Agent skill · research science · sickn33
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.
Why this skill is useful
Adds executable scripts for LLM tracing and observability that enhance monitoring and debugging capabilities for LLM applications.
What it needs
Requires python installed locally. Requires langfuse, openai account access. About 3k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Langfuse Instrument an existing LLM application with traceable, minimized observations and versioned evaluation inputs. Modified by AAS maintainers on 2026-09-05 to replace mixed legacy SDK examples with a current, bounded setup procedure; existing source attribution is preserved. When to Use Use when an application already needs Langfuse tracing, prompt management or evaluation, or when debugging missing/duplicated spans. Do not add an observability service merely because an LLM is present; start from the incident or product decision the data must support. Inputs and prerequisites Installed Python or JS SDK version, framework and runtime lifecycle. The explicitly authorized Langfuse project/endpoint, credentials supplied through the project’s secret mechanism, and data retention/access policy. An allowlist of observable fields and a synthetic request that contains no private content. A defined verifier: expected parent/child spans, status, timing, model/prompt version and flush behavior. Procedure 1. Inspect the dependency lock and existing instrumentation. Do not mix old langfuse.trace(), langfuse.decorators or langfuse.callback examples with the current SDK without checking its migration guide. 2. Choose one integration layer: direct SDK spans, a framework callback or OpenTelemetry instrumentation. Avoid tracing the same call twice through overlapping wrappers. 3. Configure the approved endpoint and credentials outside source. Verify export permission before running an example: instrumentation can send inputs, outputs, metadata and exceptions externally. 4. Start with explicit observations containing only synthetic or allowlisted values. Add correlation IDs only when their scope and privacy treatment are defined. 5. Execute one success and one failure request; inspect the actual exported span tree and verify no sensitive fields escaped through nested metadata or third-party instrumentation. 6. Flush in short-lived processes and test shutdown/timeouts. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/langfuse--d23504