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LangFuse LLM observability integration for tracing, analytics, and cost tracking
Passed 11 of 23 test cases with the skill, and 4 of 23 without it.
and 5 more in the full report
7 fixed4 passed either way12 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to set up full observability for my Python application's LLM pipeline. Could you write a complete script that configures the LangChain callback handler, attaches custom metadata and user identifiers to execution traces, and tracks latency and cost metrics?
| Checked | Without skill | With skill |
|---|---|---|
| The implementation uses the langfuse SDK for LLM observability callback integration. | Missed | Passed |
| The script initializes CallbackHandler from the langfuse library. | Missed | Passed |
The instructions your model reads when the skill loads.
test cases passed, vs 4 / 23 without
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