langfuse — скилл для Claude Code: что делает и как установить
Помогает настроить трейсинг, оценку и промпты в Langfuse прямо из чата с агентом. Пригодится при отладке вызовов моделей и сборе датасетов.
npx skills add langfuse/skills --skill langfuse -a claude-codeЧто делает langfuse
Скилл обучает агента работать с платформой Langfuse по актуальной документации и через CLI. Перед генерацией кода помощник обращается к свежим страницам документации или к файлу индексов, вместо того чтобы полагаться на устаревшие знания. Для чтения и изменения данных в проекте агент вызывает команды langfuse-cli через npx или bunx.
Внутри собраны сценарии для переноса промптов и калибровки судей на базе языковых моделей. Для доступа к API нужны ключи LANGFUSE_PUBLIC_KEY и LANGFUSE_SECRET_KEY, а также переменная LANGFUSE_BASE_URL с адресом сервера. Без них CLI не сможет подключиться к проекту, поэтому надёжнее сразу задать их в окружении.
Какие задачи решает
- настроить трейсинг вызовов LLM в приложении
- перенести промпты из репозитория в Langfuse
- создать датасет для оценки качества ответов
- запустить проверку экспериментов в CI/CD пайплайне
Примеры запросов агенту
- Проверь документацию Langfuse и настрой трейсинг для агента
- Экспортируй текущие промпты из проекта в Langfuse
- Получи схему API через CLI и найди методы для работы с датасетами
Как установить langfuse
1Через Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a claude-code2Вручную: положить файлы
~/.claude/skills/langfuse/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/langfuse/
1Через Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a codex2Вручную: положить файлы
.agents/skills/langfuse/SKILL.mdДля всех проектов — ~/.agents/skills/
1Через Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a cursor2Вручную: положить файлы
.cursor/skills/langfuse/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install langfuse/skills langfuse2Вручную: положить файлы
.github/skills/langfuse/SKILL.md1Через Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a gemini-cli2Командой платформы
gemini skills install https://github.com/langfuse/skills --path skills/langfuseВручную: положить файлы
.devin/skills/langfuse/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Через Skills CLI (Vercel)
npx skills add langfuse/skills --skill langfuse -a opencodeClaude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
Похожие
Вопросы о langfuse
Как установить langfuse в Claude Code?
Выполните в терминале команду npx skills add langfuse/skills --skill langfuse -a claude-code.
Что делает скилл langfuse?
Он предоставляет агенту инструкции по работе с Langfuse, доступ к актуальной документации и вызовы langfuse-cli для управления трейсами, промптами и датасетами.
Какие данные нужны для подключения к Langfuse?
Потребуются переменные окружения LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY и LANGFUSE_BASE_URL. Их создают в настройках проекта в разделе API Keys и сохраняют в файле .env или переменных шелла.
Исходный текст
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