What types of AI pipelines does this skill support?

It provides specific patterns and templates for Simple LLM calls, RAG (Retrieval-Augmented Generation), Agentic workflows, and Multi-model chains.

Does it support automated scoring and evaluations?

Yes, the skill includes a scoring module to help you implement latency metrics, categorical scores, and LLM-as-judge evaluations.

Does this skill work with self-hosted Langfuse?

Yes, the environment validation step allows you to configure custom hosts for self-hosted Langfuse instances.

Can this skill help debug existing Langfuse configurations?

Yes, it includes a setup validator and an anti-patterns reference to identify and fix common issues like incorrect nesting or missing metadata.

Langfuse Instrumentation Setup

Name: Langfuse Instrumentation Setup
Author: mberto10

bymberto10

0•

Analytics & Monitoring

Instruments Python applications with Langfuse tracing to provide deep observability into LLM calls, pipelines, and agentic workflows.

This skill streamlines the integration of Langfuse observability into Python projects by analyzing code structure and providing tailored instrumentation patterns. It guides users through the correct implementation of traces, spans, and generations for various architectures including RAG and autonomous agents. By following its interactive workflow, developers can avoid common anti-patterns, ensure proper context propagation, and implement sophisticated scoring modules for production-grade AI monitoring and debugging.

Key Features

01Best-practice guidance for context propagation and data flushing

020 GitHub stars

03Tailored templates for RAG, Agentic, and Multi-model pipelines

04Automated environment and API key validation

05Correct mapping of observations to traces, spans, and generations

06Integration of automated scoring and LLM-as-judge metrics

Use Cases

01Debugging nested tool calls in autonomous agent workflows

02Implementing automated quality and cost monitoring for LLM applications

03Adding production-grade tracing to a complex RAG pipeline

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mberto10/mberto-compound instrumentation-setup

For use in Claude.ai and ChatGPT

Key Features

01Best-practice guidance for context propagation and data flushing

020 GitHub stars

03Tailored templates for RAG, Agentic, and Multi-model pipelines

04Automated environment and API key validation

05Correct mapping of observations to traces, spans, and generations

06Integration of automated scoring and LLM-as-judge metrics

Use Cases

01Debugging nested tool calls in autonomous agent workflows

02Implementing automated quality and cost monitoring for LLM applications

03Adding production-grade tracing to a complex RAG pipeline

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mberto10/mberto-compound instrumentation-setup

For use in Claude.ai and ChatGPT