Skill UI

An advanced workflow orchestrator designed to perform multi-phase, deep-dive schema optimization on large datasets (e.g., BigQuery exports). It systematically analyzes schema structure, identifies unused or redundant fields, assesses potential impact, and generates a comprehensive, actionable report with recommended optimization strategies and estimated savings.

Automates Scrapingbee tasks through Composio via Rube MCP, ensuring each workflow begins with RUBE_SEARCH_TOOLS, validates connections, and executes schema-compliant tool calls with consistent sessions.

A comprehensive guide to implementing secure secrets management practices for Continuous Integration/Continuous Deployment (CI/CD) pipelines. It covers using industry-leading tools like HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, and GCP Secret Manager to securely store, retrieve, and automatically rotate credentials (API keys, database passwords, TLS certificates) without hardcoding them into source code. Includes best practices for least-privilege access and audit logging.

Automates Sendspark workflows through Composio's Rube MCP toolkit by searching tools first, ensuring an active Sendspark connection, and issuing RUBE_MULTI_EXECUTE_TOOL calls with schema-compliant arguments.

A comprehensive toolkit for the full software development lifecycle. It guides stack selection using a detailed decision engine, automatically scaffolds complex boilerplate projects across multiple popular stacks (Next.js, MERN, FastAPI, Django), and provides deep code quality analysis. Features include security vulnerability detection, complexity scoring, and dependency auditing, ensuring high-quality development from the start.

A comprehensive toolkit for ensuring code quality in modern React and Next.js applications. It automatically generates unit test stubs using Jest and React Testing Library, scaffolds Playwright E2E tests for entire application routes, and analyzes coverage reports to identify crucial testing gaps. Ideal for developers looking to establish, improve, and maintain robust, enterprise-grade test suites.

A comprehensive collection of diagnostic scripts designed for troubleshooting Sentry integration failures. This bundle collects SDK versions, runtime configuration details (e.g., DSN, environment), and performs network connectivity checks to the Sentry ingest endpoint. Use it when events are not appearing in Sentry, or when systematically diagnosing complex setup issues across Node.js and Python environments.

Master advanced error capture and context enrichment using Sentry SDKs for both TypeScript and Python. This skill teaches how to reliably capture exceptions, track non-exception events (messages), and enrich reports with structured data like user context, tags, breadcrumbs, and custom fingerprints. It is essential for dramatically improving the ability to diagnose complex production issues quickly.

This comprehensive guide details how to harden Sentry's security posture and ensure compliance with regulations like GDPR. It covers best practices for managing sensitive data, implementing PII scrubbing, and configuring `beforeSend` hooks to automatically redact sensitive information such as passwords, API keys, tokens, IP addresses, and emails from client-side and server-side events.

This specialized skill generates comprehensive, best-practice SEO metadata packages, including optimized titles, compelling descriptions, and structured URLs. It ensures strict compliance with character limits (e.g., 150-160 characters) while strategically integrating primary and secondary keywords, emotional triggers, and clear CTAs. Use this when launching new content or needing A/B test variations to maximize search visibility and click-through rates across platforms.

Orchestrates Serpapi searches through Composio's Rube MCP by discovering current tool schemas, validating the connection, and executing schema-compliant calls with session reuse and explicit memory placeholders to keep automation workflows reliable.

SGLang is a high-performance serving framework for LLMs and VLMs that uses RadixAttention prefix caching to deliver structured outputs (JSON/regex/grammar) and agentic workflows with function calls, providing up to 5× faster inference than vLLM across multi-GPU production deployments.