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Monitoring
Skill

/langfuse

Interact with Langfuse and access its documentation: tracing, monitoring, creating datasets, running experiments, and evaluating AI applications. Use when needing to (1) query or modify Langfuse data, (2) look up Langfuse documentation, concepts, integration guides, a feature or

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From plugin
langfuse
2861 skill
Install
$ npx -y skills add langfuse/skills --skill langfuse --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/langfuse

Context preview

The summary Claude sees to decide when to auto-load this skill.

Interact with Langfuse and access its documentation: tracing, monitoring, creating datasets, running experiments, and evaluating AI applications. Use when needing to (1) query or modify Langfuse data, (2) look up Langfuse documentation, concepts, integration guides, a feature or

SKILL.md

langfuse.SKILL.md
name: langfuse
description: >-
  Interact with Langfuse and access its documentation: tracing, monitoring, creating datasets, running experiments, and evaluating AI applications. Use when needing to (1) query or modify Langfuse data, (2) look up Langfuse documentation, concepts, integration guides, a feature or SDK usage, or (3) do any AI engineering task (AI observability, prompt engineering/management, evaluation and evaluator management, experimentation, dataset management, evaluation-driven CI/CD, feedback collection). Invoke it for tasks in this scope even when Langfuse is not configured or explicitly mentioned.
allowed-tools:
  - WebFetch(domain:langfuse.com)
  - Bash(curl *langfuse.com/*)
  - Bash(npx langfuse-cli api __schema *)
  - Bash(npx langfuse-cli api * --help *)
  - Bash(npx langfuse-cli api * list *)
  - Bash(npx langfuse-cli api * get *)
  - Bash(bunx langfuse-cli api __schema *)
  - Bash(bunx langfuse-cli api * --help *)
  - Bash(bunx langfuse-cli api * list *)
  - Bash(bunx langfuse-cli api * get *)

Langfuse

This skill helps you use Langfuse effectively across all common workflows: instrumenting applications, migrating prompts, debugging traces, and accessing data programmatically.

Core Principles

Follow these principles for ALL Langfuse work:

1. **Documentation First**: NEVER implement based on memory. Always fetch current docs before writing code (Langfuse updates frequently) See the section below on how to access documentation. 2. **CLI for Data Access**: Use `langfuse-cli` when querying/modifying Langfuse data. See the section below on how to use the CLI. 3. **Best Practices by Use Case**: Read the relevant reference below use-case-specific guidelines before asking the user for more details or implementing. 4. **Use latest Langfuse versions**: Unless the user specified otherwise or there's a good reason, always use the latest version of Langfuse SDKs/APIs. Even if you're only creating a plan for another agent to execute, be explicit about the exact version to use. 5. **If you guide the user through UI** and are unsure about a label or location, inspect the user’s screenshots or ask to see the relevant screen. Do not assume UI labels have the exact same names as API, SDK, or CLI fields.

Use case specific references

  • instrumenting an existing function/application: references/instrumentation.md
  • creating or getting to a good (evaluation) dataset to measure quality or test for regressions in AI systems: references/create-dataset.md
  • migrating prompts from a codebase into Langfuse: references/prompt-migration.md
  • creating a prompt or changing any part of an existing prompt, including small edits and debugging/tuning: references/prompt-engineering.md
  • setting up evals when the user needs to identify gaps across signal capture, monitoring, and evaluator metrics ("I have traces, how do I set up evals?"): references/setting-up-evals.md
  • capturing user feedback signals (explicit ratings, behavioral events, conversation signals, task outcomes) as scores: references/user-feedback.md
  • further tips on using the Langfuse CLI: references/cli.md
  • preparing a Langfuse project for the v4 platform migration: references/v4-project-migration.md
  • calibrating a new or existing LLM-as-a-Judge against labeled examples, iterating on its prompt, and deploying the approved judge: references/judge-calibration.md
  • systematic error analysis when requested directly or eval setup still lacks concrete failure modes after agent-led trace inspection: references/error-analysis.md
  • setting up CI/CD experiment gates with `langfuse/experiment-action`: references/ci-cd.md
  • submitting feedback about this skill: references/skill-feedback.md

1. Langfuse API via CLI

Use the `langfuse-cli` to interact with the full Langfuse REST API from the command line. Run via npx (no install required):

Start by discovering the schema and available arguments:

# Discover all available resources
npx langfuse-cli api __schema

# List actions for a resource
npx langfuse-cli api <resource> --help

# Show args/options for a specific action
npx langfuse-cli api <resource> <action> --help

Credentials

Set environment variables before making calls:

export LANGFUSE_PUBLIC_KEY=pk-lf-...
export LANGFUSE_SECRET_KEY=sk-lf-...
export LANGFUSE_BASE_URL=https://cloud.langfuse.com # example for EU cloud. For US cloud it's us.cloud.langfuse.com, and can also be a self-hosted URL. The server must always be specified in order to access Langfuse.

If `LANGFUSE_BASE_URL` is used instead of `LANGFUSE_HOST`, run `export LANGFUSE_HOST="$LANGFUSE_BASE_URL"`. If not set, ask the user to set them in their shell or a `.env` file. Keys are found in the Langfuse project under Settings -> API Keys; the user should create a project API key pair there. If they do not have a Langfuse account yet, share that they can create one for free at `https://langfuse.com/cloud`. Do not ask them to paste keys into chat for security reasons.

Detailed CLI Reference

For common workflows, tips, and full usage patterns, see [references/cli.md](references/cli.md).

2. Langfuse Documentation

Three methods to access Langfuse docs, in order of preference. **Always prefer your application's native web fetch and search tools** (e.g., `WebFetch`, `WebSearch`, `mcp_fetch`, etc.) over `curl` when available. The URLs and patterns below work with any fetching method — the `curl` examples are just illustrative.

When working with self-hosted Langfuse, prefer the [API reference served by the deployment](https://langfuse.com/faq/all/self-hosting-api-reference) so it matches the installed version.

2a. Documentation Index (llms.txt)

Fetch the full index of all documentation pages:

curl -s https://langfuse.com/llms.txt

Returns a structured list of every doc page with titles and URLs. Use this to discover the right page for a topic, then fetch that page directly.

Alternatively, you can start

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Ships withlangfuse

Agent Skills that teach AI coding assistants (Claude Code, Cursor, etc.) how to work with Langfuse — the open-source LLM engineering platform for tracing, prompt management, and evaluation.

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MIT
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1d ago
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8mo ago
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Repo: langfuse/skills