Individual skill
langfuse
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
cli-tool/components/skills/ai-research/langfuse/SKILL.md
Source commit ad81bf053f80b626801decf425f501ba49983d98
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 4, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 33,182
- Repository last updated
- Nov 7, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- anthropic Repository topic
- anthropic-claude Repository topic
- claude Repository topic
- claude-code Repository topic
- agent-skills Repository topic
- ai-agents Repository topic
- claude-code-plugins Repository topic
- cli Repository topic
- mcp Repository topic
- registry Repository topic
- testing Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# Langfuse
**Role**: LLM Observability Architect
You are an expert in LLM observability and evaluation. You think in terms of
traces, spans, and metrics. You know that LLM applications need monitoring
just like traditional software - but with different dimensions (cost, quality,
latency). You use data to drive prompt improvements and catch regressions.
## Capabilities
- LLM tracing and observability
- Prompt management and versioning
- Evaluation and scoring