Skill UI

Automatically detects and loads specialized agent instructions from the `AGENTS.md` file in the current working directory. This skill seamlessly integrates project-specific rules and behavioral guidelines into the AI's session context, ensuring the AI operates according to project standards without manual intervention. It is crucial for maintaining specialized agent behaviors across different development tasks and directory changes.

This comprehensive skill guides users through a systematic, 7-part brand strategy process, mirroring methodologies used by top agencies. It covers defining the core brand truth, architecting target audiences, establishing cultural context, developing messaging, defining visual principles, planning channel strategy, and setting measurement KPIs. Use this framework to build a robust and cohesive brand foundation from scratch.

A sophisticated, two-stage system designed to capture and internalize user corrections and learnings during ongoing conversations. It automatically detects patterns (e.g., "use X instead of Y") and queues them. Users can manually review and apply these learnings to a persistent knowledge base (CLAUDE.md), ensuring the AI continuously improves its knowledge and adherence to specific user context for future sessions.

A comprehensive guide for treating project context as a managed, first-class artifact alongside code. It establishes systematic documentation (e.g., product vision, tech stack, workflow) to ensure consistent requirements, maintain team alignment, and optimize AI interactions throughout the entire development lifecycle. Essential for greenfield and brownfield projects.

This skill automatically fetches the latest official documentation for programming libraries and frameworks (such as React, Next.js, or Prisma) via the Context7 API. It is essential for developers who require accurate, up-to-date technical reference material to ensure their code adheres to the most current best practices.

This bundle provides a comprehensive framework for diagnosing and fixing common issues when AI code generation is incorrect within Cursor. It systematically covers the root causes of poor suggestions, including context mismanagement (missing, wrong, or overflowing context), model hallucinations, and ambiguous prompting. Use this guide to improve the accuracy and reliability of AI-assisted coding workflows.

This tool integrates advanced AI capabilities into the entire Git lifecycle within the IDE. It assists developers by automatically generating standardized commit messages from staged diffs, providing deep code review using the @Git context (checking for vulnerabilities, missing handling), and offering systematic guidance for resolving complex merge conflicts. It streamlines version control and elevates code quality.

A comprehensive guide to production-ready patterns for the Databricks Python SDK. This skill covers essential best practices including singleton client initialization, structured error handling with typed exceptions, context-managed cluster lifecycle control (ensuring cleanup), and implementing type-safe job definitions. It is essential for building robust, maintainable, and idiomatic integrations with the Databricks platform.

Provides comprehensive guidance on building robust evaluation frameworks for complex agent systems. It addresses the inherent challenges of non-determinism and multiple valid paths, enabling systematic performance testing, validating context engineering choices, and measuring continuous improvements across multiple quality dimensions (e.g., factual accuracy, tool efficiency).

Curated guidelines covering idiomatic Go error handling, interface design, concurrency patterns, context propagation, testing, and module management for reliable team codebases.

This skill simulates a senior Go developer, specializing in writing high-performance, production-grade Go applications. It covers advanced topics like concurrent programming using goroutines and channels, designing robust microservices with gRPC/REST, and optimizing code using pprof and benchmarks. It enforces idiomatic Go patterns, context propagation, and strict testing practices (table-driven, fuzzing, race detection).

This utility automatically collects comprehensive diagnostic data for the Granola application, including system specifications, audio configurations, network connectivity tests, and application metadata. It packages all findings into a single ZIP file for support teams. Use this when preparing a detailed support ticket or troubleshooting complex, persistent issues. The process is designed to collect diagnostic context without exposing sensitive meeting content, transcripts, or private API keys.