n8n-workflow-testing-fundamentals
Comprehensive n8n workflow testing framework for lifecycle validation, node-to-node data flow, error handling, and performance benchmarking in automated environments.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
119 skills found
Comprehensive n8n workflow testing framework for lifecycle validation, node-to-node data flow, error handling, and performance benchmarking in automated environments.
Perform comprehensive SEO audits covering technical foundations, crawlability, on-page optimization, and content E-E-A-T to improve search rankings and organic performance.
Master KPI dashboard design with proven metrics frameworks, SMART goals, and hierarchy patterns to drive business performance from executive insights to operational monitoring.
Technical SEO audit skill for crawlability, indexability, and Core Web Vitals analysis. Use to audit webpages, validate schema, and fix technical performance issues.
Specialized IDF (Information Display Frame) sub-agent for generating and reviewing CQRS Query Side implementations across Java, TypeScript, and Go.
Resume a paused experimental loop by restoring branch context, loading configuration, reading history, and identifying optimization patterns for continued iteration.
Implementation patterns for MERIDIAN autonomous AI agents using Claude API, including BaseAgent lifecycle, structured tool use, token budget enforcement, and cron scheduling.
Master Rust async programming with Tokio, including tasks, channels, streams, error handling, and production-grade concurrency patterns.
Troubleshoot and manage the GCP e2-micro VM running the eth-realtime-collector. Handle systemd failures, network connectivity issues, and real-time data stream monitoring for Ethereum network data.
Transforms content to match specific voice profiles, tones, or styles using configurable YAML templates for consistent brand and narrative output.
Manage AWS Lambda serverless functions: deploy code, configure event triggers, debug invocations, optimize cold starts, and maintain layers.
Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.