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:

User mentions "multi-language", "polyglot", or "mixed languages"
Project contains 2+ programming languages
User asks about language distribution or architecture boundaries
Comparing quality across different language components
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

bash

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

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

2. Multi-Language Quality Analysis

bash

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

Output: Quality metrics aggregated per language

3. Cross-Language Complexity Comparison

bash

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

Output: Complexity distributions for each language

4. Language-Specific Deep Context

bash

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

bash

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:

bash

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

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

Example Output:

json

{
  "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:

bash

# 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:

bash

# 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:

bash

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

Example Workflows

Example 1: Polyglot Architecture Assessment

bash

# 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

bash

# 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

bash

# 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:

bash

# 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:

bash

# 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:

bash

# 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:

bash

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:

bash

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:

bash

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

Integration with Other PMAT Skills

Workflow for Polyglot Projects:

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

Reporting for Polyglot Projects

Generate comprehensive multi-language reports:

bash

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

Report Sections:

Language Distribution: Percentages, file counts, roles
Quality Comparison: Metrics by language
Integration Patterns: Cross-language dependencies
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:

bash

# 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:

Software Architecture Metrics (Chidamber & Kemerer, 1994)
Polyglot Programming (Ford et al., 2014)
Cross-Language Static Analysis (Livshits & Lam, 2005)
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

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.

Search AI Tools

Install this agent skill to your Project

SKILL.md

PMAT Multi-Language Project Analysis Skill

When to Activate

Core Concepts: Polyglot Architecture

Available PMAT Commands

1. Language Detection and Distribution

2. Multi-Language Quality Analysis

3. Cross-Language Complexity Comparison

4. Language-Specific Deep Context

5. Polyglot Architecture Visualization

Usage Workflow

Step 1: Language Discovery

Step 2: Quality Assessment by Language

Step 3: Identify Integration Boundaries

Step 4: Language-Specific Recommendations

Example Workflows

Example 1: Polyglot Architecture Assessment

Example 2: Language-Specific Quality Comparison

Example 3: Cross-Language Refactoring Strategy

Language-Specific Quality Thresholds

Cross-Language Best Practices

1. API Contract Definition

2. Consistent Code Style

3. Unified Quality Gates

4. Language Detection Automation

Polyglot Architecture Patterns

Pattern 1: Microservices

Pattern 2: Monorepo

Pattern 3: Native Extensions

Integration with Other PMAT Skills

Reporting for Polyglot Projects

Common Multi-Language Challenges

Challenge 1: Inconsistent Quality Standards

Challenge 2: Build System Complexity

Challenge 3: Dependency Management

Challenge 4: Code Duplication Across Languages

Performance Optimization by Language

Limitations

When NOT to Use This Skill

Scientific Foundation

Version Requirements