Skill UI

Automates conditional request helper workflows for API development, offering guidance, best practices, and production-ready code/configurations for REST, GraphQL, OpenAPI, and authentication patterns.

Automates cross-browser compatibility verification by orchestrating BrowserStack, Selenium Grid, or Playwright runs across Chrome, Firefox, Safari, and Edge, capturing screenshots/logs and producing a report when requested via `/browser-test`/`/bt` to confirm web app behavior across devices.

Handles Confluence page search, creation, updates, space listings, and comments through Atlassian MCP tools; enforces Markdown bodies, natural-language Rovo Search, and Cloud ID/URL detection before any operation.

Automates Connecteam operations through Composio’s Rube MCP integration, guiding you to discover current schemas, verify connections, and execute multi-step workflows safely.

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.

Automated guidance for csv processor workflows inside business automation, offering best practices, step-by-step plans, production-grade snippets, and validation to optimize email, spreadsheet, or workflow automation tasks.

Automates common CTE query builder tasks for data analytics, offering best-practice guidance, production-ready SQL snippets, and validation against standard requirements when you need help with BI, visualization, or statistical analysis.

A comprehensive guide to mastering AI-powered coding assistance within the IDE. Learn to use the chat panel for conversational debugging and architectural discussion, and utilize inline editing for surgical code refactoring. Features advanced context referencing using @-mentions (e.g., @Files, @Codebase) to improve code understanding and generation accuracy.

A comprehensive guide to structuring effective custom prompts for AI coding assistants like Cursor. Learn the four components of prompt anatomy (Context, Task, Constraints, Format), and utilize reusable templates for complex tasks such as feature implementation, bug fixing, code review, and test generation. Master advanced techniques like Chain of Thought and Few-Shot examples to ensure high-quality, consistent code output.

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.

This comprehensive dashboard tracks and analyzes usage metrics for AI coding assistants (Cursor) in enterprise environments. It monitors total requests, model consumption, user adoption rates, and associated costs. Managers can use it to optimize resource quotas, control AI spending, and calculate the Return on Investment (ROI) to justify AI tool adoption.

Guides engineers through data partitioner setups within ETL workflows, offering best practice advice, ready-to-run code, and validation for streaming and batch pipelines.