Skill UI

Generates implementation-ready JSON-LD schema markup for 10 types (e.g., FAQPage, Product, Article). It validates all fields against Google rich result eligibility rules, flags missing data with fix instructions, and provides clean, ready-to-paste script blocks to optimize search engine visibility and achieve rich results.

Used to verify the actual billing reality, revenue, and pricing mechanics—such as team billing, quotas, and refunds—based on underlying product code, rather than sales documents or simple customer fixes. This skill separates billing facts, customer impact, and the true product implementation logic.

This file is dedicated to handling Japanese translations for the 'messages-ops' component. It currently acts as a localization placeholder, indicating that the full Japanese translation is under active implementation. Users should refer to the original English skill file for the core functionality and detailed usage instructions.

This skill file acts as a Japanese translation placeholder for the 'product-capability' module. It indicates that the localized content is currently under development and not functional. Users should refer to the original English skill file for the core functionality and detailed implementation.

This comprehensive guide details the correct implementation of Firebase Crashlytics in Flutter applications. Learn how to capture fatal errors, non-fatal exceptions, and async/isolate errors across the entire app lifecycle. It covers best practices for customizing reports with custom keys, logs, and user identifiers, ensuring deep visibility into production crashes.

A disciplined workflow that forces you to build in thin vertical slices, test, verify, and commit each working increment before moving on, ideal for multi-file features or large refactors.

This skill activates the P7 Senior Engineer mode, designed for rigorous, solution-driven execution under P8 supervision. It is used when the user explicitly requests 'P7模式' or when invoked as a sub-task executor by P8. The process involves three key stages: designing a detailed implementation plan and impact analysis, executing the core coding, and finally, completing a mandatory three-question self-review, ensuring high quality and comprehensive verification.

Test-driven development requires writing a failing test before any implementation so logic, bug fixes, and behavioral changes are always backed by proof; it is ideal for guarding refactors and ensuring regressions stay caught by the automated suite.

This meta-skill set provides a structured framework covering the entire software development lifecycle (SDLC). It guides developers through every phase, from initial concept refinement and requirement specification to incremental implementation, testing, code review, and deployment. It enforces critical professional behaviors like surfacing assumptions, managing ambiguity, and maintaining scope discipline to ensure high-quality, robust engineering outcomes.

This advanced prompt acts as a structured guide for generating comprehensive, enterprise-grade technical implementation plans. It forces the detailed breakdown of a feature based on a PRD, covering system architecture (Mermaid diagrams), database schema design, API specifications, frontend component hierarchy, and deployment strategies, all while adhering to a monorepo structure. Ideal for large-scale SaaS development.

This tool analyzes specification files to extract all defined requirements and cross-references them with the current codebase implementation status. It systematically checks for unimplemented features, searches existing GitHub issues to prevent duplicates, and automatically generates detailed issues using a standardized feature request template. It is essential for robust product management, ensuring every defined requirement is tracked and scheduled for development.

This guide provides a detailed template and directive for generating machine-readable, deterministic implementation plans. It is designed for complex software development tasks, such as adding new features, refactoring existing code, or designing system architecture. The output ensures that all steps are atomic, measurable, and structured for autonomous execution by AI agents or human teams, minimizing ambiguity and dependency.