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.
SkillScore63Maintained
Automatic
◆AI Engineer pack
Last verified 8/29/2026
What it changes
Passed 15 of 24 test cases with the skill, and 12 of 24 without it.
Without skill50%
With skill63%
Checks it passed that the model alone missed
The code passes a label parameter to get_prompt.
The serverless handler executes flushAsync prior to returning.
The implementation compiles variables using the compile method.
and 2 more in the full report
5 fixed10 passed either way7 still failing2 broke
Pass-rate lift
+13 pp
Tokens
-5%
Agent turns
0%
Response time
-18%
Model gemini-3.6-flashMeasured 8/29/2026v115 of 24 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
We are running a LangChain multi-turn chat application in TypeScript and need to track user interactions, session histories, costs, and response latencies. Please give me an implementation example showing how to attach observability callbacks to our chains so we can monitor per-user analytics.
What was checked, without and with the skill
Checked
Without skill
With skill
The implementation uses CallbackHandler from langfuse-langchain.
Missed
Passed
The callback configuration sets a userId parameter.
Passed
Passed
case-01 · gemini-3.6-flash · 8/29/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md35 linessingle file
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.
When the user wants to plan a product launch, feature announcement, or release strategy. Also use when the user mentions 'launch,' 'Product Hunt,' 'feature release,' 'announcement,' 'go-to-market,' 'beta launch,' 'early access,' 'waitlist,' or 'product update.' This skill covers phased launches, channel strategy, and ongoing launch momentum.