How does this skill handle Llama Stack recording files?

It automatically ignores the content of the recordings/ folders and JSON files within them, as these are auto-generated test fixtures. Instead, it directs focus toward verifying CI status and actual logic changes.

What architectural patterns does it look for?

The skill specifically validates the Provider Interface Pattern, hierarchical configuration patterns, and correct asynchronous error propagation across component boundaries.

Can it help with distributed system bugs?

Yes, it is designed to flag common distributed system issues such as race conditions, eventual consistency errors, and improper network failure handling like missing retries or timeouts.

Why is API backward compatibility a priority?

Llama Stack emphasizes stability; this skill ensures that new changes do not break existing clients and that proper deprecation paths are provided for any removed features.

Llama Stack Code Reviewer

Name: Llama Stack Code Reviewer
Author: bbrowning

bybbrowning

Data Science & ML

Streamlines code reviews for the Llama Stack repository by focusing on distributed system patterns, API compatibility, and automated testing fixtures.

About

This skill provides specialized architectural knowledge and best practices for developers contributing to or maintaining the Llama Stack repository. It intelligently optimizes the review process by identifying critical areas like API backward compatibility, distributed system synchronization, and provider implementation patterns while automatically filtering out noise from auto-generated testing recordings. By focusing on error propagation, performance at scale, and asynchronous standards, it helps maintain high-quality code standards required for distributed AI infrastructure.

Key Features

Verification of provider interface and configuration patterns
Smart filtering of auto-generated recordings/ JSON test fixtures
Detection of anti-patterns like blocking I/O and missing telemetry
0 GitHub stars
Distributed system analysis for race conditions and consistency
API backward compatibility and versioning validation

Use Cases

Reviewing complex pull requests in the Llama Stack repository
Ensuring new provider implementations follow architectural standards
Validating that API changes maintain backward compatibility for clients

About

Key Features

Verification of provider interface and configuration patterns
Smart filtering of auto-generated recordings/ JSON test fixtures
Detection of anti-patterns like blocking I/O and missing telemetry
0 GitHub stars
Distributed system analysis for race conditions and consistency
API backward compatibility and versioning validation

Use Cases

Reviewing complex pull requests in the Llama Stack repository
Ensuring new provider implementations follow architectural standards
Validating that API changes maintain backward compatibility for clients