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parallel-debug-orchestrator skill

/parallel-debug-orchestrator

This skill helps orchestrate parallel debugging with root-cause tracing across failures, guiding best practices for efficient, reliable debugging workflows.

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---
name: parallel-debug-orchestrator
description: |
  Orchestrate parallel debugging agents with root-cause tracing for multi-failure scenarios
version: "1.0.0"
category: Debugging
tags:
  - parallel
  - debugging
  - orchestration
  - agents
  - root-cause
author: mcp-skillset
license: MIT
created: 2026-01-01
last_updated: 2026-01-01
---

# Parallel Debug Orchestrator

## Overview

This skill provides guidance for debugging tasks using modern best practices and proven patterns.

## When to Use This Skill

Use this skill when:
- Working with debugging projects
- Implementing debugging-related features
- Following best practices for debugging

## Core Principles

### 1. Follow Industry Standards

**Always adhere to established conventions and best practices**

```
# Example: Follow naming conventions and structure
# Adapt this to your specific domain and language
```

### 2. Prioritize Code Quality

**Write clean, maintainable, and well-documented code**

- Use consistent formatting and style
- Add meaningful comments for complex logic
- Follow SOLID principles where applicable

### 3. Test-Driven Approach

**Write tests to validate functionality**

- Unit tests for individual components
- Integration tests for system interactions
- End-to-end tests for critical workflows

## Best Practices

### Structure and Organization

- Organize code into logical modules and components
- Use clear and descriptive naming conventions
- Keep files focused on single responsibilities
- Limit file size to maintain readability (< 500 lines)

### Error Handling

- Implement comprehensive error handling
- Use specific exception types
- Provide actionable error messages
- Log errors with appropriate context

### Performance Considerations

- Optimize for readability first, performance second
- Profile before optimizing
- Use appropriate data structures and algorithms
- Consider memory usage for large datasets

### Security

- Validate all inputs
- Sanitize outputs to prevent injection
- Use secure defaults
- Keep dependencies updated

## Common Patterns

### Pattern 1: Configuration Management

```
# Separate configuration from code
# Use environment variables for sensitive data
# Provide sensible defaults
```

### Pattern 2: Dependency Injection

```
# Inject dependencies rather than hardcoding
# Makes code testable and flexible
# Reduces coupling between components
```

### Pattern 3: Error Recovery

```
# Implement graceful degradation
# Use retry logic with exponential backoff
# Provide fallback mechanisms where appropriate
```

## Anti-Patterns

### ❌ Avoid: Hardcoded Values

**Don't hardcode configuration, credentials, or magic numbers**

```
# BAD: Hardcoded values
API_TOKEN = "hardcoded-value-bad"  # Never do this!
max_retries = 3
```

✅ **Instead: Use configuration management**

```
# GOOD: Configuration-driven
API_TOKEN = os.getenv("API_TOKEN")  # Get from environment
max_retries = config.get("max_retries", 3)
```

### ❌ Avoid: Silent Failures

**Don't catch exceptions without logging or handling**

```
# BAD: Silent failure
try:
    risky_operation()
except Exception:
    pass
```

✅ **Instead: Explicit error handling**

```
# GOOD: Explicit handling
try:
    risky_operation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")
    raise
```

### ❌ Avoid: Premature Optimization

**Don't optimize without measurements**

✅ **Instead: Profile first, then optimize**

- Measure performance with realistic workloads
- Identify actual bottlenecks
- Optimize the critical paths only
- Validate improvements with benchmarks

## Testing Strategy

### Unit Tests

- Test individual functions and classes
- Mock external dependencies
- Cover edge cases and error conditions
- Aim for >80% code coverage

### Integration Tests

- Test component interactions
- Use test databases or services
- Validate data flow across boundaries
- Test error propagation

### Best Practices for Tests

- Make tests independent and repeatable
- Use descriptive test names
- Follow AAA pattern: Arrange, Act, Assert
- Keep tests simple and focused

## Debugging Techniques

### Common Issues and Solutions

**Issue**: Unexpected behavior in production

**Solution**:
1. Enable detailed logging
2. Reproduce in staging environment
3. Use debugger to inspect state
4. Add assertions to catch assumptions

**Issue**: Performance degradation

**Solution**:
1. Profile the application
2. Identify bottlenecks with metrics
3. Optimize critical paths
4. Monitor improvements with benchmarks

## Related Skills
- **test-driven-development**: Write tests before implementation
- **systematic-debugging**: Debug issues methodically
- **code-review**: Review code for quality and correctness

## References

- Industry documentation and best practices
- Official framework/library documentation
- Community resources and guides
- Code examples and patterns

## Version History

- **1.0.0** (2026-01-01): Initial version

Overview

This skill orchestrates parallel debugging agents and provides root-cause tracing patterns for multi-failure scenarios. It focuses on coordinating concurrent diagnostics, consolidating traces, and producing actionable root-cause insights for complex systems. The guidance emphasizes practical patterns, error recovery, and test-driven validation.

How this skill works

The orchestrator coordinates multiple debugging agents running in parallel, routes diagnostic tasks, and merges their outputs into a unified trace. It applies retry and exponential backoff strategies, tags and correlates events for root-cause analysis, and surfaces prioritized failure paths. The approach includes configuration-driven behavior, dependency injection for testability, and comprehensive logging to preserve context across concurrent runs.

When to use it

  • Investigating incidents that involve several simultaneous or cascading failures
  • Running distributed diagnostics across microservices, containers, or nodes
  • Automating parallel test-and-debug workflows to reduce time-to-root-cause
  • Validating error-recovery strategies under load or failure injection
  • Integrating with observability pipelines to enrich traces with debug artifacts

Best practices

  • Keep orchestration logic modular and inject agent implementations to enable mocking and testing
  • Separate configuration (timeouts, retry policies, agent lists) from code using environment variables or config files
  • Log structured context (trace IDs, timestamps, agent metadata) to correlate outputs reliably
  • Use retry with exponential backoff and capped attempts; provide clear fallback behavior when agents fail
  • Limit parallelism to available resources and profile under realistic workloads before scaling

Example use cases

  • Simultaneously run memory, CPU, and I/O diagnostics across a cluster and merge findings into a single root-cause report
  • Trigger targeted tracing on affected services and orchestrate downstream agent runs to verify propagation paths
  • Automate a chaos-testing workflow where parallel debug agents validate service recovery and collect failure traces
  • Run parallel integration diagnostics in CI to quickly isolate failing subsystems and produce actionable logs

FAQ

How do I avoid overwhelming production systems with parallel agents?

Throttle concurrency based on resource quotas, schedule low-impact checks during off-peak hours, and use sampling or lightweight probes instead of full diagnostics for high-frequency runs.

What if agents return conflicting root-cause signals?

Correlate by trace IDs and timestamps, weight signals by agent confidence and coverage, and present prioritized hypotheses with evidence so engineers can validate the most likely causes.

How should I test the orchestrator itself?

Use dependency injection to replace agents with deterministic mocks, run unit tests for orchestration logic, and create integration tests that simulate multi-failure scenarios with controlled chaos inputs.