chrome-devtools-mcp
Chrome DevTools MCP server for AI-driven browser automation, testing, and debugging via Puppeteer. Features input automation, visual snapshots, performance tracing, and network inspection.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
530 skills found
Chrome DevTools MCP server for AI-driven browser automation, testing, and debugging via Puppeteer. Features input automation, visual snapshots, performance tracing, and network inspection.
Build, manage, and deploy AI-powered voice assistants, phone bots, and IVR systems with Vapi using the Model Context Protocol (MCP).
A framework to transform experimental ML prototypes into robust, production-ready Python packages using src layout, hybrid architecture, and strict configuration management.
Epsimo AI platform SDK and CLI for building agents with persistent state, Virtual Database, streaming conversations, and a React UI kit.
Build distinctive, high-end React Native Expo interfaces using liquid glass design and iOS Human Interface Guidelines for production-grade mobile apps.
Generate professional Product Requirements Documents (PRD) and structure features for autonomous development cycles.
Behavioral guidelines for LLMs to reduce coding mistakes, follow best practices, and improve output quality by enforcing simplicity, surgical changes, and goal-driven verification.
CLI-only iOS development agent for Swift, SwiftUI, and UIKit. Handles the full lifecycle: build, debug, test, and release without Xcode.
Generate professional multi-platform ad campaigns from a URL. Get ad copy, audience targeting, creative specs, and budget strategies ready for media buying.
Generate professional, cohesive, project-specific SVG icon sets with consistent style, stroke weight, and visual density. Ideal for unique web and app UI branding.
Intelligent orchestration for dispatching tasks to specialized background agents with performance-based routing and execution tracking.
Epistemic safety analysis for JSON data in prompts to prevent LLM hallucinations and reasoning errors when handling incomplete or large-scale datasets.