langfuse — Claude Code skill: what it does, how to install
Equips your coding agent to inspect traces, manage prompts, and run evaluations in Langfuse. Useful when instrumenting LLM calls or building test datasets.
npx skills add langfuse/skills --skill langfuse -a claude-codeWhat langfuse does
The skill guides your agent through Langfuse integration using current documentation and the command-line interface. Instead of relying on training memory, the assistant fetches doc pages or index files before generating code. It queries and modifies project data through langfuse-cli via npx or bunx.
It includes reference patterns for migrating prompts and calibrating model judges. Calling the API requires LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, and LANGFUSE_BASE_URL in your environment. Without these credentials, CLI commands cannot authenticate to your project.
What it helps with
- instrument application code with Langfuse tracing
- migrate hardcoded prompts into Langfuse
- create datasets to test LLM quality and regressions
- configure automated experiment evaluation gates in CI/CD
Example prompts for the agent
- Check the Langfuse docs and add tracing to my LLM pipeline
- Migrate prompts from this repository into Langfuse
- Inspect the CLI schema and show commands for datasets
How to install langfuse
1Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a claude-code2Manually: put the files in place
~/.claude/skills/langfuse/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/langfuse/
1Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a codex2Manually: put the files in place
.agents/skills/langfuse/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a cursor2Manually: put the files in place
.cursor/skills/langfuse/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install langfuse/skills langfuse2Manually: put the files in place
.github/skills/langfuse/SKILL.md1Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a gemini-cli2Platform CLI
gemini skills install https://github.com/langfuse/skills --path skills/langfuseManually: put the files in place
.devin/skills/langfuse/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Similar
Questions about langfuse
How do I install langfuse in Claude Code?
Run npx skills add langfuse/skills --skill langfuse -a claude-code in your terminal.
What does the langfuse skill do?
It provides your agent with reference workflows for Langfuse, fetches up-to-date documentation, and executes langfuse-cli commands to manage traces, prompts, and datasets.
Which credentials are required to use langfuse?
You need LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, and LANGFUSE_BASE_URL. Generate the key pair under project Settings and set them in your environment.
Source text
SKILL.md · MIT
---
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
- optimizing the cost of an LLM application or AI agent: references/cost-optimization.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:
```bash
# 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:
```bash
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:
```bash
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 on `https://langfuse.com/docs` and explore the site to find the page you need.
### 2b. Fetch Individual Pages as Markdown
Any page listed in llms.txt can be fetched as markdown by appending `.md` to its path or by using `Accept: text/markdown` in the request headers. Use this when you know which page contains the information needed. Returns clean markdown with code examples and configuration details.
```bash
curl -s "https://langfuse.com/docs/observability/overview.md"
curl -s "https://langfuse.com/docs/observability/overview" -H "Accept: text/markdown"
```
### 2c. Search Documentation
When you need to find information across all docs and github issues/discussions without knowing the specific page:
```bash
curl -s "https://langfuse.com/api/search-docs?query=<url-encoded-query>"
```
Example:
```bash
curl -s "https://langfuse.com/api/search-docs?query=How+do+I+trace+LangGraph+agents"
```
Returns a JSON response with:
- `query`: the original query
- `answer`: a JSON string containing an array of matching documents, each with:
- `url`: link to the doc page
- `title`: page title
- `source.content`: array of relevant text excerpts from the page
Search is a great fallback if you cannot find the relevant pages or need more context. Especially useful when debugging issues as all GitHub Issues and Discussions are also indexed. Responses can be large — extract only the relevant portions. Note that changelog posts may also surface here: use them only to confirm a feature exists, never to implement from — their examples may be outdated, so always implement from the docs and API/SDK reference.
### Documentation Workflow
1. Start with **llms.txt** to orient — scan for relevant page titles
2. **Fetch specific pages** when you identify the right one
3. Fall back to **search** when the topic is unclear and you want more context
## Skill Feedback
When the user expresses that something about this skill is not working as expected, gives incorrect guidance, is missing information, or could be improved — offer to submit feedback to the Langfuse skill maintainers. This includes when:
- The skill gave wrong or outdated instructions
- A workflow didn't produce the expected result
- The user wishes the skill covered something it doesn't
- The user explicitly says something like "this should work differently" or "this is wrong"
**Do NOT trigger this** for issues with Langfuse itself (the product) — only for issues with this skill's instructions and behavior.
When triggered, follow the process in [references/skill-feedback.md](references/skill-feedback.md).
© langfuse · MIT · langfuse/skills