Community skillDraft — source instructions not independently live-testedAI & development

Langfuse for Meta Muse

Query traces and observations, manage prompts and scores.

Read and write the user's Langfuse LLM observability data: browse traces and observations, list prompts and datasets, score traces, and add dataset items. Use when the user mentions Langfuse or LLM tracing and evaluation.

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How to set it up

Paste a prompt

Draft — these instructions come from the linked source and have not been independently live-tested by OpenTools.

Before you start

  • Public+secret key pair (per-project; host declared at connect time)

Steps

  1. Paste the install prompt into a chat with Muse.
  2. Muse fetches the pinned SKILL.md and the 2 files in its manifest.
  3. Authorize your own account through Muse’s secure credential flow.
  4. Have Muse run the status check shown in the source skill.

Prompt to paste into Muse

Install this connector: https://raw.githubusercontent.com/bluman1/muse-connectors/6e31fd44a71f9f28377a1460e64cd7483c3cce22/connectors/langfuse/SKILL.md
Read its Files manifest and install exactly those files. Collect credentials through Muse's secure credential flow; never ask me for raw keys in chat. Run the skill's status check and report the result.

Account access: via the secure credential flow (`credentials.request_api_access`). The credential stores ONE combined value in the format `public_key:secret_key` (e.g. `pk-lf-...:sk-lf-...`), as issued in Langfuse under Settings > API keys. The CLI splits on the first colon into username (public key) and password (secret key) and sends them as HTTP Basic auth (`Authorization: Basic base64(pk:sk)`). Store the combined value exactly once, with exactly one colon separator.

Allowed hosts: cloud.langfuse.com, us.cloud.langfuse.com

Confirm it works: `bin/langfuse.py auth` (must return `"ok": true`)

Services it connects with

These are the services the connector uses. The publisher is identified separately.

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