Skill UI

This skill guides users on fine-tuning CodeRabbit's review quality. It teaches the AI agent team preferences through PR feedback, integrates custom code guidelines (e.g., security policies), allows customization of the review tone, and implements path filters to drastically reduce false positives and noise. This ensures that all code feedback is highly relevant, actionable, and aligns with specific team standards.

Orchestrates an exhaustive, multi-dimensional code review process by coordinating specialized agents (e.g., security, performance, quality, architecture). This workflow covers code quality, architectural integrity, security vulnerabilities (OWASP Top 10), performance bottlenecks, and test coverage, providing consolidated, actionable feedback and remediation guidance.

A sophisticated memory management system designed to recover, reconstruct, and maintain project context across complex, distributed, and multi-agent AI workflows. It specializes in advanced semantic memory rehydration, utilizing semantic vector search, relevance ranking, and conflict resolution to ensure seamless continuity and high fidelity of historical knowledge.

An expert system for designing and implementing advanced AI context architectures. It specializes in integrating intelligent memory systems, vector databases, and knowledge graphs to ensure AI applications maintain coherent, relevant, and scalable state across complex, multi-agent workflows. Essential for enterprise RAG implementation and stateful AI development.

This advanced system transforms session interactions into reusable, atomic knowledge units called "Instincts." By hooking into tool usage and session activity, it observes patterns (like preferred coding styles, error fixes, or repeated workflows). These instincts are then clustered and "evolved" into structured Skills, Commands, or specialized Agents, capturing deep contextual knowledge with weighted confidence scoring to enhance development productivity.

Designs and orchestrates role-based multi-agent workflows with CrewAI, defining agent personas, tasks, processes, and memory to run complex structured collaborations and planning pipelines.

CrewAI orchestrates autonomous multi-agent teams for role-based collaboration, sequential or hierarchical workflows, and production-grade orchestration with memory and tool integrations without LangChain.

This skill unlocks advanced multi-step code generation and architectural refactoring capabilities using autonomous agents. It supports parallel agent workflows, complex multi-file migrations, and multi-codebase orchestration by autonomously calling tools (Read, Write, Bash) in controlled, checkpointed cycles, drastically improving large-scale development efficiency.

A powerful AI tool designed for complex, coordinated programming tasks. It enables developers to generate, refactor, and scaffold entire features across multiple files (e.g., schema, API routes, service layers, tests) while maintaining consistency with existing codebase patterns. It supports agent mode for autonomous, multi-step changes and allows systematic migration and review of all affected files via detailed diffs.

Guides creation of Cursor-specific subagents with isolated contexts, structured prompts, and frontmatter so multi-step workflows, verifiers, or specialists follow Cursor’s directories and delegation rules.

Defines how to automate Dadata Ru operations through Composio’s Rube MCP, covering prerequisite connection checks, schema-aware tool discovery, execution patterns, and common pitfalls so agents can run reliable workflows with updated tool interfaces.

This meta-skill coordinates the entire design lifecycle, ensuring that ideas are systematically refined through mandatory stages: brainstorming, risk assessment, and multi-agent review. It acts as a process gatekeeper, enforcing adherence to structured steps and mitigating risks before allowing implementation, thus preventing premature or flawed execution.