Skill UI

This guide provides minimal working examples demonstrating the LangChain Expression Language (LCEL). It shows how to chain together diverse components—such as prompts, LLMs, and output parsers—using simple pipe syntax (e.g., .pipe() or |). Ideal for developers starting a new LangChain project, understanding core AI workflow patterns, or mastering structured data output.

This comprehensive skill implements advanced event-driven patterns for LangChain applications. It provides custom callback handlers to observe every stage of chain execution (e.g., tool start/end, LLM token generation). Furthermore, it supports real-time data transmission via Server-Sent Events (SSE) and WebSockets, and integrates webhooks for centralized event logging and comprehensive tracing.

Learn how to monitor, analyze, and optimize the expenditure of Large Language Models (LLMs) using Langfuse's advanced analytics suite. This guide covers techniques like automatic token tracking, programmatic cost reporting via the Metrics API, and implementing smart model routing to automatically select the most cost-effective model based on task complexity and input length. Essential for any developer managing AI budgets and ensuring operational efficiency.

A comprehensive guide covering production-grade patterns for using the Langfuse SDK. Learn how to implement robust LLM observability, including singleton client setup, using the `observe` wrapper for automatic span creation, and utilizing `startActiveObservation` for fine-grained trace control. Essential for maintaining high standards of observability in AI-powered applications.

This guide demonstrates the basic workflow for building an AI agent using a webhook trigger. The agent receives structured data (payload), utilizes an LLM to extract key information and generate a concise summary (summarization step), and finally executes an action by posting the summary to Slack or sending an email. Ideal for beginners learning about AI agent architecture, testing data flow, and implementing simple automated system notifications.

This guide provides advanced strategies for optimizing API usage with Linear, helping users stay within strict rate limits and minimize operational costs. It covers best practices like replacing polling with webhooks, implementing request coalescing, minimizing query complexity, and utilizing smart caching with tailored TTLs, ensuring maximum throughput efficiency.

Execute comprehensive load, stress, and soak tests to validate API performance, identify scaling bottlenecks, and establish throughput baselines. This tool simulates realistic, high-volume traffic patterns using industry-standard tools like k6 or Artillery, allowing users to define complex scenarios (e.g., e-commerce checkout flows, traffic spikes) and assert performance thresholds (p95 latency, error rates). It helps pinpoint resource limitations in databases, network layers, or compute resources, ensuring the API is ready for production scalability.

Loki Mode is a multi-agent autonomous startup system that progresses PRD to production with zero human intervention, orchestrating research-enhanced components such as OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, and agent tooling while enforcing RARV reasoning and efficiency tracking.

A comprehensive guide to minimizing API usage costs by implementing advanced integration patterns. Learn to reduce request volume through response caching with TTL, replacing expensive polling with webhook-driven sync, and utilizing smart polling with change detection filters. Also covers usage tracking and request deduplication for highly efficient system architecture.

A comprehensive guide to implementing robust, multi-layered API response caching using distributed stores like Redis and Memcached, alongside CDN integration. Learn advanced strategies such as tag-based cache invalidation, read-through/write-through patterns, TTL management, and serving stale-while-revalidate content to drastically boost application performance and scalability.

Automates designing and implementing table partitioning strategies to keep large databases performant, optimize time-series workloads, and shorten maintenance windows, with guidance on key selection, SQL scripts, and query tuning.

Structured guidance for TAM/SAM/SOM calculations, combining top-down, bottom-up, and value-theory methods for startup market analysis and fundraising planning.