LLM Observer is a complete, platform-agnostic workflow for auditing, optimizing, and documenting LLM usage through any observability platform.
It guides the user through 7 structured modules:
Helicone · LangSmith · Langfuse · Braintrust · W&B Weave · Phoenix/Arize · PromptLayer
Each platform has a dedicated setup guide in references/platforms.md with copy-paste code snippets for proxy, SDK wrapper, and MCP server integration methods.
Anyone building or maintaining LLM-powered products who wants to:
Before creating this skill, I searched the skills.sh ecosystem and found zero existing skills for LLM observability, prompt auditing, or cost optimization workflows. This is the first skill in the ecosystem addressing this domain.
llm-observer/
├── SKILL.md # Main skill (7 modules)
└── references/
├── platforms.md # Setup guides for 7 platforms
└── prompt-optimization.md # Diagnostic framework + optimization techniques
The skill uses progressive disclosure: description loads on every session (~100 tokens), SKILL.md body loads on activation, references load only when the relevant module runs.
Built from real audit work on Helicone.ai, then generalized to cover the full ecosystem. Tested with Claude Sonnet in Cowork mode.
By @migetech