Skill UI

Assists ML Training workflows by automatically activating on confusion matrix generator requests, offering structured guidance, best-practice checks, and ready-to-use code snippets.

Addresses the critical challenge of managing extremely long conversation histories (millions of tokens) in advanced AI agents, such as coding assistants. This technique moves beyond simple token minimization by implementing structured compression strategies (like Anchored Iterative Summarization) to prevent the loss of critical technical details—such as file paths, decisions, and error messages—ensuring the agent maintains functional context across complex, multi-step tasks. It emphasizes optimizing for 'tokens-per-task' rather than just tokens-per-request.

This skill set provides advanced, structured strategies for optimizing Large Language Model (LLM) interactions. It covers techniques such as intelligent context summarization, dynamic trimming, context routing, and precise token budget allocation to prevent 'context rot'. It is essential for building robust, long-running, multi-turn conversational AI systems.

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.

Details how to research context, structure homepage or landing copy, and follow clarity/benefit-driven rules so every headline, CTA, and section pushes visitors toward a single conversion.

Transforms detailed user requirements into secure, production-ready Ansible playbooks that automate server configuration, software deployments, and infrastructure workflows across multiple platforms for repeatable DevOps operations.

Transforms raw datasets into charts, plots, and graphs by analyzing structure, picking suitable visualization types, and rendering them clearly so users can request visualizations or explore patterns and relationships.

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.

This is a hands-on walkthrough demonstrating the four core AI features of the Cursor IDE: Tab Completion, Chat, Inline Edit, and Composer. Users learn how to leverage AI for coding tasks, debugging, generating boilerplate code, and performing multi-file refactoring within a structured project environment. It's an essential guide for developers adopting AI-assisted coding workflows.

Manages and enforces project-specific coding standards and architectural conventions within the Cursor IDE. Developers can define rules using `.mdc` files, specifying required patterns (e.g., TypeScript strict mode, React component structure, API validation). This system guides the AI assistant by providing deep context, ensuring code consistency and adherence to best practices across the entire codebase.

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.

A comprehensive guide for administrators and teams on setting up Cursor for enterprise use. It covers organizational plan selection (Pro, Business, Enterprise), advanced features like SSO, SCIM provisioning, and Privacy Mode enforcement. The guide details setting up roles, managing usage analytics, defining shared project rules via Git, and providing a structured onboarding checklist to ensure seamless adoption and secure coding practices across the organization.