Skill UI

A comprehensive guide to designing transparent, predictable, and developer-friendly usage-based pricing models. Learn which metrics (API calls, compute time, storage) developers accept, how to structure clear pricing pages, and how to avoid confusing or punitive billing practices to maximize adoption and revenue.

This skill enforces a rigorous, multi-phase coding workflow (Discuss -> Map -> Decompose -> Execute -> Verify) to ensure code is structurally grounded, complete, and free from invented APIs or assumptions. It is designed for high-stakes, complex, or multi-file engineering tasks, acting as a senior, disciplined coding partner that plans meticulously before writing code.

This skill integrates with DeepAPI to facilitate advanced web scraping, market research, and controlled email workflows. It handles scraping data from various sources like websites, LinkedIn, GitHub, and Twitter, while managing costs and ensuring explicit user approval for sensitive actions like sending emails. Use it for robust data extraction and automated reporting.

This skill fetches raw text transcripts from any YouTube video. It prioritizes using the DeepAPI service for reliable, server-side scraping, which bypasses local bot detection issues. If DeepAPI fails or the API key is unavailable, it gracefully falls back to using the local `yt-dlp` tool. The output is always saved as a clean, unformatted .txt file.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

Adds x402 payment execution for AI agents with task budgets, spending controls, and non-custodial wallets. Supports Base via agentwallet-sdk and X Layer via OKX payment protocols for controlled API and service payments.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

This guide outlines how to build new API connectors or providers that strictly adhere to the existing integration architecture and patterns of a target repository. It is designed for adding integrations while maintaining architectural consistency (e.g., layout, configuration schema, authentication model, and testing style), preventing the creation of novel, unsupported patterns.

Automatically generates comprehensive API documentation from existing TypeScript or JavaScript source code. It scans public exports, parses detailed JSDoc annotations (including parameters, returns, and throws), and can generate standardized OpenAPI 3.0 specifications for HTTP endpoints, ensuring robust and up-to-date API references.

A comprehensive guide covering scalable backend architecture patterns, including Repository, Service, and Middleware design. It details best practices for building robust APIs using Node.js, Express, and Next.js. Topics covered include RESTful API structuring, database query optimization (preventing N+1 issues), implementing database transactions, and integrating caching strategies like Redis. This guide is essential for developing high-performance, maintainable server-side applications.

This skill provides comprehensive web browser automation capabilities for agents. It utilizes AI-optimized snapshots, reducing context size by 93% by using element references instead of full DOM. Functions include navigation, interactive element extraction, form filling, data scraping, and state management across multiple isolated sessions. Ideal for complex web testing and data extraction workflows.

This skill allows users to extract structured data (text, tables, attributes) from rendered web pages. It enhances traditional web scraping by ensuring all extracted strings pass mandatory AIDefence PII checks and prompt-injection gates before being stored or returned to the model. It supports building reusable extraction templates and running one-shot queries.