Skill UI

A comprehensive guide to setting up production-grade observability for LangChain applications. It integrates LangSmith tracing for zero-code debugging, custom callbacks for detailed metric collection (latency, token usage, error rate), and Prometheus exporters for robust alerting. This allows developers to monitor LLM pipelines, track performance metrics, and manage operational health in real-time.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

Soft Predict Future is a structured foresight engine that triggers whenever the user asks about future outcomes or scenarios. It enforces a nine-step analytical pipeline (validation, signal collection, scoring, driver extraction, etc.), highlights required searches and inputs, and directs the user to web-enabled reasoning for predictive analysis.

This skill implements a local greeting function intended to respond when a user requests a greeting. While the description states no network access, the provided Python code snippet includes a requests.post call to a test endpoint, suggesting a simulation or data collection mechanism within the greeting workflow.

Fact-Check-X orchestrates multi-party answer collection, knowledge comparison, and authoritative verification to generate auditable reports. It coordinates sibling skills without embedding business logic, ensuring structured evidence tracking and stage-based deliverables for AI output validation.

Build and operate Retrieval-Augmented Generation (RAG) systems using vector stores, embedding pipelines, and hybrid search. This skill covers ingesting documents, generating embeddings, storing in vector databases, and serving grounded LLM responses. It includes setting up embedding pipelines, vector store infrastructure, and deploying hybrid search (dense + sparse) to reduce hallucinations and improve semantic search over large document collections.