ai-observability-langfuse
Pass
Audited by Gen Agent Trust Hub on Sep 20, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides documentation and examples for LLM observability using Langfuse. No malicious patterns were detected.\n- [CREDENTIALS_UNSAFE]: The skill correctly instructs users to use environment variables (
LANGFUSE_SECRET_KEY,LANGFUSE_PUBLIC_KEY) rather than hardcoding credentials. Examples use clear placeholders (e.g.,sk-lf-...).\n- [INDIRECT_PROMPT_INJECTION]: The skill facilitates the ingestion of untrusted data (LLM inputs and outputs) into the Langfuse observability platform.\n - Ingestion points: LLM call inputs and outputs are captured via
span.update()calls inexamples/tracing.mdand automated wrappers inexamples/openai-integration.md.\n - Boundary markers: Explicit boundary markers for the data stored within Langfuse traces are not defined in the provided examples.\n
- Capability inventory: The skill uses network operations to transmit captured data to the Langfuse API.\n
- Sanitization: The skill includes a pattern for PII masking in
examples/core.mdusing regex-based redaction (e.g., credit card and email patterns) before data transmission.
Audit Metadata