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gaia-performance-review

Identify and report performance bottlenecks in story-scoped code changes.

2|Updated Apr 15, 2026
Search Tags:#performance#caching#story#memory#n-plus-one#review-gate#bottleneck-analysis
One-click install
npx skills add https://github.com/gaiastudio-ai/gaia-public --skill gaia-performance-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gaia-performance-review
Source: https://github.com/gaiastudio-ai/gaia-public/tree/main/plugins/gaia/skills/gaia-performance-review
Command: npx skills add https://github.com/gaiastudio-ai/gaia-public --skill gaia-performance-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Bottlenecks in a story's performance (N+1 queries, memory/bundle impact, caching gaps, and algorithmic complexity) slow down delivery and obscure root causes. This Skill automates per-story bottleneck discovery, produces a machine-readable verdict, and updates the Review Gate to streamline deployments.

Core Features & Use Cases

  • Automated, end-to-end bottleneck analysis for a single story, including changes under docs/implementation-artifacts.
  • Detects N+1 queries, unbounded queries, memory leaks, excessive bundle sizes, and caching gaps; emits a detailed, machine-readable report.
  • Integrates with the Review Gate to register verdicts and generate per-story performance artifacts for sprint reviews.

Quick Start

Run the performance review for a story using its key, for example /gaia-performance-review E28-S108.

Frequently Asked Questions about gaia-performance-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate per-story bottleneck analysis for N+1 queries and caching gaps?▼

Per-story bottleneck analysis is automated by running a performance review with a specific story key. This evaluates changed files for N+1 queries, memory impact, caching strategy, and algorithmic complexity.

What is a machine-readable verdict in performance bottleneck detection?▼

A machine-readable verdict is a deterministic classification output by a fast-path classifier. It registers performance findings into a Review Gate to streamline deployment decisions.

How do I run a performance review for a specific story key?▼

You trigger the review by invoking the performance review command with your story key, such as E28-S108. It analyzes changes under docs/implementation-artifacts to produce per-story performance artifacts.

Does the bottleneck analysis detect memory leaks and excessive bundle sizes?▼

Yes, the bottleneck analysis detects memory leaks, unbounded queries, and excessive bundle sizes. It evaluates memory and bundle impact alongside caching gaps within the changed files.

Can I integrate performance review results into my sprint review process?▼

Yes, the performance review integrates with a Review Gate to register deterministic verdicts. It generates per-story performance artifacts that can be used directly in sprint reviews.

What are the limitations of using an automated review gate for bottleneck analysis?▼

Bottleneck analysis is scoped per-story and restricted to changed files within implementation artifacts. It requires a valid story key to execute and does not evaluate the entire codebase globally.

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