What is the benefit of the different output modes?

Modes like 'io', 'prompts', and 'flow' allow you to suppress irrelevant data, keeping Claude's context window focused on the specific debugging task at hand.

Can I see the exact prompts sent to my models?

Absolutely. Using the '--mode prompts' flag will filter the output to show only the LLM inputs and outputs for the requested traces.

What is Langfuse Data Retrieval?

It is a Claude Code skill that enables developers to query, filter, and display trace data from Langfuse directly within their AI-powered coding workflow.

How do I filter traces by performance or quality?

You can use the --max-score and --score-name flags to perform client-side filtering on retrieved traces based on your Langfuse evaluation metrics.

Does this work with self-hosted Langfuse instances?

Yes, by configuring the LANGFUSE_HOST environment variable, you can connect the skill to either Langfuse Cloud or your own private instance.

Langfuse Data Retrieval

Name: Langfuse Data Retrieval
Author: mberto10

bymberto10

0•

Analytics & Monitoring

Fetches and analyzes Langfuse traces with customizable output modes to debug LLM workflows and monitor application performance.

Langfuse Data Retrieval is a specialized skill designed to bring AI observability directly into the Claude Code environment. It allows developers to seamlessly fetch trace data, list recent runs, and inspect specific execution steps without leaving the terminal. By offering targeted output modes like 'prompts' for quality checks and 'flow' for latency investigation, this skill helps developers pinpoint issues in complex LLM chains, filter traces by metadata or evaluation scores, and maintain a clean, readable context during debugging sessions.

Key Features

01List and filter recent runs by tags, metadata, or project identifiers

02Multiple output modes including prompt-only, latency flow, and minimal summaries

030 GitHub stars

04Retrieve specific traces by ID with focused input/output visibility

05Client-side score filtering for targeted quality and performance analysis

06Support for Langfuse Cloud and self-hosted host configurations

Use Cases

01Debugging LLM workflow failures by inspecting raw inputs, outputs, and tool calls

02Identifying performance bottlenecks using the flow mode to visualize node timing

03Auditing prompt quality by reviewing filtered batches of recent LLM interactions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mberto10/mberto-compound langfuse-data-retrieval

For use in Claude.ai and ChatGPT

Key Features

01List and filter recent runs by tags, metadata, or project identifiers

02Multiple output modes including prompt-only, latency flow, and minimal summaries

030 GitHub stars

04Retrieve specific traces by ID with focused input/output visibility

05Client-side score filtering for targeted quality and performance analysis

06Support for Langfuse Cloud and self-hosted host configurations

Use Cases

01Debugging LLM workflow failures by inspecting raw inputs, outputs, and tool calls

02Identifying performance bottlenecks using the flow mode to visualize node timing

03Auditing prompt quality by reviewing filtered batches of recent LLM interactions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mberto10/mberto-compound langfuse-data-retrieval

For use in Claude.ai and ChatGPT