Skill UI

Provides CodeRabbit-specific rate limit handling with exponential backoff, jittered retries, idempotency keys, and queue-based throttling so API calls stay reliable even when hitting 429s or other transient errors.

Guide detailing how to secure CodeRabbit API keys, tokens, and access through environment variables, secret rotation, least-privilege scopes, webhook validation, and logging for audits in production and staging deployments.

Validates CodeRabbit GitHub/GitLab webhook signatures, routes review/check events, and processes actionable notifications or metrics so automated AI code reviews stay secure in CI workflows.

Guide for automating Coinranking operations through Composio’s toolkit via Rube MCP, emphasizing tool discovery, connection checks, schema compliance, and multi-execution workflows for reliable task execution.

Helps designers explore trending Coolors palettes, capture hex codes, and map selections into Tailwind configs or curated fallbacks when the browser is unavailable, ensuring contrast and role guidance for frontend themes.

Guides how to author Claude Code slash commands using Markdown prompts, YAML frontmatter, dynamic arguments, Bash execution, and best-practice organization for reusable workflows and interactive automation.

Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working. Helps inspire and improve your own ad campaigns.

Guides creation of competitor comparison or alternative landing pages for SEO and sales enablement, detailing honesty-first messaging, multiple page formats, and centralized competitor data so evaluators can choose confidently.

Automates Composio tasks through the Rube MCP interface, guiding tool discovery, connection checks, and multi-tool execution while enforcing schema compliance and session reuse for reliable workflows.

Automates Composio Search workflows through Rube MCP by discovering the latest tool schemas first, validating connections, and executing schema-compliant multi-tool calls for reliable search operations.

This tool generates comprehensive, structured Pull Request (PR) descriptions directly from Git diffs. It automatically categorizes changes (source, test, config, docs), adds critical review checklists, assesses risks (breaking changes, security), and provides executive summaries. Use this when submitting large or complex code changes to ensure reviewers have a clear, actionable, and complete overview of the modifications.

This skill details the fundamental architecture of computer use agents, following the Perception-Reasoning-Action loop. It shows how to integrate vision models with desktop control using Python, simulating screen observation, planning, and action execution (mouse/keyboard). Crucially, it mandates and demonstrates best practices for running these agents within secure, isolated, sandboxed environments (e.g., using Docker, seccomp, and resource limits) to minimize security risks and ensure reliable deployment.