Skill UI

Guide to diagnose and fix LangChain authentication, import, rate limit, parsing, chain, agent, and memory failures, plus debugging tips and tracing resources for integrations.

Guide for instrumenting LangChain applications with LangSmith tracing, Prometheus metrics, OpenTelemetry spans, structured logging, Grafana dashboards, and alerting to monitor health and performance in staging or production.

Guides setup of Langfuse tracing for LLM pipelines, covering automatic OpenAI wrapping, manual spans, streaming responses, and parent-child trace linking for end-to-end observability.

Guides through installing the Langfuse SDK and configuring public/secret keys plus host settings so you can authenticate tracing for new LLM observability projects in Node.js or Python.

LangSmith centralizes tracing, evaluation, and monitoring for LLM applications, letting teams debug prompts, run regression tests, collect latency/cost metrics, and integrate with OpenAI, Anthropic, LangChain, or LlamaIndex to keep production systems observable.

Guides researchers through nnsight’s tracing and activation APIs so you can inspect or modify PyTorch internals locally or via NDIF-powered remote runs on 70B+ foundation models without local GPUs.

Configure OpenEvidence clinical AI integrations with Prometheus metrics, OpenTelemetry tracing, structured logging, and Grafana dashboards so you can monitor health, latency, errors, cache behavior, and alerts.

Phoenix is an open-source observability platform for tracing, evaluating, and monitoring LLM applications; use it to debug prompts, run dataset evaluations, build experiment pipelines, and watch production inference in real time.

Pyvene is a declarative framework designed for performing causal interventions on PyTorch neural networks. It allows researchers to conduct advanced experiments such as activation patching, causal tracing (ROME-style), and interchange intervention training (IIT). Use this library when you need to test causal hypotheses about model behavior, deeply interpret model components, or ensure reproducibility in advanced AI research.

TransformerLens is the standard library for performing mechanistic interpretability research on large language models. It provides clean interfaces to inspect and manipulate transformer internals—such as attention patterns, residual streams, and MLP outputs—via activation caching and HookPoints. This tool is essential for reverse-engineering model algorithms, performing causal tracing, and studying the internal circuits responsible for model behavior.

Guides hooking Phoenix AI services with OpenInference tracing, covering setup, spans, instrumentation, and production deployment for LLM workflows using Python or TypeScript.