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patterns skill

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This skill helps you implement robust architecture patterns in Python projects by enforcing security, testing, logging, and performance best practices.

npx playbooks add skill williamzujkowski/standards --skill patterns

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---
name: patterns
description: Patterns standards for patterns in Architecture environments. Covers
---

# Patterns

> **Quick Navigation:**
> Level 1: [Quick Start](#level-1-quick-start) (5 min) → Level 2: [Implementation](#level-2-implementation) (30 min) → Level 3: [Mastery](#level-3-mastery-resources) (Extended)

---

## Level 1: Quick Start

### Core Principles

1. **Best Practices**: Follow industry-standard patterns for architecture
2. **Security First**: Implement secure defaults and validate all inputs
3. **Maintainability**: Write clean, documented, testable code
4. **Performance**: Optimize for common use cases

### Essential Checklist

- [ ] Follow established patterns for architecture
- [ ] Implement proper error handling
- [ ] Add comprehensive logging
- [ ] Write unit and integration tests
- [ ] Document public interfaces

### Quick Links to Level 2

- [Core Concepts](#core-concepts)
- [Implementation Patterns](#implementation-patterns)
- [Common Pitfalls](#common-pitfalls)

---

## Level 2: Implementation

### Core Concepts

This skill covers essential practices for architecture.

**Key areas include:**

- Architecture patterns
- Implementation best practices
- Testing strategies
- Performance optimization

### Implementation Patterns

Apply these patterns when working with architecture:

1. **Pattern Selection**: Choose appropriate patterns for your use case
2. **Error Handling**: Implement comprehensive error recovery
3. **Monitoring**: Add observability hooks for production

### Common Pitfalls

Avoid these common mistakes:

- Skipping validation of inputs
- Ignoring edge cases
- Missing test coverage
- Poor documentation

---

## Level 3: Mastery Resources

### Reference Materials

- [Related Standards](../../docs/standards/)
- [Best Practices Guide](../../docs/guides/)

### Templates

See the `templates/` directory for starter configurations.

### External Resources

Consult official documentation and community best practices for architecture.

Overview

This skill provides a compact, battle-tested set of architecture patterns and standards for starting LLM software projects quickly and correctly. It focuses on secure defaults, maintainability, and performance so teams can bootstrap projects in minutes and scale safely. The guidance is implementation-oriented and suitable for Python-based systems.

How this skill works

The skill inspects common architecture concerns and prescribes patterns for selection, error handling, observability, testing, and performance. It offers a simple checklist for quick starts, concrete implementation patterns for day-to-day work, and links to templates and deeper reference materials for mastery. Use it as a living checklist and pattern catalog to validate design decisions before coding.

When to use it

  • Starting a new LLM-backed service and needing a reliable architecture baseline
  • Reviewing or auditing an existing system for security, tests, and observability gaps
  • Onboarding engineers who need a consistent set of implementation standards
  • Preparing production rollouts that require performance and failure-mode guidance
  • Creating templates or starter projects for repeatable development

Best practices

  • Choose patterns that match your scale and failure characteristics rather than copying blindly
  • Validate and sanitize all inputs; fail fast on unexpected formats
  • Add comprehensive logging and metrics early to enable troubleshooting
  • Automate unit and integration tests; cover edge cases and error paths
  • Document public interfaces and keep templates updated with real-world fixes

Example use cases

  • Bootstrap a new LLM microservice with secure defaults, logging, and tests in under 30 minutes
  • Run an architecture review against a deployed system to identify missing validation or monitoring
  • Provide starter templates for teams that enforce consistent error handling and observability
  • Optimize an inference pipeline by applying performance patterns and measuring impact
  • Train new engineers on proven patterns for resilient LLM integration

FAQ

Is this skill tied to a specific framework or cloud provider?

No. The patterns are framework-agnostic and focus on general architecture, testing, security, and observability principles that apply across environments.

How do I apply these patterns incrementally?

Start with the essential checklist: input validation, error handling, logging, tests, and documentation. Add observability and performance optimizations in the next iteration.