Skill UI

This guide provides production-ready patterns for integrating with the Grammarly API using both TypeScript and Python. It covers essential architectural aspects, including typed client implementation, robust token management (OAuth flow), and efficient text chunking for processing large documents. Use this when building scalable, reliable Grammarly-powered features into your application.

This guide provides advanced, deep debugging techniques for complex Figma REST API issues that resist standard error handling. It covers full network tracing (cURL), validating unexpected response shapes (TypeScript), measuring rate limits, and safely chunking large files. Use this when standard troubleshooting fails, or detailed evidence is required for support escalation.

A guide and toolkit for optimizing Grammarly API costs in large-scale applications. It addresses common cost overruns associated with high text volume by implementing strategies such as result caching, chunking long documents, sample-based scoring, and setting usage budget alerts. Use this when integrating Grammarly into enterprise workflows or managing billing for high-volume content analysis.

Implements a robust data pipeline for handling large documents with the Grammarly API. This utility manages text chunking to comply with API character limits (max 100k), processes text in manageable segments, and aggregates multiple scoring metrics (like correctness, clarity, and tone) into a single, comprehensive report. Ideal for large-scale content analysis and data ingestion.

This skill restructures AI-generated content to maximize cognitive accessibility, specifically optimized for users with ADHD or neurodivergence. It uses evidence-based techniques such as chunking, visual hierarchy, front-loading key points, and progressive disclosure. It offers three distinct modes (clean, flow, zen) to tailor the output structure, ensuring that complex information is highly digestible and easy to follow.

Master the complexities of building robust Retrieval Augmented Generation (RAG) pipelines using LangChain. This guide addresses common pitfalls in vector search, such as score semantics differences (L2 vs. Cosine), embedding dimension mismatches, and chunking issues for code/Markdown. Learn best practices for selecting between FAISS, Pinecone, Chroma, and PGVector, normalizing scores, and implementing hybrid keyword+vector search for maximum retrieval quality.

This skill provides comprehensive patterns for extracting and transforming Navan booking and transaction data. It addresses complex data pipeline challenges such as API pagination, date range chunking, and UUID-based deduplication. It supports both direct REST API implementation (via code examples) and configuration for managed connectors like Fivetran and Airbyte. Ideal for populating data warehouses, building robust analytics dashboards, or ensuring data quality from Navan sources.

A comprehensive playbook detailing advanced methods to bypass Web Application Firewalls (WAFs) when standard injection payloads (SQLi, XSS, RCE) are blocked. Techniques include encoding (URL, Unicode, Hex), protocol abuse (HTTP/2, chunking), parameter pollution, and path normalization tricks, allowing security professionals to test robust application defenses.