Skill UI

Guides how to write JavaScript inside n8n Code nodes, choose execution modes, access data with $input/$node, handle Webhook payloads, and return the required [{json:…}] structure for automation tasks.

A comprehensive guide for writing and managing Python code specifically within n8n Code nodes. Learn how to perform complex data transformations, handle different execution modes (batch vs. item-by-item), and master various data access patterns (_input.all(), _input.item, _node). Note the critical limitations, such as relying only on the standard Python library and the general recommendation to use JavaScript for most n8n use cases.

Automates converting PPTX, PDF, HTML, or Google Slides inputs into a GitHub Pages repo, covering conversion, asset handling, repo creation, and returning the live URL so presentations or HTML content can be shared quickly.

A persistent, graph-based knowledge base designed to accumulate structured knowledge throughout the entire research lifecycle. It stores and connects diverse entities—including published papers, novel ideas, conducted experiments, and testable claims. Inspired by LLM Wiki patterns, it ensures knowledge compounding, meaning the wiki becomes progressively smarter with every piece of input, rather than requiring re-derivation for every query.

Analyzes and generates a self-contained, explorable HTML report detailing usage metrics for Claude Code sessions. It tracks token consumption (input/output), subagent usage, skill calls, and cache efficiency across specified time ranges. This tool helps developers visualize resource waste and pinpoint areas for workflow optimization, ensuring cost-effective and efficient AI development cycles.

This technique helps users solve highly complex movie trivia or cinematic research questions that involve multiple, intertwined constraints (such as specific awards, historical eras, directors, or plot elements). Instead of attempting to input all variables at once, it teaches the methodology of decomposing the main question into 2-3 independent, searchable dimensions, ensuring a systematic and accurate search process.

Creates safety-bounded draft structures and runs local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

This guide provides a reference architecture for automating the design-to-code workflow. It outlines the complete pipeline—from Figma design input to structured React/Vue output—detailing components like Figma API integration, code generation, and crucial post-processing steps (token mapping, normalization). Ideal for establishing scalable design systems and managing large-scale design handoffs in a professional environment.

Provides comprehensive security guidelines for integrating Anthropic Claude API. Covers essential best practices including secure API key management (rotation, isolation), defending against prompt injection attacks by separating system instructions from user input, robust input sanitization, and output validation to prevent accidental data leakage. Essential for building production-grade, secure AI applications.

A comprehensive guide for debugging complex Claude integration issues beyond basic error handling. It covers advanced patterns such as managing inconsistent outputs (temperature control), validating tool use calls, recovering from streaming connection drops, handling max_tokens truncation, and correctly processing image/vision inputs. Essential for building robust, production-grade AI applications with Anthropic's API.

This guide covers advanced usage of the Anthropic Messages API. Learn how to implement real-time streaming responses, utilize system prompts for persona control, process visual inputs (Vision), and enforce structured data output (JSON). This is essential for building sophisticated, robust, and multi-modal Claude-powered applications.

A comprehensive guide detailing security best practices for integrating and managing enterprise search with Glean. It covers critical topics such as indexing token management, validating webhook signatures, implementing input validation using Zod, and techniques for data redaction, ensuring that sensitive information is protected across the entire search lifecycle.