This repository is a distribution and development monorepo for "Agent Skills" — packaged instruction sets, rule files, and CLI tooling that extend AI coding agents to work more effectively with Vercel, React, Next.js, and React Native. The dominant artifact is the `vercel-optimize` skill, a sophisticated performance/cost audit pipeline. It starts by collecting real signals from the Vercel CLI (metrics, usage, project config, route data), then applies deterministic pure-JS gates and AST-based code scanners to generate candidate investigation targets. LLM sub-agents receive structured investigation briefs with playbooks, citations, and support-topic guardrails; their outputs are collected, mechanically verified, sanitized, deduplicated, graded, and rendered into a customer-safe markdown report. This is not a web service or library in the traditional sense — it is a toolchain of executable scripts plus knowledge artifacts designed to be installed into agent environments via `npx skills add`. The repository also contains curated knowledge bases (`react-best-practices`, `react-native-skills`, `composition-patterns`, `react-view-transitions`, `web-design-guidelines`) stored as markdown rules with YAML frontmatter. A build package (`react-best-practices-build`) parses these rules, validates them, and compiles them into `AGENTS.md` files for consumption by agents. The engineering quality is strongest in `vercel-optimize`: it has extensive `node:test` coverage, real CLI output fixtures, a modular gate/scanner/sanitizer architecture, claim-based verification, and careful handling of Vercel API rate limits. The main risks are external dependency drift — Vercel CLI output schemas, metric IDs, Observability Plus availability, and Next.js/React framework versions all change over time — and the inherent fragility of parsing LLM sub-agent output. Although the repo contains large amounts of markdown knowledge content, the dominant character is application/tooling: it ships executable entry points, has unit tests, CI workflows, and a build pipeline. It is not a data-research repository, and the metric fixtures exist to test the tooling rather than to answer analytical questions.
This repository is a Webpack plugin that integrates Google Lighthouse for running performance and quality audits during the build process. After Webpack emits assets, the plugin triggers Lighthouse against a specified URL, collects the report, and optionally saves artifacts like screenshots and traces. The codebase consists of two main parts: a Webpack-specific plugin that taps into the compiler's afterEmit hook, and a reusable Lighthouse runner module that handles Chrome lifecycle management, audit execution, and result processing. The project is explicitly marked as deprecated in the README, advising users to use Lighthouse directly instead.