Skill UI

Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs. This skill guides users through establishing robust design standards, reviewing specifications, and optimizing APIs for various use cases, ensuring longevity and developer delight.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This tool performs a comprehensive review of OAuth 2.0 scopes, identifying over-permissioned third-party application integrations, excessive API scopes, and unused token grants. It is crucial for implementing the principle of least privilege across complex identity providers and SaaS ecosystems. Use cases include compliance auditing (GDPR, SOC 2), post-incident security reviews, and managing shadow IT risk by auditing cross-boundary consent grants.

This skill provides a comprehensive framework for conducting high-quality code reviews. It guides developers through checking code across four critical dimensions: Correctness (functional accuracy and edge cases), Maintainability (readability and documentation), Performance (efficiency and resource usage), and Security (input validation and vulnerability management). It also emphasizes providing actionable, constructive, and empathetic feedback.

A CFO-style skill that numerically stress-tests any plan affecting money: burn multiple, runway, unit economics, dilution, capital allocation, and bear-case survival. Use before approving significant spend, fundraising, pricing changes, or new hiring requisitions to get a GREEN/YELLOW/RED verdict.

A sophisticated tool for commercial leaders preparing board-ready forecasts. It goes beyond single-number projections by decomposing pipeline into commit, best-case, and pipe-only tiers. It also projects cohort-level Net/Gross Revenue Retention (NRR/GRR) to detect leaky cohorts and scores per-stage funnel confidence, ensuring all assumptions are explicit for executive review.

This utility provides an extension point to inject custom query parameters into all live preview URLs generated by the Website Builder. Developers can use this to automatically append necessary data, such as signed tokens, tenant identifiers, or feature flags, ensuring the preview environment is fully configured for specific use cases. It supports complex async operations like fetching tokens from remote APIs.

This skill automates a structured Product Management (PM) design review process. It assesses a Figma design against defined product requirements (PRD) and user flows, ensuring functional completeness and readiness for engineering handoff. It checks for requirements coverage, handles edge cases (empty/error/loading states), identifies functional concerns (not aesthetic), and provides a clear, actionable approval status.

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This tool is designed to manage the process of altering an existing, functional feature to meet a new desired specification. It ensures a structured workflow: first, update the existing test cases to reflect the new behavior; second, modify the underlying implementation to pass these updated tests; finally, complete through review and gated commit. Use this when the feature works but its behavior needs adjustment.