Audit frontend, backend, agentic, and infrastructure performance and generate fix plans.
npx skills add https://github.com/gkganesh12/gk-stack --skill gk-perf
Please help me install this Agent Skill. Skill: gk-perf Source: https://github.com/gkganesh12/gk-stack/tree/main/skills-library/gk-perf Command: npx skills add https://github.com/gkganesh12/gk-stack --skill gk-perf
Performance issues are often invisible until they cause production outages or high costs. This skill provides a systematic, measurable way to audit and optimize your entire stack, ensuring performance is tracked and regressions are caught before deployment.
Run the gk-perf skill to perform a full-stack audit and generate a fix plan for the current project.
High-intent search queries and answers about installing and using this skill.
A full-stack performance audit analyzes metrics across frontend, backend, agentic, and infrastructure layers to identify bottlenecks. It measures API latency, database queries, and LLM token efficiency to generate an actionable fix plan for pre-deployment quality gating.
To optimize LLM token usage and reduce latency, the auditing process analyzes agentic layers to identify inefficient prompt chains. It automatically trims token budgets and generates safe optimizations to ensure your application meets performance thresholds.
Automated performance regression testing catches database query bottlenecks and frontend bloat by measuring system metrics using Lighthouse and analyzing database configurations. It identifies N+1 queries and bloated bundles, generating a comprehensive fix plan.
Yes, Python3, the Lighthouse CLI, and tiktoken are required dependencies to run this automated performance optimization. They enable the system to measure frontend metrics and analyze LLM token efficiency across your infrastructure layers.
Yes, the performance audit generates and applies safe, approved optimizations including database indexing and code memoization. It resolves bottlenecks automatically to ensure your application passes pre-deployment quality gates.
High latency can persist due to invisible infrastructure or agentic layer bottlenecks not caught by standard checks. A comprehensive performance audit analyzes container configurations and LLM token efficiency to identify and resolve these hidden system bottlenecks.
al-stevConfigure Atlassian API credentials for Jira and Confluence access.
NourcineAbBenchmark and validate AI system speed, memory, and search improvements.
Ryno-Crypto-Mining-ServicesCreate CI/CD pipelines with GitHub Actions, Terraform, and Prometheus.
kyupidGenerate, validate, and test MXQL queries with ADDROW sample data.
