Skill UI

Evaluates pitch decks slide by slide, benchmarking against the 11-slide VC template, scoring every section, flagging common killer patterns, and benchmarking versus famous decks for actionable fundraising feedback.

Guides creators already near product-market fit to grow an organic audience through free, authentic content, consistent scheduling, email ownership, and build-in-public feedback before spending on ads.

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

Automates CI/CD pipeline setup, from linting and type checks to integration, E2E, security, and deployment stages, plus feedback loops for AI agents and preview deployments.

Context Engineering Protocol optimizes agent sessions by curating persistent rules, specs, source files, and feedback so outputs stay focused, accurate, and aligned with conventions whenever you start work or the quality drops.

Generate a Google Form tailored for event or session feedback and quickly distribute it to participants through Gmail, automating the survey creation and invitation process for productivity boosts.

An agent skill simulating high-pressure big-tech corporate culture. It enforces strict owner consciousness, end-to-end validation, and role-based behaviors (P7-P10) using escalating PUA feedback to drive developers or sub-agents to deliver high-quality results.

Streamlines the process of providing code review feedback by enforcing a highly structured, ultra-compressed format. It guides users to cut through noise and deliver actionable, single-line comments (Location: Problem. Fix.) while maintaining critical context like severity and technical rationale. Ideal for team settings prioritizing efficiency and high code quality.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

A comprehensive, five-stage methodology for reviewing code quality. This process systematically checks for requirements compliance, logical correctness, code maintainability, test coverage, and critical security/performance vulnerabilities. It provides actionable feedback categorized by severity (Blocker, Major, Minor, Nit), ensuring high standards for all merged code.

This skill guides the implementation of essential user feedback states—Loading (Skeleton), Empty (Zero Data), Error, and Success—for data-dependent components or entire pages. It ensures a robust and polished user experience by providing structured, best-practice patterns for handling various data scenarios, preventing visual gaps or dead ends in the UI flow.

A comprehensive guide for professional code review in Chinese development teams. It addresses common communication pitfalls, offering structured methods to give critical feedback that is both rigorous and constructive. Learn how to use suggestion-based language, structured severity levels, mixed Chinese/English commenting, and optimal review processes to elevate code quality and team collaboration.