Performance Benchmark (gstack)

Trust Score 62/100

Run automated performance baselines and regression detection for web pages (TTFB, FCP, LCP, bundle sizes, requests) and compare against historical baselines.

triggers:performancebenchmarkpage speedweb vitalsbundle sizeload time

GitHub SKILL.md

What it does

Collects detailed web performance metrics (TTFB, FCP, LCP, DOM timings, resource sizes and request counts) using a browse daemon and produces baseline and comparison reports. It identifies regressions, slowest resources, and gives concrete remediation suggestions like code-splitting, lazy loading, and async/defer for third-party scripts.

When to use it

Use this skill when you want to: capture a performance baseline before changes, run quick or full audits on pages, compare current metrics to a baseline to detect regressions, or generate trend reports across multiple runs. Typical triggers: making a PR that changes frontend assets, shipping new third-party scripts, or investigating sudden load-time regressions.

What's included

Scripts: none bundled with the SKILL.md (instructions assume the browse daemon exists)
References: guidance and thresholds for regression detection and performance budgeting embedded in the skill body
Instructions: step-by-step phases for setup, page discovery, data collection, baseline capture, comparison, trend analysis and report saving

Compatible agents

Compatible with agent environments that can run shell tools and a browse daemon (Claude Code / gstack-style environments).

Audit Summary

Performance benchmarking skill for web pages using gstack's browse daemon. Measures TTFB, FCP, LCP, bundle sizes, and compares against baselines. Extremely detailed SKILL.md with clear phases, thresholds, and output formats. No bundled scripts — relies entirely on external gstack CLI tools. Heavy preamble includes telemetry phone-home, auto-update checks, eval of external command output, and curl|bash install flow which raise security concerns.

Watch Out

Requires gstack browse daemon to be built locally before use
Preamble runs telemetry logging and auto-update checks by default
Uses eval and source on external command output in preamble
Heavy dependency on entire gstack ecosystem (binaries, config, analytics)
curl|bash pattern used for bun installation in setup

Notes

Core benchmark skill is read-only and well-designed. Security concerns are concentrated in the shared gstack preamble boilerplate rather than the skill itself. The preamble includes telemetry with device ID, auto-update flows, eval of external command output, and a curl|bash install pattern. These are standard for CLI tooling but represent real attack surface. The skill is part of the gstack-ko monorepo and cannot function without the gstack ecosystem.

Information

Repository: gstack-ko
Stars: 42

Trust Score

Overall62

Security48

Code Quality82

Architecture68

Usefulness58

More from gstack-ko

Plan Design Review

Interactive, designer-eye review of design plans: rates dimensions, finds gaps, and edits the plan to add missing UI decisions and interaction states.

Retro

MANUAL TRIGGER ONLY: invoke only when user types /retro. Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics w...

Related Skills

OpenTestAI

Automated, high-confidence AI testing: bug detection, persona feedback, and prioritized test-case generation using many specialized tester profiles.

Canary — Post-Deploy Visual Monitor

Run a short post-deploy monitor that captures screenshots, checks console errors, and compares performance against baselines to detect regressions and page fail

Go Data Structures

Authoritative guidance on choosing and using Go built-in and standard-library data structures, with practical best practices for slices, maps, arrays, container

React Development Expert

Provides authoritative React guidance on hooks, state patterns, Server Components, performance optimization, and common architectural patterns.

Code Reviewer

Perform structured, prioritized code reviews that find correctness, security, performance, reliability, and testing issues and provide concrete fix suggestions.

dotLottie Web

Guidelines and patterns for implementing performant dotLottie/Lottie animations on the web (vanilla JS and React), including workers, state machines, and themin

Party Engine Skill

Guidance and examples for using the @cazala/party particle engine (engine lifecycle, modules, WebGPU vs CPU patterns) in custom apps.

Party Skill

Programmatic guide for the @cazala/party library: engine setup, modules, particle APIs, and performance tips for WebGPU and CPU runtimes.

Back to Skills