problem: Language-specific package managers Solution: Centralized dependency scanning with pmat audit-deps

Challenge 4: Code Duplication Across Languages

Problem: Business logic duplicated in different languages Solution: Identify duplication with pmat analyze duplication --cross-language

Performance Optimization by Language

Typical Performance Characteristics:

• Rust, C, C++: Fastest (compiled, zero-cost abstractions)
• Go: Fast (compiled, GC overhead minimal)
• Java, C#: Fast (JIT optimization)
• TypeScript, JavaScript: Moderate (JIT, V8 optimization)
• Python, Ruby: Slower (interpreted, dynamic)

Migration Strategy:

# Identify performance bottlenecks
pmat profile --path . --output perf_bottlenecks.json

# Recommend language for hot paths
# "Consider migrating data_processor.py (Python) to Rust for 10-50x speedup"

Limitations

• Language Coverage: PMAT supports 25+ languages, but not all (e.g., COBOL, Fortran limited)
• Cross-Language Analysis: Some patterns (e.g., FFI safety) require manual review
• Build System Integration: May need language-specific tooling for full CI/CD
• Team Expertise: Quality recommendations assume team proficiency in target languages

When NOT to Use This Skill

• Single-Language Projects: Use language-specific skills instead
• Prototypes: Multi-language analysis adds overhead for throwaway code
• Non-Code Polyglot: HTML/CSS/JSON are configuration, not programming languages

Scientific Foundation

Multi-language analysis based on:

1. Software Architecture Metrics (Chidamber & Kemerer, 1994)
2. Polyglot Programming (Ford et al., 2014)
3. Cross-Language Static Analysis (Livshits & Lam, 2005)
4. Monorepo Best Practices (Google Engineering, 2016)

Version Requirements

• Minimum: PMAT v2.170.0
• Recommended: Latest version for best multi-language support
• Check version: pmat --version

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Remember: Polyglot architecture is a powerful tool when used intentionally. Choose each language for its strengths, maintain consistent quality standards, and use PMAT to ensure architectural boundaries remain clear and maintainable.