langfuse-observability
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
npx skills add majiayu000/claude-skill-registry --skill langfuse-observability --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Langfuse Observability ## Overview **Langfuse** is the open-source LLM observability platform that SkillForge uses for tracing, monitoring, evaluation, and prompt management. Unlike LangSmith (deprecated), Langfuse is self-hosted, free, and designed for production LLM applications. **When to use this skill:** - Setting up LLM observability from scratch - Debugging slow or incorrect LLM responses - Tracking token usage and costs - Managing prompts in production - Evaluating LLM output quality - Migrating from LangSmith to Langfuse **SkillForge Integration:** - **Status**: Migrated from LangSmith (Dec 2025) - **Location**: `backend/app/shared/services/langfuse/` - **MCP Server**: `skillforge-langfuse` (optional) --- ## Quick Start ### Setup ```python # backend/app/shared/services/langfuse/client.py from langfuse import Langfuse from app.core.config import settings langfuse_client = Langfuse( public_key=settings.LANGFUSE_PUBLIC_KEY, secret_key=settings.LANGFUSE_SECRET_KEY, host=settings.LANGFUSE_HOST # Self-hosted or cloud ) ``` ### Basic Tracing with @observe ```python from langfuse.decorators import observe, langfuse_context @observe() # Automatic tracing async def analyze_content
- Overview
- Quick Start
- Setup
- Basic Tracing with @observe
- Session & User Tracking
- Core Features Summary
- References
- Tracing Setup
- Cost Tracking
- Prompt Management
- LLM Evaluation
- Session Tracking
- Experiments API
- Multi-Judge Evaluation
What does the langfuse-observability skill do?
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill langfuse-observability --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.